Article Search
검색
검색 팝업 닫기

Metrics

Help

  • 1. Aims and Scope

    Gut and Liver is an international journal of gastroenterology, focusing on the gastrointestinal tract, liver, biliary tree, pancreas, motility, and neurogastroenterology. Gut atnd Liver delivers up-to-date, authoritative papers on both clinical and research-based topics in gastroenterology. The Journal publishes original articles, case reports, brief communications, letters to the editor and invited review articles in the field of gastroenterology. The Journal is operated by internationally renowned editorial boards and designed to provide a global opportunity to promote academic developments in the field of gastroenterology and hepatology. +MORE

  • 2. Editorial Board

    Editor-in-Chief + MORE

    Editor-in-Chief
    Yong Chan Lee Professor of Medicine
    Director, Gastrointestinal Research Laboratory
    Veterans Affairs Medical Center, Univ. California San Francisco
    San Francisco, USA

    Deputy Editor

    Deputy Editor
    Jong Pil Im Seoul National University College of Medicine, Seoul, Korea
    Robert S. Bresalier University of Texas M. D. Anderson Cancer Center, Houston, USA
    Steven H. Itzkowitz Mount Sinai Medical Center, NY, USA
  • 3. Editorial Office
  • 4. Articles
  • 5. Instructions for Authors
  • 6. File Download (PDF version)
  • 7. Ethical Standards
  • 8. Peer Review

    All papers submitted to Gut and Liver are reviewed by the editorial team before being sent out for an external peer review to rule out papers that have low priority, insufficient originality, scientific flaws, or the absence of a message of importance to the readers of the Journal. A decision about these papers will usually be made within two or three weeks.
    The remaining articles are usually sent to two reviewers. It would be very helpful if you could suggest a selection of reviewers and include their contact details. We may not always use the reviewers you recommend, but suggesting reviewers will make our reviewer database much richer; in the end, everyone will benefit. We reserve the right to return manuscripts in which no reviewers are suggested.

    The final responsibility for the decision to accept or reject lies with the editors. In many cases, papers may be rejected despite favorable reviews because of editorial policy or a lack of space. The editor retains the right to determine publication priorities, the style of the paper, and to request, if necessary, that the material submitted be shortened for publication.

Search

Search

Year

to

Article Type

Review Article

Split Viewer

Capsule Endoscopy in Inflammatory Bowel Disease: Panenteric Capsule Endoscopy and Application of Artificial Intelligence

Offir Ukashi1,2,3 , Shelly Soffer4,5,6 , Eyal Klang2,4,7 , Rami Eliakim1,2 , Shomron Ben-Horin1,2 , Uri Kopylov1,2

1Gastroenterology Institute, Sheba Medical Center, Tel Hashomer, 2Sackler School of Medicine, Tel Aviv University, Tel Aviv, 3Department of Internal Medicine A and 4Deep Vision Lab, Sheba Medical Center, Tel Hashomer, 5Internal Medicine B, Assuta Medical Center, Ashdod, 6Faculty of Health Sciences, Ben Gurion University of the Negev, Beer Sheva, and 7Department of Diagnostic Imaging, Sheba Medical Center, Tel Hashomer, Israel

Correspondence to: Uri Kopylov
ORCID https://orcid.org/0000-0002-7156-0588
E-mail ukopylov@gmail.com

Offir Ukashi
ORCID https://orcid.org/0000-0002-8601-6550
E-mail offirukashi@gmail.com

Received: December 1, 2022; Revised: January 23, 2023; Accepted: January 30, 2023

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Gut Liver 2023;17(4):516-528. https://doi.org/10.5009/gnl220507

Published online June 12, 2023, Published date July 15, 2023

Copyright © Gut and Liver.

Video capsule endoscopy (VCE) of the small-bowel has been proven to accurately diagnose small-bowel inflammation and to predict future clinical flares among patients with Crohn’s disease (CD). In 2017, the panenteric capsule (PillCam Crohn’s system) was introduced for the first time, enabling a reliable evaluation of the whole small and large intestines. The great advantage of visualization of both parts of the gastrointestinal tract in a feasible and single procedure, holds a significant promise for patients with CD, enabling determination of the disease extent and severity, and potentially optimize disease management. In recent years, applications of machine learning, for VCE have been well studied, demonstrating impressive performance and high accuracy for the detection of various gastrointestinal pathologies, among them inflammatory bowel disease lesions. The use of artificial neural network models has been proven to accurately detect/classify and grade CD lesions, and shorten the VCE reading time, resulting in a less tedious process with a potential to minimize missed diagnosis and better predict clinical outcomes. Nevertheless, prospective, and real-world studies are essential to precisely examine artificial intelligence applications in real-life inflammatory bowel disease practice.

Keywords: Video capsule endoscopy, Crohn disease, Pan-enteric capsule, Artificial intelligence

Video capsule endoscopy (VCE) is a noninvasive modality for visualizing the mucosal surface of the small and large intestines.1 VCE of the small-bowel has been proven to precisely diagnose small-bowel inflammation and predict future clinical flares among patients with Crohn’s disease (CD).2-6 Small-bowel VCE is a prime modality for diagnosis and monitoring of patients with CD, and consequently, to prevent disease progression and complications (i.e., intestinal-surgery, clinical exacerbation) among this population.7-9

While small-bowel VCE had been widely and beneficially used among patients with CD much earlier,10 colon capsule endoscopy use in this population has been first reported in 2014.11 Thenceforth several studies have been published describing colon capsule endoscopy performance in patients with CD.8,11-14 Though initially colon capsule endoscopy has been claimed to underestimate colonic lesions compared with optical colonoscopy,12 it was considered as a promise due to the higher rates of terminal ileum lesion detection compared with the traditional procedure,8,11,13,14 covering an extended area of the gastrointestinal tract. Emboldened by this advantage, a novel panenteric capsule had been developed and introduced in 201715–the PillCam Crohn’s system (PCC; Medtronic, Yokneam, Israel), in which the capsule and its software were tailored to patients with CD,16 allowing visualization of both the small and large intestines.

Machine-learning technology is a subclass of artificial intelligence (AI), affecting many aspects of medical practice,17 including several medical fields such as radiology, dermatology, gastroenterology and ophthalmology.18 Deep learning is a subclass of machine-learning which is mainly based on artificial neural networks.17,18 In the recent years, applications of deep-learning, including convolutional neural networks (CNN), for VCE have been well studied, demonstrating an accurate performance for detection of various gastrointestinal pathologies (e.g., gastrointestinal bleeding,19-22 angioectasias,23-26 esophagus and small-bowel mucosal ulcers26-31).

In this paper we aimed to review the accumulating data regarding the use of PCC among patients with CD. We also aimed to describe the innovative and emerging use of AI among patients with CD undergoing VCE.

In 2017, a new panenteric VCE was introduced.16 The PCC is a two-headed capsule with a field of view of 344°, along with an adaptive frame rate technology which obtains up to 35 frames per second adapting to the speed of transit, allowing better tissue coverage and battery conservation.16 The PCC system platform and software have a novel assessment for inflammatory disease, specifically CD. The software divides the small bowel into three anatomic segments according to their length, as well as the colon. Three key assessment parameters are then assessed: disease distribution, lesion severity and linear extent. The most severe lesion and most common lesion in each segment are documented (Fig. 1).

Figure 1.(A) PillCam Crohn's capsule, DR3 data recorder and wireless sensors. (B) A representative graphic of a patient with active Montreal L1 and images of small bowel lesions. (C) RAPIDTM Reader Software breaks down small bowel segment based on identified anatomical landmarks. The reader classifies the most severe and most common lesion (none, mild, moderate and severe), presence or absence of stricture and extent of disease (0%–10%, 10%–30%, 30%–60%, 60%–100% of segment). Adapted from Eliakim R, et al. Endosc Int Open 2018;6:E1235-E124616 and Tai FW, et al. United European Gastroenterol J 2021;9:248-255.33

Leighton et al.15 demonstrated an improved performance of the PCC compared with ileo-colonoscopy (IC) among 66 CD patients with active disease who underwent both procedures. Either per-subject diagnostic yield rate or per-segment diagnostic yield rate for active CD lesions, was higher in the PCC compared with the IC procedure (83.3% vs 69.7% [yield difference, 13.6%; 95% confidence interval, 2.6% to 24.7%], and 40.6% vs 32.7% [yield difference, 7.9%; 95% confidence interval, 3.3% to 12.4%], respectively). Considering the substantial rate of active lesions detected only by the PCC (18%, 12/66), in which only one was limited to the proximal bowel, it was concluded that this procedure should be at least a complementary one to the IC among patients with CD. Leighton’s study used the capsule without its specific software. In 2020, a study performed by Bruining et al.,32 included 99 patients with established non-stricturing CD to assess the performance of PCC compared with IC and/or magnetic resonance enterography (MRE), in the detection of mucosal lesions in this population. The authors demonstrated comparable sensitivity rates and higher specificity rates in the overall intestinal assessment between the PCC compared to the MRE and/or IC (94% vs 100%, p=0.125 and 74% vs 22%, p=0.001, respectively). PCC had higher sensitivity and specificity in the proximal small bowel compared with MRE, higher specificity in the terminal ileum compared with MRE and/or IC, and equal performance in the colon compared to the IC. Patients’ satisfaction was superior for the capsule compared with the two other procedures. These findings emphasized the great advantage of PCC to enable a reliable disease staging of anatomic involvement among patients with CD, while undergoing a single procedure. Tai et al.33 examined the PCC performance in predicting the need of treatment intensification among 93 patients with CD (22 suspected CD, 71 established CD). PCC detected active disease in 48 out of 71 (67.6%) patients with established CD, and in three out of 22 patients (13.6%) with suspected CD. Disease extent was upstaged in 24 out of 71 (33%) patients with CD, of them, nine patients with newly upper gastrointestinal tract involvement. Overall, PCC findings led to treatment intensification in 36 out of 93 (39%) patients, and it was associated with proximal small-bowel involvement. Neither symptoms nor biochemical markers (i.e., fecal calprotectin, C-reactive protein) reliably identified active CD compared with PCC. This study demonstrated the important role of PCC in diagnosing, disease staging and optimizing disease treatment among patients with suspected or established CD. Oliva et al.,35 assessed the yield of PCC among 48 pediatric patients with quiescent CD (Crohn’s Disease Activity Index34 <10) to monitor mucosal healing and deep remission in a treat-to-target strategy. The PCC detected significant inflammation in 34 out of 48 (71%) patients involving the small-bowel or the colon (16/26 patients in clinical remission vs 18/22 patients with clinical activity). Accordingly, these findings led to treatment change in 34 out of 48 (71%) patients at baseline and in 11 out of 48 (23%) patients at 24 weeks follow-up. As a result, mucosal healing rate increased from 21% at baseline to 58% at week 52. Thus, PCC treat-to-target approach, led to higher rates of mucosal healing and deep remission among this population. In 2020, Eliakim et al.,36 evaluated the accuracy of a novel scoring system for PCC including 41 patients with CD. For each small bowel tertile, the Lewis score (LS) was extracted using the automated calculator embedded in the software. Similarly, LS was calculated for the right and left colon. The small-bowel LS was derived of the score of the tertile with the most significant disease involvement plus stricture score. The correlation of Eliakim score for PCC to fecal calprotectin was higher than between LS and fecal calprotectin (r=0.32 and r=0.54 respectively, p=0.001 for both). Table 1 summarizes PCC studies as mentioned above.

Table 1 Summary of the PillCam Crohn’s Capsule (PCC) Studies

----
Study (year)Study designPatientsComparative procedurePerformance measuresSafety
Leighton et al. (2017)15Prospective66 Patients with active CDIC83.3% and 69.7% of CD lesions in the PCC group vs IC group, respectively1: Obstructive symptoms following PCC procedure
1: GIT symptoms following PC ingestion
1: GIT symptoms following bowel preparation protocol
Eliakim et al. (2018)16Prospective feasibility study41 Patients with established or suspected IBD--No retained capsule was reported
Bruining et al. (2020)32Prospective99 Patients with non-stricturing CDIC, MREComparable sensitivity rate between PCC and either IC or MRE1: Partial bowel obstruction due to retained capsule
Higher specificity rate compared with MRE in detection of small-bowel lesions. Comparable specificity compared to IC in detection of TI and colon lesions1: Sigmoid perforation during IC
Tai et al. (2021)33Observational93 Patients (22 suspected CD, 71 established CD)-33%: Upstaging of disease extent2: Retained capsule (small-bowel and colon strictures)
9 Patients with newly upper GIT involvement
39%: Disease management change
Oliva et al. (2018)35Prospective48 Pediatric patients with quiescent CD-Disease management change in 71% and 23% at baseline and at 24 wk, respectively3: Nausea and vomiting following bowel preparation protocol
Accordingly, mucosal healing rate increased from 21% (baseline) to 58% (52 wk)
Eliakim et al. (2020)36RCT41 Patients with CDLewis score, fecal calprotectinBetter correlation of Eliakim score to fecal calprotectin than Lewis score to fecal calprotectinNo capsule retention

CD, Crohn’s disease; IC, ileo-colonoscopy; GIT, gastrointestinal tract; PC, patency capsule; IBD, inflammatory bowel disease; MRE, magnetic resonance enterography; TI, terminal ileum; RCT, randomized control trial.


Table 2 summarizes bowel preparation protocols and cleaning performance in PCC studies. All the studies’ protocols used polyethylene glycol based solution prior to the capsule ingestion (two divided doses of 1.5 to 2 L administered in the evening before and in the morning of the examination day). Food but a clear liquid diet was prohibited on the day before and the examination day. Additional laxative was used upon the capsule had been reached to the small bowel. Bowel cleansing was graded as poor, fair, good or excellent.37 Comparing bowel cleansing level between PCC and IC, there was no difference in small-bowel cleansing, while colon cleansing was significantly better in the latter procedure.15,32 Overall bowel cleansing was better for small-bowel portions than the colon portions (good/excellent rate of 80% to 90% and up to 75%, respectively). Out of 386 patients undergoing PCC, there were only two cases in which colon preparation was inadequate to preclude reading of the colon frames.33,36

Table 2 Bowel Preparation Protocols and Cleaning Performance in PillCam Crohn’s Capsule (PCC) Studies

StudyBowel preparation protocolPreparation performance37
Leighton et al. (2017)15
  • Day before (–1): clear liquid diet and 2 L PEG at the evening. Day 0: 2 L PEG at the morning

  • Optional: 10 mg Metoclopramide 1 hr after capsule ingestion.

  • Upon small-bowel detection and (again) 3 hr later: 88 mL Suprep+1 L water

  • 10 mg bisacodyl suppository 2 hr later

  • % Excellent/good cleansing level: >80% in each part of the colon and the TI for the IC procedure.

  • For the PCC procedure: TI 87.9%, colon segments 39.4%–62.9%

Eliakim et al. (2018)16
  • Day (–1): clear liquid diet and 2L PEG/Fortrans/Solucion Bohm at the evening

  • Day 0: 2 L PEG/Fortrans/Solucion Bohm at the morning

  • Optional: 10 mg Metoclopramide 1hr after capsule ingestion (if the capsule was detected in the stomach)

  • Upon small-bowel detection 88 mL Suprep +240 mL water or 1 sachet of PICO-SALAX and again 3 hr later

  • 10 mg bisacodyl suppository 2 hr later

% Excellent/good cleansing level: >97.5% in the small bowel vs up to 75 % in the colon
Bruining et al. (2020)32
  • Day (–1): clear liquid diet

  • 13–15 hr and 1–3 hr before PCC: 2 L PEG

  • 10 mg Metoclopramide or 250 mg erythromycin 1 hr after PCC ingestion

  • Upon small-bowel detection and 3 hr later: 88 mL of sodium/Magnesium sulfate diluted to 1 L of water

  • 10 mg bisacodyl suppository 2 hr later

  • % Excellent/good:

  • 79% proximal small bowel

  • 90% TI

  • 64% colon

Tai et al. (2021)33
  • Day before (–1): clear liquid diet and 2 L PEG at the evening.

  • Day 0: 2 L PEG at the morning Optional: 10 mg Metoclopramide 1 hr after capsule ingestion.

  • Upon small-bowel detection and (again) 3 hr later: 88 mL Suprep+1 L water

  • 10 mg bisacodyl suppository 2 hr later

Inadequate bowel preparation: 1/93 (1.1%)
Oliva et al. (2018)35
  • Day (–1): clear liquid diet with 50 mL up to 2 L of PEG solution

  • Day 0: 1 hr before capsule ingestion 50 mL up to 2 L of PEG solution

  • Optional: 0.25 mg/kg of domperidone (for delayed gastric transit >1 hr)

  • Once, the PCC had been detected in the small bowel

  • - first booster of sodium sulfate 30 mL was given and another booster of 15 mL, 3 hr later

  • Optional: 10 mg bisacodyl suppository 3.5 hr later

  • Small-bowel and colon cleansing level:

  • Excellent: 30%

  • Good: 58%

  • Fair: 10%

  • Poor: 2%

Eliakim et al. (2020)36
  • Day (–1): clear liquid diet and 1.5 L of PEG on the evening before the examination

  • Day 0: 1.5 L an hour prior to capsule ingestion

  • PICO-SALAX (10 mg sodium picosulfate) diluted in 75 mL of water upon small-bowel detection

  • A 10 mg of metoclopramide PO if the capsule remained in the stomach for more than an hour after ingestion (optional)

  • If the PCC was not excreted within 3 hr of ingestion, a second sachet of PICO-SALAX was administered 3 hr after the first one

  • A 10 mg Bisacodyl suppository 2 hr later if the capsule was not excreted

  • % Excellent/good:

  • Small bowel: 94.4%

  • Colon: 75%

  • % Fair and poor for the colon 22.5% and 2.5%, respectively

PEG, polyethylene glycol; TI, terminal ileum; IC, ileo-colonoscopy; PO, per os.


Of 386 patients undergoing PCC,15,16,32,33,35,36 only 12 patients experienced serious adverse events (including three cases of capsule retention [<1%]32,33) (Table 1). One case occurred despite patency confirmation by patency capsule (PC) procedure,33 one case after MRE assurance (without PC procedure),32 and a sole case of capsule retention due to a colonic stricture,33 though PC had passed uneventfully. Of them, one patient was hospitalized due to partial bowel obstruction.32 All cases were attributed to bowel strictures, and required PCC retrieval and stricture dilation by endoscopic procedure.32,33 No case required surgical treatment.

Other serious adverse events included two cases of obstructive symptoms and signs after PCC and PC procedures,15 gastrointestinal tract symptoms following bowel-cleansing preparation protocol15 and sigmoid perforation during IC procedure.32 Other non-serious adverse events including nausea, vomiting and abdominal pain occurred in less than 15% of the procedures. No serious adverse events related to the PCC were reported in pediatric CD patients (Table 1), while two episodes of nausea and one episode of vomiting were reported in three patients following adherence to bowel-preparation protocol.35

Tai et al.33 reported there were eight patients (8.6%) with incomplete colon examination (excluding a colonic stricture), five were due to loss of battery power, two due to loss of capsule signal (2.2%) and one due to inadequate bowel preparation (1.1%). Oliva et al.35 noted 17 out of 142 (~1.2%) procedures in which the capsule were not excreted before the battery expired, though seven of them reached the rectum, enabling a complete evaluation of the colon.

A single VCE procedure, captures and broadcasts an average of 12,000 images per-patient, making it tedious for a single reader reading and interpreting an entire examination. The latter may require 30 to 40 minutes on average, even for experienced VCE readers.38,39 Leenhardt et al.40 observed more than 80% of interobserver agreement rate in the identifying of ulcerative and inflammatory lesions during VCE reading, but still, there was a substantial rate of disagreement in VCE interpretation. Considering the monotone manner of VCE reading, with other technical challenges including no way to direct or focus the camera and the existence of only few frames for each lesion, its considerable rate of missed lesions (10%) is conceivable.41 AI-based VCE reading, including CNN algorithms to interpret VCE frames, has the potential to minimize the above-mentioned drawbacks, performing automated image analysis and interpretation.18 The CNN automatically extracts the features from raw input data (i.e., VCE frames), to identify distinct patterns in the dataset (e.g., small-bowel ulcers). The main dataset is randomly distributed into training, validation, and testing sets. The training set is used to fit the model and hyper-parameters, while the validation set is used to evaluate model performance. The testing set, which is sometimes an external dataset, provides an unbiased evaluation of the final model (Figs 2 and 3).42,43

Figure 2.Visualization of dataset splits performing a convolutional neural network model.

Figure 3.Convolutional neural networks architecture representation.
Conv, convolutional; ReLU, rectified linear unit.

Since 2018, data regarding the detection of ulcers and/or erosions using deep-learning application in VCE have been accumulated (Table 3 summarizes their main characteristics and findings).

Table 3 Summary of the Published Studies on Artificial Intelligence for Detection of Ulcers/Erosions in the Small Bowel

StudyStudy designAlgorithmNo. of patientsCohort detailsType of lesion (No. of normal/pathological frames)No. of frames (training/validation datasets)Performance measures
Fan et al. (2018)31RetrospectiveAlexNet CNN144NASmall-bowel ulcers and erosions (13,000/8,160)12,910/8,250Accuracy of 95.16% and 95.34% for the detection of ulcers and erosions, respectively
Aoki et al. (2019)30RetrospectiveCNN-based on SSD180Patients with various causes of erosions and ulcers*Small-bowel ulcers and erosions (10,000/5,800)5,360/10,440Accuracy of 90.8% for the detection of ulcers and erosions
Wang et al. (2019)27RetrospectiveModified1,504NASmall-bowel ulcers (19,457/17,821)32,919/4,359Accuracy of 90.1% for the detection of ulcers
RetinaNet
Ding et al. (2019)44RetrospectiveCNN-based auxiliary model6,970Patients with various small-bowel VCE findingsPathological vs normal VCE frame (NA)158,235/113,268,334Sensitivity and specificity of 99.73% and 100% for the detection of ulcers
Otani et al. (2020)26RetrospectiveModified194Patients undergoing VCE for occult/overt GIB, tumor follow-up or IBDPathological vs normal VCE frames (34,437/5,526)39,963/1,247Accuracy of 98.6%–99.3% for the detection of ulcers and erosions, respectively
RetinaNet
Klang et al. (2020)28RetrospectiveCNN49Patients with or without CD with ulcerated or normal mucosaSmall-bowel CD ulcers (10,249/7,391)14,112/3,528Accuracy of 95.4%–96.7% and 73.7%–98.2%, for per-lesion analysis and per-patient analysis, respectively
Barash et al. (2021)47RetrospectiveCNN49Patients with or without CD with ulcerated or normal mucosaGrading of small-bowel CD ulcers (10,249/7,391)1,242/248Accuracy rate of 91% comparing grade 1 to grade 3 ulcerations
Klang et al. (2021)48RetrospectiveGoogle’s EfficientNet networksNAPatients with or without CD with ulcerated or normal mucosaSmall-bowel strictures (14,266/13,626)NAAccuracy rate of 93.5% and 78.9% for the detection of strictures, and for the classification of ulcerated versus non-ulcerated strictures, respectively
Hwang et al. (2021)45RetrospectiveVGGNetNAPatients undergoing small-bowel VCEClassification of hemorrhagic and ulcerative small-bowel lesions (8,578/4,738)7,556/5,760Accuracy rate of 96.62%–96.83%
Mascarenhas Saraiva et al. (2021)50RetrospectiveXception4,319Patients undergoing VCE with normal mucosa, or small-bowel pathology (polyps, ulcers, vascular lesions, etc.)Classification of higher risk small-bowel lesions for bleeding (18,010/35,545)42,844/10,711AUC for ulcer detection of 0.99 for P1 ulcers, and 1.00 for P2 ulcers
Afonso et al. (2022)49RetrospectiveCNNNAPatients with small-bowel ulcers, erosions, or normal small-bowel VCEClassification of potential risk for bleeding of small-bowel ulcers and erosions (1,897/4,233)4,904/1,226Accuracy rate of 95.6% for the detection and classification of erosions and/or ulcers (with any bleeding potential)
Majtner et al. (2021)52ProspectiveResNet-5038Patients with suspected or known CD undergoing PCCDetection of small-bowel or colon CD lesions and classification of the severity of these lesions (4,996/2,748)5,419/767Accuracy rate of 98.4%–98.6% to detect small-bowel and or colon inflammatory/ulcerative lesions
Ferreira et al. (2022)53RetrospectiveXceptionNAPatients undergoing PCCDetection of small-bowel or colon ulcers or erosions (19,190/5,300)19,740/4,935Accuracy rate of 98.8% for the detection of ulcers and erosions
Kratter et al. (2022)46RetrospectiveEfficientNetNAPatients with and without CD undergoing small-bowel VCE or PCCDetection of small-bowel or colon ulcers or erosions (15,684/17,416)NAAccuracy rate of 97.4% for the detection and the grading of mucosal ulcers in different VCE types

CNN, convolutional neural network; NA, not applicable; SSD, single-shot detector; VCE, video capsule endoscopy; GIB, gastrointestinal bleeding; IBD, inflammatory bowel disease; CD, Crohn’s disease; AUC, area under the curve; PCC, PillCam Crohn’s capsule.

*Patients who used nonsteroidal anti-inflammatory drugs (26%), patients with IBD (11%), small-bowel malignancy (7%), anastomotic ulcer (6%), ischemic enteritis (2%), Meckel diverticulum (2%), radiation enteritis (1%), miscellaneous (3%), and unknown cause (45%); The dataset contained frames of various small-bowel lesions including ulcers, erosions, vascular lesions, tumors, polyps, protruding lesion, vascular lesion, bleeding, parasites, and diverticulum and normal small-bowel VCE frames; Based on Saurin’s classification.51



Fan et al.31 presented a novel computer-aided method to detect ulcers and erosions in the small-bowel with a high accuracy rate (>95%). This model demonstrated higher sensitivity rate in the detection of ulcers compared with erosions, probably due to more distinctive features of the former compared with the latter. Though excellent performance has been achieved, there were about 5% of false positive rate. Aoki et al.,30 trained a CNN system, to detect small-bowel ulcerations and erosions from a pool of frames, originated from various small-bowel pathologies (nonsteroidal anti-inflammatory drugs, inflammatory bowel disease, malignancy, etc.). Sensitivity rates were almost comparable either in nonsteroidal anti-inflammatory drugs or CD originated frames (~90%). Despite the high-speed review time by the model (44.8 images per second), it had still identified three pathological frames, which were missed by the conventional readers. Hence, emphasizing its great advantage in the detection of fine features in the frame. On the other hand, high degree of obscuration due to bubbles, debris, and bile led to 11.8% of false negative. Wang et al.27 used a second glance detection framework to detect small-bowel ulcers. Their model both classified images and also provided bounding box for lesion localization. In comparison to previously studied frameworks (RetinaNet, Faster-RCNN), using the second glance improved small ulcer detection by 10%. Still, ulcer size had a prime effect on the detection rate (92% vs 85% for ulcer size >1% and <1% of the whole image, respectively).

Two previously published studies have focused on a diagnosis of multiple types of lesions (i.e., ulcers, vascular lesions, tumors, etc.) rather than a single one.26,44 In a multicenter study, Ding et al.44 presented a CNN-based model auxiliary with increased detection rate by 20% compared with conventional reading, either per-lesion or per-patient analysis. Interestingly, though CNN-based model was significantly more sensitive in the detection of small-bowel ulcers, sensitivity has been improved only by 1% to 2% compared with conventional reading. Otani et al.,26 used a modified RetinaNet CNN, demonstrating an accuracy rate of ~99% in the detection of small-bowel ulcers and erosions as well as for vascular lesions. Notably, the modified RetinaNet CNN model had a higher area under the curve (AUC) value compared with the Single-Shot MultiBox Detector based AI system, which had predominantly been used in previously published studies. The latter’s performance has been proven among external validation cohort from 40 patients, though with a more modest AUC value (0.996 vs 0.928). Ding et al.44 and Otani et al.26 reported four and three cases of missed pathological diagnosis, respectively.

Klang et al.,28 retrospectively collected VCE frames from 49 patients with and without CD, to evaluate CNN performance in the detection of small-bowel ulcers. Like similar studies, the authors noted an impressive accuracy rate for the detection of ulcers using the trained dataset. However, the model’s accuracy rate on unseen patients ranged from 73.7% to 98.2%, probably reflecting real-world practice. The average duration for detecting a complete film was 204.7±93.9 seconds.

Hwang et al.45 trained CNN model in two different ways: a combined model (hemorrhagic and ulcerative lesions trained separately) and a binary model (all abnormal images trained without discrimination). Both the combined and binary models acquired high accuracy for lesion detection, and the difference between the two models was not significant (96.83% vs 96.62%, p=0.122). However, there were higher sensitivity and negative predictive value rates of the combined model compared with the binary one, leading to lower rates of missed diagnosis (23 cases vs 47 cases).

Recently, Kratter et al.46 developed a combined model for two different capsules (PillCam Crohn and PillCam SB3, Medtronic), with excellent performance in detection of intestinal ulcers (accuracy rate of 97.4%), providing an essential tool in real-life practice of patients with CD in which several types of VCE may be used.

As part of inflammatory lesion detection (i.e., ulcer and erosion), several studies have been focused on classifying lesions based on its distinct parameters, to better predict disease course and personalize disease management.

In 2021, Barash et al.47 demonstrated a novel use of ordinal CNN model for ulcer severity grading among patients with CD. Severity grading of CD ulcers was based on the PillCam CD classification (grade 1-3 from mild to severe) (Fig. 4). The best performance was in distinguishing between grade 1 to grade 3 ulcerations, achieving an accuracy rate of 91%. In differentiating between grade 2 to either grade 1 or grade 3, the performance was less impressive (accuracy of 65% and 79%, respectively), consistent with the performance of the conventional reading method (distinction of severity category, involving grade-2 ulcer achieved up to 40% of accuracy rate). Kratter et al.,46 demonstrated similar results in classification ulcerations to grade 1 and grade 3, with AUC of 0.99.

Figure 4.Severity grading of small-bowel ulcers (A: mild, B: moderate, C: severe) based on the PillCam Crohn's disease classification. Adapted from Barash Y, et al. Gastrointest Endosc 2021;93:187-192, with permission from Elsevier.47

Klang et al.48 evaluated the ability of a neural network model in identifying intestinal strictures for the first time. For classifying stricture versus non-stricture lesions, the network exhibited an average accuracy of 93.5%. The ulcerated versus non-ulcerated strictures classification network resulted in an accuracy rate of 78.9% (Fig. 5).

Figure 5.Class activation maps (heatmaps) of an ulcer image. Heatmaps enabled a visual presentation of image regions which led to lesion classification. Adapted from Klang E, et al. J Crohns Colitis 2021;15:749-756, with permission from Oxford University Press.48

Two studies from the same group demonstrated a novel CNN model to identify and classify small-bowel ulcers (among other enteric lesions) in whom having high risk for bleeding49,50 based on Saurin classification.51 According to Saurin classification, the hemorrhagic potential of ulcers was depended on their size: small ulcers were regarded as P1 lesions, while large ulcerations (>20 mm) were regarded as P2 lesions. Mascarenhas Saraiva et al.50 showed that among a wide range of enteric lesions, mucosal ulcers were identified with a sensitivity of 81% for P1 lesions and 94% for P2 lesions, presenting impressive values of AUC (0.99 and 1.00, respectively). A recent study by Afonso et al.,49 focused on risk potential assessment of small-bowel ulcers and erosions, achieving an accuracy rate of 95.6% in the detection and classification of erosions and ulcers, with any bleeding potential.

Two studies were conducted to evaluate deep learning performance among patients with CD undergoing PCC.52,53 Majtner et al.52 used two splitting methods: random one and per-patient one (in which each patient’s frame was used only for training, validation, or testing). Only four of 558 images of the colon were misclassified as the small-bowel, and only seven of 1,000 images of the small-bowel were misclassified as the colon. The accuracy rates for the per-patient split and the random-split were 98.4% and 98.6%, either for small-bowel or colon lesions. Ferreira et al.,53 demonstrated an impressive performance in the detection of ulcers and erosions in patients undergoing PCC (accuracy rate, 98.8%; negative predictive value, 99.5%), as well. Considering the average rate of 68 frames per second, it was estimated that only 12 minutes would require for a full PCC video revision.

Though AI performance in patients undergoing VCE is impressive, there are still several challenges to be address in future studies. First, all but a single study were retrospective,54 limiting the ability to explore performance in a real-life practice. Second, as some of the recent published studies focused on the classification of distinct parameters in CD lesions, it is of prime importance to further discriminate lesion features to improve prognosis predication, and accordingly personalize disease management. Third, as per-patient rather than per-lesion analysis better reflects a real-life practice, further adjustment and fine-tuning of CNN models are needed to cope with indistinguishable features, among frames from a single patient, to improve the accuracy rate of lesion classification. Fourth, comparative researches and accuracy-thresholds standardization should be addressed as well as demonstration of clinical correlation before it will be implemented in real-life practice.55 Fifth, though capsule readers are generally eager toward AI-based VCE reading and interpretation, a substantial part of them are frightened of its implementation in real-life practice.54 Moreover, almost half of capsule readers are aware of using AI application in medical fields.54 Thus, a learning program training addressing it, is of prime importance among this population.54,55 Sixth, assessment of its cost-effectiveness should be performed prior to AI implementation to real-life practice.54 Finally, several technical adjustments might improve CNN model performance: (1) the current datasets composed of selected still frames, rather than video films,55 with inherited risk of selection bias; (2) external validation datasets are mostly absent in the current studies,54 limiting generalization of the studies’ findings; (3) stringent bowel-preparation protocols should be implemented to better cope with the low (but still exists) missed diagnosis rate using AI in VCE; (4) most of the studies have been dealt with a single type of VCE, limiting the use of the examined models in other VCE types.55 Further development of a single and universal model for all VCE types, will probably prompt the incorporation of AI-based VCE reading in real-life clinical practice.

VCE has a crucial role in the management of patients with CD, to enable a reliable evaluation and monitoring of patients with active, as well as quiescent disease. The introduction of the colon capsule and subsequently the PCC allow a visualization of the entire small and large bowel during a single procedure, to facilitate disease management in those patients. The highest yield in determining disease extent and severity in a feasible and single procedure may improve patients’ adherence, mainly in patients with long-standing disease. Furthermore, the PCC has a prime benefit to precisely stage disease severity and extent, leading to therapy optimization and better clinical outcomes. The superiority of PCC in the detection of small-bowel lesions compared with MRE and in the same single procedure to identify colon involvement is promised in this field. As mentioned, severe adverse events are rare, mostly preventable by PC ingestion before VCE procedure.

Finally, the recent developments of machine learning applications in the detection and the grading of small and large bowel lesions (i.e., ulcer and its severity, erosion, stricture, and assessment of bleeding potential) have led to excellent performance and high accuracy rates as detailed above. Using the CNN models may shorten VCE reading time (up to 95%55), resulting in a less tedious process with a potential to minimize missed diagnosis and false positive rates. However, the published literature in this field, a part of a single study was retrospective, which limits the ability to assess it in real-life practice, as well as the lacking cost-effectiveness evaluation. Also, to the best of our knowledge, no study has been conducted to assess AI-based VCE reading to predict future clinical outcomes in patients with CD. Considering the efficient and rapid process of AI-based VCE reading, developing of prediction-model using CNN architecture may substantially improve disease management of patients with CD, to afford treatment tailoring in this population.

S.B.H. has received advisory board and/or consulting fees from Abbvie, Takeda, Janssen, Celltrion, Pfizer, GSK, Ferring, Novartis, Roche, Gilead, NeoPharm, Predicta Med, Galmed, Medial Earlysign, BMS and Eli Lilly, holds stocks/options in Predicta Med, Evinature & Galmed, and received research support from Abbvie, Takeda, Janssen, Celltrion, Pfizer, & Galmed. U.K. received speaker and consulting fees from Abbvie, BMS, Celltrion, Janssen, Medtronic, Pfizer and Takeda, research support from Medtronic, Takeda and Janssen. R.E. received consultant and speaker fees from Janssen, Abbvie, Takeda and Medtronic. The remaining authors declare that they have no conflicts of interest.

  1. Iddan G, Meron G, Glukhovsky A, Swain P. Wireless capsule endoscopy. Nature 2000;405:417.
    Pubmed CrossRef
  2. Ben-Horin S, Lahat A, Amitai MM, et al. Assessment of small bowel mucosal healing by video capsule endoscopy for the prediction of short-term and long-term risk of Crohn's disease flare: a prospective cohort study. Lancet Gastroenterol Hepatol 2019;4:519-528.
    Pubmed CrossRef
  3. Dionisio PM, Gurudu SR, Leighton JA, et al. Capsule endoscopy has a significantly higher diagnostic yield in patients with suspected and established small-bowel Crohn's disease: a meta-analysis. Am J Gastroenterol 2010;105:1240-1248.
    Pubmed CrossRef
  4. Jensen MD, Nathan T, Rafaelsen SR, Kjeldsen J. Diagnostic accuracy of capsule endoscopy for small bowel Crohn's disease is superior to that of MR enterography or CT enterography. Clin Gastroenterol Hepatol 2011;9:124-129.
    Pubmed CrossRef
  5. Flamant M, Trang C, Maillard O, et al. The prevalence and outcome of jejunal lesions visualized by small bowel capsule endoscopy in Crohn's disease. Inflamm Bowel Dis 2013;19:1390-1396.
    Pubmed CrossRef
  6. Pennazio M, Spada C, Eliakim R, et al. Small-bowel capsule endoscopy and device-assisted enteroscopy for diagnosis and treatment of small-bowel disorders: European Society of Gastrointestinal Endoscopy (ESGE) Clinical Guideline. Endoscopy 2015;47:352-376.
    Pubmed CrossRef
  7. Turner D, Ricciuto A, Lewis A, et al. STRIDE-II: an update on the Selecting Therapeutic Targets in Inflammatory Bowel Disease (STRIDE) Initiative of the International Organization for the Study of IBD (IOIBD): determining therapeutic goals for treat-to-target strategies in IBD. Gastroenterology 2021;160:1570-1583.
    Pubmed CrossRef
  8. Yamada K, Nakamura M, Yamamura T, et al. Diagnostic yield of colon capsule endoscopy for Crohn's disease lesions in the whole gastrointestinal tract. BMC Gastroenterol 2021;21:75.
    Pubmed KoreaMed CrossRef
  9. Tang N, Chen H, Chen R, Tang W, Zhang H. Combination of serological biomarkers and clinical features to predict mucosal healing in Crohn's disease: a multicenter cohort study. BMC Gastroenterol 2022;22:229.
    Pubmed KoreaMed CrossRef
  10. Triester SL, Leighton JA, Leontiadis GI, et al. A meta-analysis of the yield of capsule endoscopy compared to other diagnostic modalities in patients with non-stricturing small bowel Crohn's disease. Am J Gastroenterol 2006;101:954-964.
    Pubmed CrossRef
  11. Negreanu L, Smarandache G, Mateescu RB. Role of capsule endoscopy Pillcam COLON 2 in patients with known or suspected Crohn's disease who refused colonoscopy or underwent incomplete colonoscopic exam: a case series. Tech Coloproctol 2014;18:277-283.
    Pubmed CrossRef
  12. D'Haens G, Löwenberg M, Samaan MA, et al. Safety and feasibility of using the second-generation PillCam colon capsule to assess active colonic Crohn's disease. Clin Gastroenterol Hepatol 2015;13:1480-1486.
    Pubmed CrossRef
  13. Boal Carvalho P, Rosa B, Dias de Castro F, Moreira MJ, Cotter J. PillCam COLON 2 in Crohn's disease: a new concept of pan-enteric mucosal healing assessment. World J Gastroenterol 2015;21:7233-7241.
    Pubmed KoreaMed CrossRef
  14. Hausmann J, Schmelz R, Walldorf J, Filmann N, Zeuzem S, Albert JG. Pan-intestinal capsule endoscopy in patients with postoperative Crohn's disease: a pilot study. Scand J Gastroenterol 2017;52:840-845.
    Pubmed CrossRef
  15. Leighton JA, Helper DJ, Gralnek IM, et al. Comparing diagnostic yield of a novel pan-enteric video capsule endoscope with ileocolonoscopy in patients with active Crohn's disease: a feasibility study. Gastrointest Endosc 2017;85:196-205.
    Pubmed CrossRef
  16. Eliakim R, Spada C, Lapidus A, et al. Evaluation of a new pan-enteric video capsule endoscopy system in patients with suspected or established inflammatory bowel disease: feasibility study. Endosc Int Open 2018;6:E1235-E1246.
    Pubmed KoreaMed CrossRef
  17. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521:436-444.
    Pubmed CrossRef
  18. Soffer S, Klang E, Shimon O, et al. Deep learning for wireless capsule endoscopy: a systematic review and meta-analysis. Gastrointest Endosc 2020;92:831-839.
    Pubmed CrossRef
  19. Xiao J, Meng MQ. A deep convolutional neural network for bleeding detection in Wireless Capsule Endoscopy images. Annu Int Conf IEEE Eng Med Biol Soc 2016;2016:639-642.
    Pubmed CrossRef
  20. Xiao J, Meng MQ. Gastrointestinal bleeding detection in wireless capsule endoscopy images using handcrafted and CNN features. Annu Int Conf IEEE Eng Med Biol Soc 2017;2017:3154-3157.
    Pubmed CrossRef
  21. Aoki T, Yamada A, Kato Y, et al. Automatic detection of blood content in capsule endoscopy images based on a deep convolutional neural network. J Gastroenterol Hepatol 2020;35:1196-1200.
    Pubmed CrossRef
  22. Xing X, Jia X, Meng MQ. Bleeding detection in wireless capsule endoscopy image video using superpixel-color histogram and a subspace KNN classifier. Annu Int Conf IEEE Eng Med Biol Soc 2018;2018:1-4.
    Pubmed CrossRef
  23. Noya F, Alvarez-Gonzalez MA, Benitez R. Automated angiodysplasia detection from wireless capsule endoscopy. Annu Int Conf IEEE Eng Med Biol Soc 2017;2017:3158-3161.
    Pubmed CrossRef
  24. Tsuboi A, Oka S, Aoyama K, et al. Artificial intelligence using a convolutional neural network for automatic detection of small-bowel angioectasia in capsule endoscopy images. Dig Endosc 2020;32:382-390.
    Pubmed CrossRef
  25. Leenhardt R, Vasseur P, Li C, et al. A neural network algorithm for detection of GI angiectasia during small-bowel capsule endoscopy. Gastrointest Endosc 2019;89:189-194.
    Pubmed CrossRef
  26. Otani K, Nakada A, Kurose Y, et al. Automatic detection of different types of small-bowel lesions on capsule endoscopy images using a newly developed deep convolutional neural network. Endoscopy 2020;52:786-791.
    Pubmed CrossRef
  27. Wang S, Xing Y, Zhang L, Gao H, Zhang H. A systematic evaluation and optimization of automatic detection of ulcers in wireless capsule endoscopy on a large dataset using deep convolutional neural networks. Phys Med Biol 2019;64:235014.
    Pubmed CrossRef
  28. Klang E, Barash Y, Margalit RY, et al. Deep learning algorithms for automated detection of Crohn's disease ulcers by video capsule endoscopy. Gastrointest Endosc 2020;91:606-613.
    Pubmed CrossRef
  29. Alaskar H, Hussain A, Al-Aseem N, Liatsis P, Al-Jumeily D. Application of convolutional neural networks for automated ulcer detection in wireless capsule endoscopy images. Sensors (Basel) 2019;19:1265.
    Pubmed KoreaMed CrossRef
  30. Aoki T, Yamada A, Aoyama K, et al. Automatic detection of erosions and ulcerations in wireless capsule endoscopy images based on a deep convolutional neural network. Gastrointest Endosc 2019;89:357-363.
    Pubmed CrossRef
  31. Fan S, Xu L, Fan Y, Wei K, Li L. Computer-aided detection of small intestinal ulcer and erosion in wireless capsule endoscopy images. Phys Med Biol 2018;63:165001.
    Pubmed CrossRef
  32. Bruining DH, Oliva S, Fleisher MR, Fischer M, Fletcher JG; BLINK study group. Panenteric capsule endoscopy versus ileocolonoscopy plus magnetic resonance enterography in Crohn's disease: a multicentre, prospective study. BMJ Open Gastroenterol 2020;7:e000365.
    Pubmed KoreaMed CrossRef
  33. Tai FW, Ellul P, Elosua A, et al. Panenteric capsule endoscopy identifies proximal small bowel disease guiding upstaging and treatment intensification in Crohn's disease: a European multicentre observational cohort study. United European Gastroenterol J 2021;9:248-255.
    Pubmed KoreaMed CrossRef
  34. Best WR, Becktel JM, Singleton JW, Kern F Jr. Development of a Crohn's disease activity index. National Cooperative Crohn's Disease Study. Gastroenterology 1976;70:439-444.
    Pubmed CrossRef
  35. Oliva S, Aloi M, Viola F, et al. A treat to target strategy using panenteric capsule endoscopy in pediatric patients with Crohn's disease. Clin Gastroenterol Hepatol 2019;17:2060-2067.
    Pubmed CrossRef
  36. Eliakim R, Yablecovitch D, Lahat A, et al. A novel PillCam Crohn's capsule score (Eliakim score) for quantification of mucosal inflammation in Crohn's disease. United European Gastroenterol J 2020;8:544-551.
    Pubmed KoreaMed CrossRef
  37. Leighton JA, Rex DK. A grading scale to evaluate colon cleansing for the PillCam COLON capsule: a reliability study. Endoscopy 2011;43:123-127.
    Pubmed CrossRef
  38. Koulaouzidis A, Iakovidis DK, Karargyris A, Plevris JN. Optimizing lesion detection in small-bowel capsule endoscopy: from present problems to future solutions. Expert Rev Gastroenterol Hepatol 2015;9:217-235.
    Pubmed CrossRef
  39. Mishkin DS, Chuttani R, Croffie J, et al. ASGE Technology Status Evaluation Report: wireless capsule endoscopy. Gastrointest Endosc 2006;63:539-545.
    Pubmed CrossRef
  40. Leenhardt R, Buisson A, Bourreille A, et al. Nomenclature and semantic descriptions of ulcerative and inflammatory lesions seen in Crohn's disease in small bowel capsule endoscopy: an international Delphi consensus statement. United European Gastroenterol J 2020;8:99-107.
    Pubmed KoreaMed CrossRef
  41. Rondonotti E, Pennazio M, Toth E, Koulaouzidis A. How to read small bowel capsule endoscopy: a practical guide for everyday use. Endosc Int Open 2020;8:E1220-E1224.
    Pubmed KoreaMed CrossRef
  42. Rai HM, Chatterjee K. Detection of brain abnormality by a novel Lu-Net deep neural CNN model from MR images. Mach Learn Appl 2020;2:100004.
    CrossRef
  43. Islam MM, Karray F, Alhajj R, Zeng J. A review on deep learning techniques for the diagnosis of novel coronavirus (COVID-19). IEEE Access 2021;9:30551-30572.
    Pubmed KoreaMed CrossRef
  44. Ding Z, Shi H, Zhang H, et al. Gastroenterologist-level identification of small-bowel diseases and normal variants by capsule endoscopy using a deep-learning model. Gastroenterology 2019;157:1044-1054.
    Pubmed CrossRef
  45. Hwang Y, Lee HH, Park C, et al. Improved classification and localization approach to small bowel capsule endoscopy using convolutional neural network. Dig Endosc 2021;33:598-607.
    Pubmed CrossRef
  46. Kratter T, Shapira N, Lev Y, et al. Deep learning multi-domain model provides accurate detection and grading of mucosal ulcers in different capsule endoscopy types. Diagnostics (Basel) 2022;12:2490.
    Pubmed KoreaMed CrossRef
  47. Barash Y, Azaria L, Soffer S, et al. Ulcer severity grading in video capsule images of patients with Crohn's disease: an ordinal neural network solution. Gastrointest Endosc 2021;93:187-192.
    Pubmed CrossRef
  48. Klang E, Grinman A, Soffer S, et al. Automated detection of Crohn's disease intestinal strictures on capsule endoscopy images using deep neural networks. J Crohns Colitis 2021;15:749-756.
    Pubmed CrossRef
  49. Afonso J, Saraiva MM, Ferreira JP, et al. Automated detection of ulcers and erosions in capsule endoscopy images using a convolutional neural network. Med Biol Eng Comput 2022;60:719-725.
    Pubmed CrossRef
  50. Mascarenhas Saraiva MJ, Afonso J, Ribeiro T, et al. Deep learning and capsule endoscopy: automatic identification and differentiation of small bowel lesions with distinct haemorrhagic potential using a convolutional neural network. BMJ Open Gastroenterol 2021;8:e000753.
    Pubmed KoreaMed CrossRef
  51. Saurin JC, Delvaux M, Gaudin JL, et al. Diagnostic value of endoscopic capsule in patients with obscure digestive bleeding: blinded comparison with video push-enteroscopy. Endoscopy 2003;35:576-584.
    Pubmed CrossRef
  52. Majtner T, Brodersen JB, Herp J, Kjeldsen J, Halling ML, Jensen MD. A deep learning framework for autonomous detection and classification of Crohn's disease lesions in the small bowel and colon with capsule endoscopy. Endosc Int Open 2021;9:E1361-E1370.
    Pubmed KoreaMed CrossRef
  53. Ferreira JP, de Mascarenhas Saraiva MJ, Afonso JPL, et al. Identification of ulcers and erosions by the novel PillcamTM Crohn's capsule using a convolutional neural network: a multicentre pilot study. J Crohns Colitis 2022;16:169-172.
    Pubmed CrossRef
  54. Leenhardt R, Fernandez-Urien Sainz I, Rondonotti E, et al. PEACE: perception and expectations toward artificial intelligence in capsule endoscopy. J Clin Med 2021;10:5708.
    Pubmed KoreaMed CrossRef
  55. Leenhardt R, Koulaouzidis A, Histace A, et al. Key research questions for implementation of artificial intelligence in capsule endoscopy. Therap Adv Gastroenterol 2022;15:17562848221132683.
    Pubmed KoreaMed CrossRef

Article

Review Article

Gut and Liver 2023; 17(4): 516-528

Published online July 15, 2023 https://doi.org/10.5009/gnl220507

Copyright © Gut and Liver.

Capsule Endoscopy in Inflammatory Bowel Disease: Panenteric Capsule Endoscopy and Application of Artificial Intelligence

Offir Ukashi1,2,3 , Shelly Soffer4,5,6 , Eyal Klang2,4,7 , Rami Eliakim1,2 , Shomron Ben-Horin1,2 , Uri Kopylov1,2

1Gastroenterology Institute, Sheba Medical Center, Tel Hashomer, 2Sackler School of Medicine, Tel Aviv University, Tel Aviv, 3Department of Internal Medicine A and 4Deep Vision Lab, Sheba Medical Center, Tel Hashomer, 5Internal Medicine B, Assuta Medical Center, Ashdod, 6Faculty of Health Sciences, Ben Gurion University of the Negev, Beer Sheva, and 7Department of Diagnostic Imaging, Sheba Medical Center, Tel Hashomer, Israel

Correspondence to:Uri Kopylov
ORCID https://orcid.org/0000-0002-7156-0588
E-mail ukopylov@gmail.com

Offir Ukashi
ORCID https://orcid.org/0000-0002-8601-6550
E-mail offirukashi@gmail.com

Received: December 1, 2022; Revised: January 23, 2023; Accepted: January 30, 2023

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Video capsule endoscopy (VCE) of the small-bowel has been proven to accurately diagnose small-bowel inflammation and to predict future clinical flares among patients with Crohn’s disease (CD). In 2017, the panenteric capsule (PillCam Crohn’s system) was introduced for the first time, enabling a reliable evaluation of the whole small and large intestines. The great advantage of visualization of both parts of the gastrointestinal tract in a feasible and single procedure, holds a significant promise for patients with CD, enabling determination of the disease extent and severity, and potentially optimize disease management. In recent years, applications of machine learning, for VCE have been well studied, demonstrating impressive performance and high accuracy for the detection of various gastrointestinal pathologies, among them inflammatory bowel disease lesions. The use of artificial neural network models has been proven to accurately detect/classify and grade CD lesions, and shorten the VCE reading time, resulting in a less tedious process with a potential to minimize missed diagnosis and better predict clinical outcomes. Nevertheless, prospective, and real-world studies are essential to precisely examine artificial intelligence applications in real-life inflammatory bowel disease practice.

Keywords: Video capsule endoscopy, Crohn disease, Pan-enteric capsule, Artificial intelligence

INTRODUCTION

Video capsule endoscopy (VCE) is a noninvasive modality for visualizing the mucosal surface of the small and large intestines.1 VCE of the small-bowel has been proven to precisely diagnose small-bowel inflammation and predict future clinical flares among patients with Crohn’s disease (CD).2-6 Small-bowel VCE is a prime modality for diagnosis and monitoring of patients with CD, and consequently, to prevent disease progression and complications (i.e., intestinal-surgery, clinical exacerbation) among this population.7-9

While small-bowel VCE had been widely and beneficially used among patients with CD much earlier,10 colon capsule endoscopy use in this population has been first reported in 2014.11 Thenceforth several studies have been published describing colon capsule endoscopy performance in patients with CD.8,11-14 Though initially colon capsule endoscopy has been claimed to underestimate colonic lesions compared with optical colonoscopy,12 it was considered as a promise due to the higher rates of terminal ileum lesion detection compared with the traditional procedure,8,11,13,14 covering an extended area of the gastrointestinal tract. Emboldened by this advantage, a novel panenteric capsule had been developed and introduced in 201715–the PillCam Crohn’s system (PCC; Medtronic, Yokneam, Israel), in which the capsule and its software were tailored to patients with CD,16 allowing visualization of both the small and large intestines.

Machine-learning technology is a subclass of artificial intelligence (AI), affecting many aspects of medical practice,17 including several medical fields such as radiology, dermatology, gastroenterology and ophthalmology.18 Deep learning is a subclass of machine-learning which is mainly based on artificial neural networks.17,18 In the recent years, applications of deep-learning, including convolutional neural networks (CNN), for VCE have been well studied, demonstrating an accurate performance for detection of various gastrointestinal pathologies (e.g., gastrointestinal bleeding,19-22 angioectasias,23-26 esophagus and small-bowel mucosal ulcers26-31).

In this paper we aimed to review the accumulating data regarding the use of PCC among patients with CD. We also aimed to describe the innovative and emerging use of AI among patients with CD undergoing VCE.

PANENTERIC CAPSULE IN PATIENTS WITH CD

In 2017, a new panenteric VCE was introduced.16 The PCC is a two-headed capsule with a field of view of 344°, along with an adaptive frame rate technology which obtains up to 35 frames per second adapting to the speed of transit, allowing better tissue coverage and battery conservation.16 The PCC system platform and software have a novel assessment for inflammatory disease, specifically CD. The software divides the small bowel into three anatomic segments according to their length, as well as the colon. Three key assessment parameters are then assessed: disease distribution, lesion severity and linear extent. The most severe lesion and most common lesion in each segment are documented (Fig. 1).

Figure 1. (A) PillCam Crohn's capsule, DR3 data recorder and wireless sensors. (B) A representative graphic of a patient with active Montreal L1 and images of small bowel lesions. (C) RAPIDTM Reader Software breaks down small bowel segment based on identified anatomical landmarks. The reader classifies the most severe and most common lesion (none, mild, moderate and severe), presence or absence of stricture and extent of disease (0%–10%, 10%–30%, 30%–60%, 60%–100% of segment). Adapted from Eliakim R, et al. Endosc Int Open 2018;6:E1235-E124616 and Tai FW, et al. United European Gastroenterol J 2021;9:248-255.33

CLINICAL PRACTICE USING THE PCC

Leighton et al.15 demonstrated an improved performance of the PCC compared with ileo-colonoscopy (IC) among 66 CD patients with active disease who underwent both procedures. Either per-subject diagnostic yield rate or per-segment diagnostic yield rate for active CD lesions, was higher in the PCC compared with the IC procedure (83.3% vs 69.7% [yield difference, 13.6%; 95% confidence interval, 2.6% to 24.7%], and 40.6% vs 32.7% [yield difference, 7.9%; 95% confidence interval, 3.3% to 12.4%], respectively). Considering the substantial rate of active lesions detected only by the PCC (18%, 12/66), in which only one was limited to the proximal bowel, it was concluded that this procedure should be at least a complementary one to the IC among patients with CD. Leighton’s study used the capsule without its specific software. In 2020, a study performed by Bruining et al.,32 included 99 patients with established non-stricturing CD to assess the performance of PCC compared with IC and/or magnetic resonance enterography (MRE), in the detection of mucosal lesions in this population. The authors demonstrated comparable sensitivity rates and higher specificity rates in the overall intestinal assessment between the PCC compared to the MRE and/or IC (94% vs 100%, p=0.125 and 74% vs 22%, p=0.001, respectively). PCC had higher sensitivity and specificity in the proximal small bowel compared with MRE, higher specificity in the terminal ileum compared with MRE and/or IC, and equal performance in the colon compared to the IC. Patients’ satisfaction was superior for the capsule compared with the two other procedures. These findings emphasized the great advantage of PCC to enable a reliable disease staging of anatomic involvement among patients with CD, while undergoing a single procedure. Tai et al.33 examined the PCC performance in predicting the need of treatment intensification among 93 patients with CD (22 suspected CD, 71 established CD). PCC detected active disease in 48 out of 71 (67.6%) patients with established CD, and in three out of 22 patients (13.6%) with suspected CD. Disease extent was upstaged in 24 out of 71 (33%) patients with CD, of them, nine patients with newly upper gastrointestinal tract involvement. Overall, PCC findings led to treatment intensification in 36 out of 93 (39%) patients, and it was associated with proximal small-bowel involvement. Neither symptoms nor biochemical markers (i.e., fecal calprotectin, C-reactive protein) reliably identified active CD compared with PCC. This study demonstrated the important role of PCC in diagnosing, disease staging and optimizing disease treatment among patients with suspected or established CD. Oliva et al.,35 assessed the yield of PCC among 48 pediatric patients with quiescent CD (Crohn’s Disease Activity Index34 <10) to monitor mucosal healing and deep remission in a treat-to-target strategy. The PCC detected significant inflammation in 34 out of 48 (71%) patients involving the small-bowel or the colon (16/26 patients in clinical remission vs 18/22 patients with clinical activity). Accordingly, these findings led to treatment change in 34 out of 48 (71%) patients at baseline and in 11 out of 48 (23%) patients at 24 weeks follow-up. As a result, mucosal healing rate increased from 21% at baseline to 58% at week 52. Thus, PCC treat-to-target approach, led to higher rates of mucosal healing and deep remission among this population. In 2020, Eliakim et al.,36 evaluated the accuracy of a novel scoring system for PCC including 41 patients with CD. For each small bowel tertile, the Lewis score (LS) was extracted using the automated calculator embedded in the software. Similarly, LS was calculated for the right and left colon. The small-bowel LS was derived of the score of the tertile with the most significant disease involvement plus stricture score. The correlation of Eliakim score for PCC to fecal calprotectin was higher than between LS and fecal calprotectin (r=0.32 and r=0.54 respectively, p=0.001 for both). Table 1 summarizes PCC studies as mentioned above.

Table 1 . Summary of the PillCam Crohn’s Capsule (PCC) Studies.

----
Study (year)Study designPatientsComparative procedurePerformance measuresSafety
Leighton et al. (2017)15Prospective66 Patients with active CDIC83.3% and 69.7% of CD lesions in the PCC group vs IC group, respectively1: Obstructive symptoms following PCC procedure
1: GIT symptoms following PC ingestion
1: GIT symptoms following bowel preparation protocol
Eliakim et al. (2018)16Prospective feasibility study41 Patients with established or suspected IBD--No retained capsule was reported
Bruining et al. (2020)32Prospective99 Patients with non-stricturing CDIC, MREComparable sensitivity rate between PCC and either IC or MRE1: Partial bowel obstruction due to retained capsule
Higher specificity rate compared with MRE in detection of small-bowel lesions. Comparable specificity compared to IC in detection of TI and colon lesions1: Sigmoid perforation during IC
Tai et al. (2021)33Observational93 Patients (22 suspected CD, 71 established CD)-33%: Upstaging of disease extent2: Retained capsule (small-bowel and colon strictures)
9 Patients with newly upper GIT involvement
39%: Disease management change
Oliva et al. (2018)35Prospective48 Pediatric patients with quiescent CD-Disease management change in 71% and 23% at baseline and at 24 wk, respectively3: Nausea and vomiting following bowel preparation protocol
Accordingly, mucosal healing rate increased from 21% (baseline) to 58% (52 wk)
Eliakim et al. (2020)36RCT41 Patients with CDLewis score, fecal calprotectinBetter correlation of Eliakim score to fecal calprotectin than Lewis score to fecal calprotectinNo capsule retention

CD, Crohn’s disease; IC, ileo-colonoscopy; GIT, gastrointestinal tract; PC, patency capsule; IBD, inflammatory bowel disease; MRE, magnetic resonance enterography; TI, terminal ileum; RCT, randomized control trial..


BOWEL PREPARATION AND INTESTINAL CLEANSING

Table 2 summarizes bowel preparation protocols and cleaning performance in PCC studies. All the studies’ protocols used polyethylene glycol based solution prior to the capsule ingestion (two divided doses of 1.5 to 2 L administered in the evening before and in the morning of the examination day). Food but a clear liquid diet was prohibited on the day before and the examination day. Additional laxative was used upon the capsule had been reached to the small bowel. Bowel cleansing was graded as poor, fair, good or excellent.37 Comparing bowel cleansing level between PCC and IC, there was no difference in small-bowel cleansing, while colon cleansing was significantly better in the latter procedure.15,32 Overall bowel cleansing was better for small-bowel portions than the colon portions (good/excellent rate of 80% to 90% and up to 75%, respectively). Out of 386 patients undergoing PCC, there were only two cases in which colon preparation was inadequate to preclude reading of the colon frames.33,36

Table 2 . Bowel Preparation Protocols and Cleaning Performance in PillCam Crohn’s Capsule (PCC) Studies.

StudyBowel preparation protocolPreparation performance37
Leighton et al. (2017)15
  • Day before (–1): clear liquid diet and 2 L PEG at the evening. Day 0: 2 L PEG at the morning.

  • Optional: 10 mg Metoclopramide 1 hr after capsule ingestion..

  • Upon small-bowel detection and (again) 3 hr later: 88 mL Suprep+1 L water.

  • 10 mg bisacodyl suppository 2 hr later.

  • % Excellent/good cleansing level: >80% in each part of the colon and the TI for the IC procedure..

  • For the PCC procedure: TI 87.9%, colon segments 39.4%–62.9%.

Eliakim et al. (2018)16
  • Day (–1): clear liquid diet and 2L PEG/Fortrans/Solucion Bohm at the evening.

  • Day 0: 2 L PEG/Fortrans/Solucion Bohm at the morning.

  • Optional: 10 mg Metoclopramide 1hr after capsule ingestion (if the capsule was detected in the stomach).

  • Upon small-bowel detection 88 mL Suprep +240 mL water or 1 sachet of PICO-SALAX and again 3 hr later.

  • 10 mg bisacodyl suppository 2 hr later.

% Excellent/good cleansing level: >97.5% in the small bowel vs up to 75 % in the colon
Bruining et al. (2020)32
  • Day (–1): clear liquid diet.

  • 13–15 hr and 1–3 hr before PCC: 2 L PEG.

  • 10 mg Metoclopramide or 250 mg erythromycin 1 hr after PCC ingestion.

  • Upon small-bowel detection and 3 hr later: 88 mL of sodium/Magnesium sulfate diluted to 1 L of water.

  • 10 mg bisacodyl suppository 2 hr later.

  • % Excellent/good:.

  • 79% proximal small bowel.

  • 90% TI.

  • 64% colon.

Tai et al. (2021)33
  • Day before (–1): clear liquid diet and 2 L PEG at the evening..

  • Day 0: 2 L PEG at the morning Optional: 10 mg Metoclopramide 1 hr after capsule ingestion..

  • Upon small-bowel detection and (again) 3 hr later: 88 mL Suprep+1 L water.

  • 10 mg bisacodyl suppository 2 hr later.

Inadequate bowel preparation: 1/93 (1.1%)
Oliva et al. (2018)35
  • Day (–1): clear liquid diet with 50 mL up to 2 L of PEG solution.

  • Day 0: 1 hr before capsule ingestion 50 mL up to 2 L of PEG solution.

  • Optional: 0.25 mg/kg of domperidone (for delayed gastric transit >1 hr).

  • Once, the PCC had been detected in the small bowel.

  • - first booster of sodium sulfate 30 mL was given and another booster of 15 mL, 3 hr later.

  • Optional: 10 mg bisacodyl suppository 3.5 hr later.

  • Small-bowel and colon cleansing level:.

  • Excellent: 30%.

  • Good: 58%.

  • Fair: 10%.

  • Poor: 2%.

Eliakim et al. (2020)36
  • Day (–1): clear liquid diet and 1.5 L of PEG on the evening before the examination.

  • Day 0: 1.5 L an hour prior to capsule ingestion.

  • PICO-SALAX (10 mg sodium picosulfate) diluted in 75 mL of water upon small-bowel detection.

  • A 10 mg of metoclopramide PO if the capsule remained in the stomach for more than an hour after ingestion (optional).

  • If the PCC was not excreted within 3 hr of ingestion, a second sachet of PICO-SALAX was administered 3 hr after the first one.

  • A 10 mg Bisacodyl suppository 2 hr later if the capsule was not excreted.

  • % Excellent/good:.

  • Small bowel: 94.4%.

  • Colon: 75%.

  • % Fair and poor for the colon 22.5% and 2.5%, respectively.

PEG, polyethylene glycol; TI, terminal ileum; IC, ileo-colonoscopy; PO, per os..


SAFETY PROFILE USING THE PCC AND PROCEDURE COMPLETION

Of 386 patients undergoing PCC,15,16,32,33,35,36 only 12 patients experienced serious adverse events (including three cases of capsule retention [<1%]32,33) (Table 1). One case occurred despite patency confirmation by patency capsule (PC) procedure,33 one case after MRE assurance (without PC procedure),32 and a sole case of capsule retention due to a colonic stricture,33 though PC had passed uneventfully. Of them, one patient was hospitalized due to partial bowel obstruction.32 All cases were attributed to bowel strictures, and required PCC retrieval and stricture dilation by endoscopic procedure.32,33 No case required surgical treatment.

Other serious adverse events included two cases of obstructive symptoms and signs after PCC and PC procedures,15 gastrointestinal tract symptoms following bowel-cleansing preparation protocol15 and sigmoid perforation during IC procedure.32 Other non-serious adverse events including nausea, vomiting and abdominal pain occurred in less than 15% of the procedures. No serious adverse events related to the PCC were reported in pediatric CD patients (Table 1), while two episodes of nausea and one episode of vomiting were reported in three patients following adherence to bowel-preparation protocol.35

Tai et al.33 reported there were eight patients (8.6%) with incomplete colon examination (excluding a colonic stricture), five were due to loss of battery power, two due to loss of capsule signal (2.2%) and one due to inadequate bowel preparation (1.1%). Oliva et al.35 noted 17 out of 142 (~1.2%) procedures in which the capsule were not excreted before the battery expired, though seven of them reached the rectum, enabling a complete evaluation of the colon.

AI-BASED DETECTION OF CD LESIONS AMONG PATIENTS UNDERGOING SMALL BOWEL VCE

A single VCE procedure, captures and broadcasts an average of 12,000 images per-patient, making it tedious for a single reader reading and interpreting an entire examination. The latter may require 30 to 40 minutes on average, even for experienced VCE readers.38,39 Leenhardt et al.40 observed more than 80% of interobserver agreement rate in the identifying of ulcerative and inflammatory lesions during VCE reading, but still, there was a substantial rate of disagreement in VCE interpretation. Considering the monotone manner of VCE reading, with other technical challenges including no way to direct or focus the camera and the existence of only few frames for each lesion, its considerable rate of missed lesions (10%) is conceivable.41 AI-based VCE reading, including CNN algorithms to interpret VCE frames, has the potential to minimize the above-mentioned drawbacks, performing automated image analysis and interpretation.18 The CNN automatically extracts the features from raw input data (i.e., VCE frames), to identify distinct patterns in the dataset (e.g., small-bowel ulcers). The main dataset is randomly distributed into training, validation, and testing sets. The training set is used to fit the model and hyper-parameters, while the validation set is used to evaluate model performance. The testing set, which is sometimes an external dataset, provides an unbiased evaluation of the final model (Figs 2 and 3).42,43

Figure 2. Visualization of dataset splits performing a convolutional neural network model.

Figure 3. Convolutional neural networks architecture representation.
Conv, convolutional; ReLU, rectified linear unit.

AI PERFORMANCE IN THE DETECTION OF ULCERS AND EROSIONS

Since 2018, data regarding the detection of ulcers and/or erosions using deep-learning application in VCE have been accumulated (Table 3 summarizes their main characteristics and findings).

Table 3 . Summary of the Published Studies on Artificial Intelligence for Detection of Ulcers/Erosions in the Small Bowel.

StudyStudy designAlgorithmNo. of patientsCohort detailsType of lesion (No. of normal/pathological frames)No. of frames (training/validation datasets)Performance measures
Fan et al. (2018)31RetrospectiveAlexNet CNN144NASmall-bowel ulcers and erosions (13,000/8,160)12,910/8,250Accuracy of 95.16% and 95.34% for the detection of ulcers and erosions, respectively
Aoki et al. (2019)30RetrospectiveCNN-based on SSD180Patients with various causes of erosions and ulcers*Small-bowel ulcers and erosions (10,000/5,800)5,360/10,440Accuracy of 90.8% for the detection of ulcers and erosions
Wang et al. (2019)27RetrospectiveModified1,504NASmall-bowel ulcers (19,457/17,821)32,919/4,359Accuracy of 90.1% for the detection of ulcers
RetinaNet
Ding et al. (2019)44RetrospectiveCNN-based auxiliary model6,970Patients with various small-bowel VCE findingsPathological vs normal VCE frame (NA)158,235/113,268,334Sensitivity and specificity of 99.73% and 100% for the detection of ulcers
Otani et al. (2020)26RetrospectiveModified194Patients undergoing VCE for occult/overt GIB, tumor follow-up or IBDPathological vs normal VCE frames (34,437/5,526)39,963/1,247Accuracy of 98.6%–99.3% for the detection of ulcers and erosions, respectively
RetinaNet
Klang et al. (2020)28RetrospectiveCNN49Patients with or without CD with ulcerated or normal mucosaSmall-bowel CD ulcers (10,249/7,391)14,112/3,528Accuracy of 95.4%–96.7% and 73.7%–98.2%, for per-lesion analysis and per-patient analysis, respectively
Barash et al. (2021)47RetrospectiveCNN49Patients with or without CD with ulcerated or normal mucosaGrading of small-bowel CD ulcers (10,249/7,391)1,242/248Accuracy rate of 91% comparing grade 1 to grade 3 ulcerations
Klang et al. (2021)48RetrospectiveGoogle’s EfficientNet networksNAPatients with or without CD with ulcerated or normal mucosaSmall-bowel strictures (14,266/13,626)NAAccuracy rate of 93.5% and 78.9% for the detection of strictures, and for the classification of ulcerated versus non-ulcerated strictures, respectively
Hwang et al. (2021)45RetrospectiveVGGNetNAPatients undergoing small-bowel VCEClassification of hemorrhagic and ulcerative small-bowel lesions (8,578/4,738)7,556/5,760Accuracy rate of 96.62%–96.83%
Mascarenhas Saraiva et al. (2021)50RetrospectiveXception4,319Patients undergoing VCE with normal mucosa, or small-bowel pathology (polyps, ulcers, vascular lesions, etc.)Classification of higher risk small-bowel lesions for bleeding (18,010/35,545)42,844/10,711AUC for ulcer detection of 0.99 for P1 ulcers, and 1.00 for P2 ulcers
Afonso et al. (2022)49RetrospectiveCNNNAPatients with small-bowel ulcers, erosions, or normal small-bowel VCEClassification of potential risk for bleeding of small-bowel ulcers and erosions (1,897/4,233)4,904/1,226Accuracy rate of 95.6% for the detection and classification of erosions and/or ulcers (with any bleeding potential)
Majtner et al. (2021)52ProspectiveResNet-5038Patients with suspected or known CD undergoing PCCDetection of small-bowel or colon CD lesions and classification of the severity of these lesions (4,996/2,748)5,419/767Accuracy rate of 98.4%–98.6% to detect small-bowel and or colon inflammatory/ulcerative lesions
Ferreira et al. (2022)53RetrospectiveXceptionNAPatients undergoing PCCDetection of small-bowel or colon ulcers or erosions (19,190/5,300)19,740/4,935Accuracy rate of 98.8% for the detection of ulcers and erosions
Kratter et al. (2022)46RetrospectiveEfficientNetNAPatients with and without CD undergoing small-bowel VCE or PCCDetection of small-bowel or colon ulcers or erosions (15,684/17,416)NAAccuracy rate of 97.4% for the detection and the grading of mucosal ulcers in different VCE types

CNN, convolutional neural network; NA, not applicable; SSD, single-shot detector; VCE, video capsule endoscopy; GIB, gastrointestinal bleeding; IBD, inflammatory bowel disease; CD, Crohn’s disease; AUC, area under the curve; PCC, PillCam Crohn’s capsule..

*Patients who used nonsteroidal anti-inflammatory drugs (26%), patients with IBD (11%), small-bowel malignancy (7%), anastomotic ulcer (6%), ischemic enteritis (2%), Meckel diverticulum (2%), radiation enteritis (1%), miscellaneous (3%), and unknown cause (45%); The dataset contained frames of various small-bowel lesions including ulcers, erosions, vascular lesions, tumors, polyps, protruding lesion, vascular lesion, bleeding, parasites, and diverticulum and normal small-bowel VCE frames; Based on Saurin’s classification.51.



Fan et al.31 presented a novel computer-aided method to detect ulcers and erosions in the small-bowel with a high accuracy rate (>95%). This model demonstrated higher sensitivity rate in the detection of ulcers compared with erosions, probably due to more distinctive features of the former compared with the latter. Though excellent performance has been achieved, there were about 5% of false positive rate. Aoki et al.,30 trained a CNN system, to detect small-bowel ulcerations and erosions from a pool of frames, originated from various small-bowel pathologies (nonsteroidal anti-inflammatory drugs, inflammatory bowel disease, malignancy, etc.). Sensitivity rates were almost comparable either in nonsteroidal anti-inflammatory drugs or CD originated frames (~90%). Despite the high-speed review time by the model (44.8 images per second), it had still identified three pathological frames, which were missed by the conventional readers. Hence, emphasizing its great advantage in the detection of fine features in the frame. On the other hand, high degree of obscuration due to bubbles, debris, and bile led to 11.8% of false negative. Wang et al.27 used a second glance detection framework to detect small-bowel ulcers. Their model both classified images and also provided bounding box for lesion localization. In comparison to previously studied frameworks (RetinaNet, Faster-RCNN), using the second glance improved small ulcer detection by 10%. Still, ulcer size had a prime effect on the detection rate (92% vs 85% for ulcer size >1% and <1% of the whole image, respectively).

Two previously published studies have focused on a diagnosis of multiple types of lesions (i.e., ulcers, vascular lesions, tumors, etc.) rather than a single one.26,44 In a multicenter study, Ding et al.44 presented a CNN-based model auxiliary with increased detection rate by 20% compared with conventional reading, either per-lesion or per-patient analysis. Interestingly, though CNN-based model was significantly more sensitive in the detection of small-bowel ulcers, sensitivity has been improved only by 1% to 2% compared with conventional reading. Otani et al.,26 used a modified RetinaNet CNN, demonstrating an accuracy rate of ~99% in the detection of small-bowel ulcers and erosions as well as for vascular lesions. Notably, the modified RetinaNet CNN model had a higher area under the curve (AUC) value compared with the Single-Shot MultiBox Detector based AI system, which had predominantly been used in previously published studies. The latter’s performance has been proven among external validation cohort from 40 patients, though with a more modest AUC value (0.996 vs 0.928). Ding et al.44 and Otani et al.26 reported four and three cases of missed pathological diagnosis, respectively.

Klang et al.,28 retrospectively collected VCE frames from 49 patients with and without CD, to evaluate CNN performance in the detection of small-bowel ulcers. Like similar studies, the authors noted an impressive accuracy rate for the detection of ulcers using the trained dataset. However, the model’s accuracy rate on unseen patients ranged from 73.7% to 98.2%, probably reflecting real-world practice. The average duration for detecting a complete film was 204.7±93.9 seconds.

Hwang et al.45 trained CNN model in two different ways: a combined model (hemorrhagic and ulcerative lesions trained separately) and a binary model (all abnormal images trained without discrimination). Both the combined and binary models acquired high accuracy for lesion detection, and the difference between the two models was not significant (96.83% vs 96.62%, p=0.122). However, there were higher sensitivity and negative predictive value rates of the combined model compared with the binary one, leading to lower rates of missed diagnosis (23 cases vs 47 cases).

Recently, Kratter et al.46 developed a combined model for two different capsules (PillCam Crohn and PillCam SB3, Medtronic), with excellent performance in detection of intestinal ulcers (accuracy rate of 97.4%), providing an essential tool in real-life practice of patients with CD in which several types of VCE may be used.

ULCER SEVERITY GRADING, DETECTION OF STRICTURES AND BLEEDING POTENTIAL ASSESSMENT

As part of inflammatory lesion detection (i.e., ulcer and erosion), several studies have been focused on classifying lesions based on its distinct parameters, to better predict disease course and personalize disease management.

In 2021, Barash et al.47 demonstrated a novel use of ordinal CNN model for ulcer severity grading among patients with CD. Severity grading of CD ulcers was based on the PillCam CD classification (grade 1-3 from mild to severe) (Fig. 4). The best performance was in distinguishing between grade 1 to grade 3 ulcerations, achieving an accuracy rate of 91%. In differentiating between grade 2 to either grade 1 or grade 3, the performance was less impressive (accuracy of 65% and 79%, respectively), consistent with the performance of the conventional reading method (distinction of severity category, involving grade-2 ulcer achieved up to 40% of accuracy rate). Kratter et al.,46 demonstrated similar results in classification ulcerations to grade 1 and grade 3, with AUC of 0.99.

Figure 4. Severity grading of small-bowel ulcers (A: mild, B: moderate, C: severe) based on the PillCam Crohn's disease classification. Adapted from Barash Y, et al. Gastrointest Endosc 2021;93:187-192, with permission from Elsevier.47

Klang et al.48 evaluated the ability of a neural network model in identifying intestinal strictures for the first time. For classifying stricture versus non-stricture lesions, the network exhibited an average accuracy of 93.5%. The ulcerated versus non-ulcerated strictures classification network resulted in an accuracy rate of 78.9% (Fig. 5).

Figure 5. Class activation maps (heatmaps) of an ulcer image. Heatmaps enabled a visual presentation of image regions which led to lesion classification. Adapted from Klang E, et al. J Crohns Colitis 2021;15:749-756, with permission from Oxford University Press.48

Two studies from the same group demonstrated a novel CNN model to identify and classify small-bowel ulcers (among other enteric lesions) in whom having high risk for bleeding49,50 based on Saurin classification.51 According to Saurin classification, the hemorrhagic potential of ulcers was depended on their size: small ulcers were regarded as P1 lesions, while large ulcerations (>20 mm) were regarded as P2 lesions. Mascarenhas Saraiva et al.50 showed that among a wide range of enteric lesions, mucosal ulcers were identified with a sensitivity of 81% for P1 lesions and 94% for P2 lesions, presenting impressive values of AUC (0.99 and 1.00, respectively). A recent study by Afonso et al.,49 focused on risk potential assessment of small-bowel ulcers and erosions, achieving an accuracy rate of 95.6% in the detection and classification of erosions and ulcers, with any bleeding potential.

AI APPLICATIONS IN PCC

Two studies were conducted to evaluate deep learning performance among patients with CD undergoing PCC.52,53 Majtner et al.52 used two splitting methods: random one and per-patient one (in which each patient’s frame was used only for training, validation, or testing). Only four of 558 images of the colon were misclassified as the small-bowel, and only seven of 1,000 images of the small-bowel were misclassified as the colon. The accuracy rates for the per-patient split and the random-split were 98.4% and 98.6%, either for small-bowel or colon lesions. Ferreira et al.,53 demonstrated an impressive performance in the detection of ulcers and erosions in patients undergoing PCC (accuracy rate, 98.8%; negative predictive value, 99.5%), as well. Considering the average rate of 68 frames per second, it was estimated that only 12 minutes would require for a full PCC video revision.

FUTURE CHALLENGES USING THE AI FOR VCE

Though AI performance in patients undergoing VCE is impressive, there are still several challenges to be address in future studies. First, all but a single study were retrospective,54 limiting the ability to explore performance in a real-life practice. Second, as some of the recent published studies focused on the classification of distinct parameters in CD lesions, it is of prime importance to further discriminate lesion features to improve prognosis predication, and accordingly personalize disease management. Third, as per-patient rather than per-lesion analysis better reflects a real-life practice, further adjustment and fine-tuning of CNN models are needed to cope with indistinguishable features, among frames from a single patient, to improve the accuracy rate of lesion classification. Fourth, comparative researches and accuracy-thresholds standardization should be addressed as well as demonstration of clinical correlation before it will be implemented in real-life practice.55 Fifth, though capsule readers are generally eager toward AI-based VCE reading and interpretation, a substantial part of them are frightened of its implementation in real-life practice.54 Moreover, almost half of capsule readers are aware of using AI application in medical fields.54 Thus, a learning program training addressing it, is of prime importance among this population.54,55 Sixth, assessment of its cost-effectiveness should be performed prior to AI implementation to real-life practice.54 Finally, several technical adjustments might improve CNN model performance: (1) the current datasets composed of selected still frames, rather than video films,55 with inherited risk of selection bias; (2) external validation datasets are mostly absent in the current studies,54 limiting generalization of the studies’ findings; (3) stringent bowel-preparation protocols should be implemented to better cope with the low (but still exists) missed diagnosis rate using AI in VCE; (4) most of the studies have been dealt with a single type of VCE, limiting the use of the examined models in other VCE types.55 Further development of a single and universal model for all VCE types, will probably prompt the incorporation of AI-based VCE reading in real-life clinical practice.

CONCLUSIONS

VCE has a crucial role in the management of patients with CD, to enable a reliable evaluation and monitoring of patients with active, as well as quiescent disease. The introduction of the colon capsule and subsequently the PCC allow a visualization of the entire small and large bowel during a single procedure, to facilitate disease management in those patients. The highest yield in determining disease extent and severity in a feasible and single procedure may improve patients’ adherence, mainly in patients with long-standing disease. Furthermore, the PCC has a prime benefit to precisely stage disease severity and extent, leading to therapy optimization and better clinical outcomes. The superiority of PCC in the detection of small-bowel lesions compared with MRE and in the same single procedure to identify colon involvement is promised in this field. As mentioned, severe adverse events are rare, mostly preventable by PC ingestion before VCE procedure.

Finally, the recent developments of machine learning applications in the detection and the grading of small and large bowel lesions (i.e., ulcer and its severity, erosion, stricture, and assessment of bleeding potential) have led to excellent performance and high accuracy rates as detailed above. Using the CNN models may shorten VCE reading time (up to 95%55), resulting in a less tedious process with a potential to minimize missed diagnosis and false positive rates. However, the published literature in this field, a part of a single study was retrospective, which limits the ability to assess it in real-life practice, as well as the lacking cost-effectiveness evaluation. Also, to the best of our knowledge, no study has been conducted to assess AI-based VCE reading to predict future clinical outcomes in patients with CD. Considering the efficient and rapid process of AI-based VCE reading, developing of prediction-model using CNN architecture may substantially improve disease management of patients with CD, to afford treatment tailoring in this population.

CONFLICTS OF INTEREST

S.B.H. has received advisory board and/or consulting fees from Abbvie, Takeda, Janssen, Celltrion, Pfizer, GSK, Ferring, Novartis, Roche, Gilead, NeoPharm, Predicta Med, Galmed, Medial Earlysign, BMS and Eli Lilly, holds stocks/options in Predicta Med, Evinature & Galmed, and received research support from Abbvie, Takeda, Janssen, Celltrion, Pfizer, & Galmed. U.K. received speaker and consulting fees from Abbvie, BMS, Celltrion, Janssen, Medtronic, Pfizer and Takeda, research support from Medtronic, Takeda and Janssen. R.E. received consultant and speaker fees from Janssen, Abbvie, Takeda and Medtronic. The remaining authors declare that they have no conflicts of interest.

Fig 1.

Figure 1.(A) PillCam Crohn's capsule, DR3 data recorder and wireless sensors. (B) A representative graphic of a patient with active Montreal L1 and images of small bowel lesions. (C) RAPIDTM Reader Software breaks down small bowel segment based on identified anatomical landmarks. The reader classifies the most severe and most common lesion (none, mild, moderate and severe), presence or absence of stricture and extent of disease (0%–10%, 10%–30%, 30%–60%, 60%–100% of segment). Adapted from Eliakim R, et al. Endosc Int Open 2018;6:E1235-E124616 and Tai FW, et al. United European Gastroenterol J 2021;9:248-255.33
Gut and Liver 2023; 17: 516-528https://doi.org/10.5009/gnl220507

Fig 2.

Figure 2.Visualization of dataset splits performing a convolutional neural network model.
Gut and Liver 2023; 17: 516-528https://doi.org/10.5009/gnl220507

Fig 3.

Figure 3.Convolutional neural networks architecture representation.
Conv, convolutional; ReLU, rectified linear unit.
Gut and Liver 2023; 17: 516-528https://doi.org/10.5009/gnl220507

Fig 4.

Figure 4.Severity grading of small-bowel ulcers (A: mild, B: moderate, C: severe) based on the PillCam Crohn's disease classification. Adapted from Barash Y, et al. Gastrointest Endosc 2021;93:187-192, with permission from Elsevier.47
Gut and Liver 2023; 17: 516-528https://doi.org/10.5009/gnl220507

Fig 5.

Figure 5.Class activation maps (heatmaps) of an ulcer image. Heatmaps enabled a visual presentation of image regions which led to lesion classification. Adapted from Klang E, et al. J Crohns Colitis 2021;15:749-756, with permission from Oxford University Press.48
Gut and Liver 2023; 17: 516-528https://doi.org/10.5009/gnl220507

Table 1 Summary of the PillCam Crohn’s Capsule (PCC) Studies

----
Study (year)Study designPatientsComparative procedurePerformance measuresSafety
Leighton et al. (2017)15Prospective66 Patients with active CDIC83.3% and 69.7% of CD lesions in the PCC group vs IC group, respectively1: Obstructive symptoms following PCC procedure
1: GIT symptoms following PC ingestion
1: GIT symptoms following bowel preparation protocol
Eliakim et al. (2018)16Prospective feasibility study41 Patients with established or suspected IBD--No retained capsule was reported
Bruining et al. (2020)32Prospective99 Patients with non-stricturing CDIC, MREComparable sensitivity rate between PCC and either IC or MRE1: Partial bowel obstruction due to retained capsule
Higher specificity rate compared with MRE in detection of small-bowel lesions. Comparable specificity compared to IC in detection of TI and colon lesions1: Sigmoid perforation during IC
Tai et al. (2021)33Observational93 Patients (22 suspected CD, 71 established CD)-33%: Upstaging of disease extent2: Retained capsule (small-bowel and colon strictures)
9 Patients with newly upper GIT involvement
39%: Disease management change
Oliva et al. (2018)35Prospective48 Pediatric patients with quiescent CD-Disease management change in 71% and 23% at baseline and at 24 wk, respectively3: Nausea and vomiting following bowel preparation protocol
Accordingly, mucosal healing rate increased from 21% (baseline) to 58% (52 wk)
Eliakim et al. (2020)36RCT41 Patients with CDLewis score, fecal calprotectinBetter correlation of Eliakim score to fecal calprotectin than Lewis score to fecal calprotectinNo capsule retention

CD, Crohn’s disease; IC, ileo-colonoscopy; GIT, gastrointestinal tract; PC, patency capsule; IBD, inflammatory bowel disease; MRE, magnetic resonance enterography; TI, terminal ileum; RCT, randomized control trial.


Table 2 Bowel Preparation Protocols and Cleaning Performance in PillCam Crohn’s Capsule (PCC) Studies

StudyBowel preparation protocolPreparation performance37
Leighton et al. (2017)15

Day before (–1): clear liquid diet and 2 L PEG at the evening. Day 0: 2 L PEG at the morning

Optional: 10 mg Metoclopramide 1 hr after capsule ingestion.

Upon small-bowel detection and (again) 3 hr later: 88 mL Suprep+1 L water

10 mg bisacodyl suppository 2 hr later

% Excellent/good cleansing level: >80% in each part of the colon and the TI for the IC procedure.

For the PCC procedure: TI 87.9%, colon segments 39.4%–62.9%

Eliakim et al. (2018)16

Day (–1): clear liquid diet and 2L PEG/Fortrans/Solucion Bohm at the evening

Day 0: 2 L PEG/Fortrans/Solucion Bohm at the morning

Optional: 10 mg Metoclopramide 1hr after capsule ingestion (if the capsule was detected in the stomach)

Upon small-bowel detection 88 mL Suprep +240 mL water or 1 sachet of PICO-SALAX and again 3 hr later

10 mg bisacodyl suppository 2 hr later

% Excellent/good cleansing level: >97.5% in the small bowel vs up to 75 % in the colon
Bruining et al. (2020)32

Day (–1): clear liquid diet

13–15 hr and 1–3 hr before PCC: 2 L PEG

10 mg Metoclopramide or 250 mg erythromycin 1 hr after PCC ingestion

Upon small-bowel detection and 3 hr later: 88 mL of sodium/Magnesium sulfate diluted to 1 L of water

10 mg bisacodyl suppository 2 hr later

% Excellent/good:

79% proximal small bowel

90% TI

64% colon

Tai et al. (2021)33

Day before (–1): clear liquid diet and 2 L PEG at the evening.

Day 0: 2 L PEG at the morning Optional: 10 mg Metoclopramide 1 hr after capsule ingestion.

Upon small-bowel detection and (again) 3 hr later: 88 mL Suprep+1 L water

10 mg bisacodyl suppository 2 hr later

Inadequate bowel preparation: 1/93 (1.1%)
Oliva et al. (2018)35

Day (–1): clear liquid diet with 50 mL up to 2 L of PEG solution

Day 0: 1 hr before capsule ingestion 50 mL up to 2 L of PEG solution

Optional: 0.25 mg/kg of domperidone (for delayed gastric transit >1 hr)

Once, the PCC had been detected in the small bowel

- first booster of sodium sulfate 30 mL was given and another booster of 15 mL, 3 hr later

Optional: 10 mg bisacodyl suppository 3.5 hr later

Small-bowel and colon cleansing level:

Excellent: 30%

Good: 58%

Fair: 10%

Poor: 2%

Eliakim et al. (2020)36

Day (–1): clear liquid diet and 1.5 L of PEG on the evening before the examination

Day 0: 1.5 L an hour prior to capsule ingestion

PICO-SALAX (10 mg sodium picosulfate) diluted in 75 mL of water upon small-bowel detection

A 10 mg of metoclopramide PO if the capsule remained in the stomach for more than an hour after ingestion (optional)

If the PCC was not excreted within 3 hr of ingestion, a second sachet of PICO-SALAX was administered 3 hr after the first one

A 10 mg Bisacodyl suppository 2 hr later if the capsule was not excreted

% Excellent/good:

Small bowel: 94.4%

Colon: 75%

% Fair and poor for the colon 22.5% and 2.5%, respectively

PEG, polyethylene glycol; TI, terminal ileum; IC, ileo-colonoscopy; PO, per os.


Table 3 Summary of the Published Studies on Artificial Intelligence for Detection of Ulcers/Erosions in the Small Bowel

StudyStudy designAlgorithmNo. of patientsCohort detailsType of lesion (No. of normal/pathological frames)No. of frames (training/validation datasets)Performance measures
Fan et al. (2018)31RetrospectiveAlexNet CNN144NASmall-bowel ulcers and erosions (13,000/8,160)12,910/8,250Accuracy of 95.16% and 95.34% for the detection of ulcers and erosions, respectively
Aoki et al. (2019)30RetrospectiveCNN-based on SSD180Patients with various causes of erosions and ulcers*Small-bowel ulcers and erosions (10,000/5,800)5,360/10,440Accuracy of 90.8% for the detection of ulcers and erosions
Wang et al. (2019)27RetrospectiveModified1,504NASmall-bowel ulcers (19,457/17,821)32,919/4,359Accuracy of 90.1% for the detection of ulcers
RetinaNet
Ding et al. (2019)44RetrospectiveCNN-based auxiliary model6,970Patients with various small-bowel VCE findingsPathological vs normal VCE frame (NA)158,235/113,268,334Sensitivity and specificity of 99.73% and 100% for the detection of ulcers
Otani et al. (2020)26RetrospectiveModified194Patients undergoing VCE for occult/overt GIB, tumor follow-up or IBDPathological vs normal VCE frames (34,437/5,526)39,963/1,247Accuracy of 98.6%–99.3% for the detection of ulcers and erosions, respectively
RetinaNet
Klang et al. (2020)28RetrospectiveCNN49Patients with or without CD with ulcerated or normal mucosaSmall-bowel CD ulcers (10,249/7,391)14,112/3,528Accuracy of 95.4%–96.7% and 73.7%–98.2%, for per-lesion analysis and per-patient analysis, respectively
Barash et al. (2021)47RetrospectiveCNN49Patients with or without CD with ulcerated or normal mucosaGrading of small-bowel CD ulcers (10,249/7,391)1,242/248Accuracy rate of 91% comparing grade 1 to grade 3 ulcerations
Klang et al. (2021)48RetrospectiveGoogle’s EfficientNet networksNAPatients with or without CD with ulcerated or normal mucosaSmall-bowel strictures (14,266/13,626)NAAccuracy rate of 93.5% and 78.9% for the detection of strictures, and for the classification of ulcerated versus non-ulcerated strictures, respectively
Hwang et al. (2021)45RetrospectiveVGGNetNAPatients undergoing small-bowel VCEClassification of hemorrhagic and ulcerative small-bowel lesions (8,578/4,738)7,556/5,760Accuracy rate of 96.62%–96.83%
Mascarenhas Saraiva et al. (2021)50RetrospectiveXception4,319Patients undergoing VCE with normal mucosa, or small-bowel pathology (polyps, ulcers, vascular lesions, etc.)Classification of higher risk small-bowel lesions for bleeding (18,010/35,545)42,844/10,711AUC for ulcer detection of 0.99 for P1 ulcers, and 1.00 for P2 ulcers
Afonso et al. (2022)49RetrospectiveCNNNAPatients with small-bowel ulcers, erosions, or normal small-bowel VCEClassification of potential risk for bleeding of small-bowel ulcers and erosions (1,897/4,233)4,904/1,226Accuracy rate of 95.6% for the detection and classification of erosions and/or ulcers (with any bleeding potential)
Majtner et al. (2021)52ProspectiveResNet-5038Patients with suspected or known CD undergoing PCCDetection of small-bowel or colon CD lesions and classification of the severity of these lesions (4,996/2,748)5,419/767Accuracy rate of 98.4%–98.6% to detect small-bowel and or colon inflammatory/ulcerative lesions
Ferreira et al. (2022)53RetrospectiveXceptionNAPatients undergoing PCCDetection of small-bowel or colon ulcers or erosions (19,190/5,300)19,740/4,935Accuracy rate of 98.8% for the detection of ulcers and erosions
Kratter et al. (2022)46RetrospectiveEfficientNetNAPatients with and without CD undergoing small-bowel VCE or PCCDetection of small-bowel or colon ulcers or erosions (15,684/17,416)NAAccuracy rate of 97.4% for the detection and the grading of mucosal ulcers in different VCE types

CNN, convolutional neural network; NA, not applicable; SSD, single-shot detector; VCE, video capsule endoscopy; GIB, gastrointestinal bleeding; IBD, inflammatory bowel disease; CD, Crohn’s disease; AUC, area under the curve; PCC, PillCam Crohn’s capsule.

*Patients who used nonsteroidal anti-inflammatory drugs (26%), patients with IBD (11%), small-bowel malignancy (7%), anastomotic ulcer (6%), ischemic enteritis (2%), Meckel diverticulum (2%), radiation enteritis (1%), miscellaneous (3%), and unknown cause (45%); The dataset contained frames of various small-bowel lesions including ulcers, erosions, vascular lesions, tumors, polyps, protruding lesion, vascular lesion, bleeding, parasites, and diverticulum and normal small-bowel VCE frames; Based on Saurin’s classification.51


References

  1. Iddan G, Meron G, Glukhovsky A, Swain P. Wireless capsule endoscopy. Nature 2000;405:417.
    Pubmed CrossRef
  2. Ben-Horin S, Lahat A, Amitai MM, et al. Assessment of small bowel mucosal healing by video capsule endoscopy for the prediction of short-term and long-term risk of Crohn's disease flare: a prospective cohort study. Lancet Gastroenterol Hepatol 2019;4:519-528.
    Pubmed CrossRef
  3. Dionisio PM, Gurudu SR, Leighton JA, et al. Capsule endoscopy has a significantly higher diagnostic yield in patients with suspected and established small-bowel Crohn's disease: a meta-analysis. Am J Gastroenterol 2010;105:1240-1248.
    Pubmed CrossRef
  4. Jensen MD, Nathan T, Rafaelsen SR, Kjeldsen J. Diagnostic accuracy of capsule endoscopy for small bowel Crohn's disease is superior to that of MR enterography or CT enterography. Clin Gastroenterol Hepatol 2011;9:124-129.
    Pubmed CrossRef
  5. Flamant M, Trang C, Maillard O, et al. The prevalence and outcome of jejunal lesions visualized by small bowel capsule endoscopy in Crohn's disease. Inflamm Bowel Dis 2013;19:1390-1396.
    Pubmed CrossRef
  6. Pennazio M, Spada C, Eliakim R, et al. Small-bowel capsule endoscopy and device-assisted enteroscopy for diagnosis and treatment of small-bowel disorders: European Society of Gastrointestinal Endoscopy (ESGE) Clinical Guideline. Endoscopy 2015;47:352-376.
    Pubmed CrossRef
  7. Turner D, Ricciuto A, Lewis A, et al. STRIDE-II: an update on the Selecting Therapeutic Targets in Inflammatory Bowel Disease (STRIDE) Initiative of the International Organization for the Study of IBD (IOIBD): determining therapeutic goals for treat-to-target strategies in IBD. Gastroenterology 2021;160:1570-1583.
    Pubmed CrossRef
  8. Yamada K, Nakamura M, Yamamura T, et al. Diagnostic yield of colon capsule endoscopy for Crohn's disease lesions in the whole gastrointestinal tract. BMC Gastroenterol 2021;21:75.
    Pubmed KoreaMed CrossRef
  9. Tang N, Chen H, Chen R, Tang W, Zhang H. Combination of serological biomarkers and clinical features to predict mucosal healing in Crohn's disease: a multicenter cohort study. BMC Gastroenterol 2022;22:229.
    Pubmed KoreaMed CrossRef
  10. Triester SL, Leighton JA, Leontiadis GI, et al. A meta-analysis of the yield of capsule endoscopy compared to other diagnostic modalities in patients with non-stricturing small bowel Crohn's disease. Am J Gastroenterol 2006;101:954-964.
    Pubmed CrossRef
  11. Negreanu L, Smarandache G, Mateescu RB. Role of capsule endoscopy Pillcam COLON 2 in patients with known or suspected Crohn's disease who refused colonoscopy or underwent incomplete colonoscopic exam: a case series. Tech Coloproctol 2014;18:277-283.
    Pubmed CrossRef
  12. D'Haens G, Löwenberg M, Samaan MA, et al. Safety and feasibility of using the second-generation PillCam colon capsule to assess active colonic Crohn's disease. Clin Gastroenterol Hepatol 2015;13:1480-1486.
    Pubmed CrossRef
  13. Boal Carvalho P, Rosa B, Dias de Castro F, Moreira MJ, Cotter J. PillCam COLON 2 in Crohn's disease: a new concept of pan-enteric mucosal healing assessment. World J Gastroenterol 2015;21:7233-7241.
    Pubmed KoreaMed CrossRef
  14. Hausmann J, Schmelz R, Walldorf J, Filmann N, Zeuzem S, Albert JG. Pan-intestinal capsule endoscopy in patients with postoperative Crohn's disease: a pilot study. Scand J Gastroenterol 2017;52:840-845.
    Pubmed CrossRef
  15. Leighton JA, Helper DJ, Gralnek IM, et al. Comparing diagnostic yield of a novel pan-enteric video capsule endoscope with ileocolonoscopy in patients with active Crohn's disease: a feasibility study. Gastrointest Endosc 2017;85:196-205.
    Pubmed CrossRef
  16. Eliakim R, Spada C, Lapidus A, et al. Evaluation of a new pan-enteric video capsule endoscopy system in patients with suspected or established inflammatory bowel disease: feasibility study. Endosc Int Open 2018;6:E1235-E1246.
    Pubmed KoreaMed CrossRef
  17. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521:436-444.
    Pubmed CrossRef
  18. Soffer S, Klang E, Shimon O, et al. Deep learning for wireless capsule endoscopy: a systematic review and meta-analysis. Gastrointest Endosc 2020;92:831-839.
    Pubmed CrossRef
  19. Xiao J, Meng MQ. A deep convolutional neural network for bleeding detection in Wireless Capsule Endoscopy images. Annu Int Conf IEEE Eng Med Biol Soc 2016;2016:639-642.
    Pubmed CrossRef
  20. Xiao J, Meng MQ. Gastrointestinal bleeding detection in wireless capsule endoscopy images using handcrafted and CNN features. Annu Int Conf IEEE Eng Med Biol Soc 2017;2017:3154-3157.
    Pubmed CrossRef
  21. Aoki T, Yamada A, Kato Y, et al. Automatic detection of blood content in capsule endoscopy images based on a deep convolutional neural network. J Gastroenterol Hepatol 2020;35:1196-1200.
    Pubmed CrossRef
  22. Xing X, Jia X, Meng MQ. Bleeding detection in wireless capsule endoscopy image video using superpixel-color histogram and a subspace KNN classifier. Annu Int Conf IEEE Eng Med Biol Soc 2018;2018:1-4.
    Pubmed CrossRef
  23. Noya F, Alvarez-Gonzalez MA, Benitez R. Automated angiodysplasia detection from wireless capsule endoscopy. Annu Int Conf IEEE Eng Med Biol Soc 2017;2017:3158-3161.
    Pubmed CrossRef
  24. Tsuboi A, Oka S, Aoyama K, et al. Artificial intelligence using a convolutional neural network for automatic detection of small-bowel angioectasia in capsule endoscopy images. Dig Endosc 2020;32:382-390.
    Pubmed CrossRef
  25. Leenhardt R, Vasseur P, Li C, et al. A neural network algorithm for detection of GI angiectasia during small-bowel capsule endoscopy. Gastrointest Endosc 2019;89:189-194.
    Pubmed CrossRef
  26. Otani K, Nakada A, Kurose Y, et al. Automatic detection of different types of small-bowel lesions on capsule endoscopy images using a newly developed deep convolutional neural network. Endoscopy 2020;52:786-791.
    Pubmed CrossRef
  27. Wang S, Xing Y, Zhang L, Gao H, Zhang H. A systematic evaluation and optimization of automatic detection of ulcers in wireless capsule endoscopy on a large dataset using deep convolutional neural networks. Phys Med Biol 2019;64:235014.
    Pubmed CrossRef
  28. Klang E, Barash Y, Margalit RY, et al. Deep learning algorithms for automated detection of Crohn's disease ulcers by video capsule endoscopy. Gastrointest Endosc 2020;91:606-613.
    Pubmed CrossRef
  29. Alaskar H, Hussain A, Al-Aseem N, Liatsis P, Al-Jumeily D. Application of convolutional neural networks for automated ulcer detection in wireless capsule endoscopy images. Sensors (Basel) 2019;19:1265.
    Pubmed KoreaMed CrossRef
  30. Aoki T, Yamada A, Aoyama K, et al. Automatic detection of erosions and ulcerations in wireless capsule endoscopy images based on a deep convolutional neural network. Gastrointest Endosc 2019;89:357-363.
    Pubmed CrossRef
  31. Fan S, Xu L, Fan Y, Wei K, Li L. Computer-aided detection of small intestinal ulcer and erosion in wireless capsule endoscopy images. Phys Med Biol 2018;63:165001.
    Pubmed CrossRef
  32. Bruining DH, Oliva S, Fleisher MR, Fischer M, Fletcher JG; BLINK study group. Panenteric capsule endoscopy versus ileocolonoscopy plus magnetic resonance enterography in Crohn's disease: a multicentre, prospective study. BMJ Open Gastroenterol 2020;7:e000365.
    Pubmed KoreaMed CrossRef
  33. Tai FW, Ellul P, Elosua A, et al. Panenteric capsule endoscopy identifies proximal small bowel disease guiding upstaging and treatment intensification in Crohn's disease: a European multicentre observational cohort study. United European Gastroenterol J 2021;9:248-255.
    Pubmed KoreaMed CrossRef
  34. Best WR, Becktel JM, Singleton JW, Kern F Jr. Development of a Crohn's disease activity index. National Cooperative Crohn's Disease Study. Gastroenterology 1976;70:439-444.
    Pubmed CrossRef
  35. Oliva S, Aloi M, Viola F, et al. A treat to target strategy using panenteric capsule endoscopy in pediatric patients with Crohn's disease. Clin Gastroenterol Hepatol 2019;17:2060-2067.
    Pubmed CrossRef
  36. Eliakim R, Yablecovitch D, Lahat A, et al. A novel PillCam Crohn's capsule score (Eliakim score) for quantification of mucosal inflammation in Crohn's disease. United European Gastroenterol J 2020;8:544-551.
    Pubmed KoreaMed CrossRef
  37. Leighton JA, Rex DK. A grading scale to evaluate colon cleansing for the PillCam COLON capsule: a reliability study. Endoscopy 2011;43:123-127.
    Pubmed CrossRef
  38. Koulaouzidis A, Iakovidis DK, Karargyris A, Plevris JN. Optimizing lesion detection in small-bowel capsule endoscopy: from present problems to future solutions. Expert Rev Gastroenterol Hepatol 2015;9:217-235.
    Pubmed CrossRef
  39. Mishkin DS, Chuttani R, Croffie J, et al. ASGE Technology Status Evaluation Report: wireless capsule endoscopy. Gastrointest Endosc 2006;63:539-545.
    Pubmed CrossRef
  40. Leenhardt R, Buisson A, Bourreille A, et al. Nomenclature and semantic descriptions of ulcerative and inflammatory lesions seen in Crohn's disease in small bowel capsule endoscopy: an international Delphi consensus statement. United European Gastroenterol J 2020;8:99-107.
    Pubmed KoreaMed CrossRef
  41. Rondonotti E, Pennazio M, Toth E, Koulaouzidis A. How to read small bowel capsule endoscopy: a practical guide for everyday use. Endosc Int Open 2020;8:E1220-E1224.
    Pubmed KoreaMed CrossRef
  42. Rai HM, Chatterjee K. Detection of brain abnormality by a novel Lu-Net deep neural CNN model from MR images. Mach Learn Appl 2020;2:100004.
    CrossRef
  43. Islam MM, Karray F, Alhajj R, Zeng J. A review on deep learning techniques for the diagnosis of novel coronavirus (COVID-19). IEEE Access 2021;9:30551-30572.
    Pubmed KoreaMed CrossRef
  44. Ding Z, Shi H, Zhang H, et al. Gastroenterologist-level identification of small-bowel diseases and normal variants by capsule endoscopy using a deep-learning model. Gastroenterology 2019;157:1044-1054.
    Pubmed CrossRef
  45. Hwang Y, Lee HH, Park C, et al. Improved classification and localization approach to small bowel capsule endoscopy using convolutional neural network. Dig Endosc 2021;33:598-607.
    Pubmed CrossRef
  46. Kratter T, Shapira N, Lev Y, et al. Deep learning multi-domain model provides accurate detection and grading of mucosal ulcers in different capsule endoscopy types. Diagnostics (Basel) 2022;12:2490.
    Pubmed KoreaMed CrossRef
  47. Barash Y, Azaria L, Soffer S, et al. Ulcer severity grading in video capsule images of patients with Crohn's disease: an ordinal neural network solution. Gastrointest Endosc 2021;93:187-192.
    Pubmed CrossRef
  48. Klang E, Grinman A, Soffer S, et al. Automated detection of Crohn's disease intestinal strictures on capsule endoscopy images using deep neural networks. J Crohns Colitis 2021;15:749-756.
    Pubmed CrossRef
  49. Afonso J, Saraiva MM, Ferreira JP, et al. Automated detection of ulcers and erosions in capsule endoscopy images using a convolutional neural network. Med Biol Eng Comput 2022;60:719-725.
    Pubmed CrossRef
  50. Mascarenhas Saraiva MJ, Afonso J, Ribeiro T, et al. Deep learning and capsule endoscopy: automatic identification and differentiation of small bowel lesions with distinct haemorrhagic potential using a convolutional neural network. BMJ Open Gastroenterol 2021;8:e000753.
    Pubmed KoreaMed CrossRef
  51. Saurin JC, Delvaux M, Gaudin JL, et al. Diagnostic value of endoscopic capsule in patients with obscure digestive bleeding: blinded comparison with video push-enteroscopy. Endoscopy 2003;35:576-584.
    Pubmed CrossRef
  52. Majtner T, Brodersen JB, Herp J, Kjeldsen J, Halling ML, Jensen MD. A deep learning framework for autonomous detection and classification of Crohn's disease lesions in the small bowel and colon with capsule endoscopy. Endosc Int Open 2021;9:E1361-E1370.
    Pubmed KoreaMed CrossRef
  53. Ferreira JP, de Mascarenhas Saraiva MJ, Afonso JPL, et al. Identification of ulcers and erosions by the novel PillcamTM Crohn's capsule using a convolutional neural network: a multicentre pilot study. J Crohns Colitis 2022;16:169-172.
    Pubmed CrossRef
  54. Leenhardt R, Fernandez-Urien Sainz I, Rondonotti E, et al. PEACE: perception and expectations toward artificial intelligence in capsule endoscopy. J Clin Med 2021;10:5708.
    Pubmed KoreaMed CrossRef
  55. Leenhardt R, Koulaouzidis A, Histace A, et al. Key research questions for implementation of artificial intelligence in capsule endoscopy. Therap Adv Gastroenterol 2022;15:17562848221132683.
    Pubmed KoreaMed CrossRef
Gut and Liver

Vol.18 No.1
January, 2024

pISSN 1976-2283
eISSN 2005-1212

qrcode
qrcode

Share this article on :

  • line

Popular Keywords

Gut and LiverQR code Download
qr-code

Editorial Office