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

Application of Artificial Intelligence in the Detection and Characterization of Colorectal Neoplasm

Kyeong Ok Kim1 , Eun Young Kim2

1Division of Gastroenterology and Hepatology, Department of Internal Medicine, Yeungnam University College of Medicine, and 2Division of Gastroenterology and Hepatology, Department of Internal Medicine, Daegu Catholic University School of Medicine, Daegu, Korea

Correspondence to: Eun Young Kim
ORCID https://orcid.org/0000-0003-3965-9964
E-mail kimey@cu.ac.kr

Received: June 12, 2020; Accepted: June 28, 2020

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 2021;15(3):346-353. https://doi.org/10.5009/gnl20186

Published online August 12, 2020, Published date May 15, 2021

Copyright © Gut and Liver.

Endoscpists always have tried to pursue a perfect colonoscopy, and application of artificial intelligence (AI) using deep-learning algorithms is one of the promising supportive options for detection and characterization of colorectal polyps during colonoscopy. Many retrospective studies conducted with real-time application of AI using convolutional neural networks have shown improved colorectal polyp detection. Moreover, a recent randomized clinical trial reported additional polyp detection with shorter analysis time. Studies conducted regarding polyp characterization provided additional promising results. Application of AI with narrow band imaging in real-time prediction of the pathology of diminutive polyps resulted in high diagnostic accuracy. In addition, application of AI with endocytoscopy or confocal laser endomicroscopy was investigated for realtime cellular diagnosis, and the diagnostic accuracy of some studies was comparable to that of pathologists. With AI technology, we can expect a higher polyp detection rate with reduced time and cost by avoiding unnecessary procedures, resulting in enhanced colonoscopy efficiency. However, for AI application in actual daily clinical practice, more prospective studies with minimized selection bias, consensus on standardized utilization, and regulatory approval are needed.

Keywords: Polyp, Colon, Colonoscopy, Artificial intelligence, Convolutional neural network

The incidence of colorectal cancer (CRC), the third most common cancer worldwide,1 has been steadily increasing in the Republic of Korea recently. Because most of the CRC arise from adenomas, detection and complete removal of these precancerous lesions can reduce the incidence and mortality associated with CRC.2,3 However, for the effective prevention of CRC, high-quality colonoscopy that detects all the polyps, is a prerequisite. Adenoma detection rate (ADR) has been considered as one of the important quality indicators of colonoscopy, and the inverse association between ADR and incidence of interval CRC has been established.4,5 Because colonoscopy may not be perfect, many efforts to enhance ADR have been made.6 Artificial intelligence (AI), one of the promising technologies, can mitigate the shortcomings of colonoscopy. As it is easy to obtain polyp images to provide enough data for AI training, many studies using this technology have been reported lately.7 Well-trained AI modelling can increase polyp identification and optical diagnosis. Especially, its use can increase the detection rate of polyps in the right colon, where a higher miss rate is understandably anticipated with conventional colonoscopy.8

In this review, we aim to contemplate the current status and future directions of AI applications, including technologies, applications, efficacy, and unmet needs, regarding colorectal neoplasm.

AI, having machine intelligence that is different from the natural intelligence displayed by humans and other animals,9 can learn and solve problems.10 Earlier, machine learning (ML) had been the main focus of AI. A computer-vision algorithm, developed by computer scientists based on earlier ML research for the detection of colorectal polyps was “hand-crafted,” based on the features adopted by the designers.11 In other words, human efforts or instructions were needed for extracting features, such as color, shape, or texture of polyps, because the earlier ML could learn only the classification of the extracted images.12 Although the “hand-crafted” algorithm showed high accuracy, the risk of missing the lesions without those extracted features or obtaining false positive results was of concern because it was designed to detect the lesions with certain features chosen by designers. In addition, actual clinical application was limited due to the difference in image quality and also slow processing time.11

Deep-learning (DL) algorithm, one of the subtype of ML introduced in the 1980s, could overcome the limitation of earlier ML by combining both the extraction and classification of image features using deep neural networks (DNN).13 The innovative method of DNN gained significant attention because of self-learning capability of DL that could automatically identify polyp and non-polyp features from the huge dataset, instead of capturing specific features of a polyp using several networks. Self-extraction, the key feature, was achieved using backpropagation algorithm and changing the internal parameters of each neural network layer.14

Among the variable classes of DNN for image and video applications, convolutional neural network (CNN), the most popular method, can carry out layers of convolutions and can completely connect layers to unite all features in the final outcome.15,16 Provided with sufficient annotated data, CNNs can be trained to describe, in detail, what they see and discriminate polyps from non-polypoid lesions.11 Currently, AI can be trained with enough input data, thanks to the easy accessibility of big data, aiding rapid progress in the research and application of AI in colonoscopic polyp detection and characterization.17

1. AI for detection of colorectal polyp

Since the initial study regarding computer-aided detection based on “hand-crafted” data,18 the performance of AI has improved, especially with the introduction of DL, for colorectal neoplasm detection.19,20 Table 1 shows the summary of the clinical studies of AI for the detection of colorectal polyps.18-26

Urban et al. 20 reported the first real-time application. They initially pretrained AI using ImageNet and then trained the deep CNNs. The algorithm was tested with multiple sets of colonoscopic images and 11 challenging videos sets. The result was very promising with 97% sensitivity, 95% specificity, and 96% overall accuracy. For the practical utilization in real-world, the CNN-assisted video was reviewed by experts. During the index colonoscopy, 28 polyps were noted in nine standard colonoscopic videos. Experts could identify 36 polyps without and 45 polyps with CNN assistance. The additional 17 polyps identified with CNN assistance were not larger than 10 mm in size. Further, the algorithm was faster than the real-time analysis by endoscopists (10 ms/frame vs 33–40 ms/frame). Meanwhile, Yu et al. 24 developed a novel three-dimensional CNN algorithm that could learn more representative spatiotemporal features and improve performance of automated polyp detection.

Wang et al. 21 developed a CNN system that processed data in real-time with a 77 millisecond delay on monitor, in which, a blue box appeared around the area of the polyp upon its detection, along with an alarm. They randomized the colonoscopy of about 1,058 patients into those using this system and the standard one and proved an increased ADR in AI (29.1% vs 20.3%, respectively) with minimal delay in examining time. However, the robust detection rate was limited to polyps smaller than 10 mm in size, and 43.6% of the polyps were hyperplastic polyps with low malignant potential.27,28 The most recent meta-analysis of three randomized controlled trial of AI-assisted colonoscopy reported 32.9% of ADR (20.8% in standard colonoscopy, risk ratio [RR]=1.58, p<0.001) and 43.0% of polyp detection rate (27.8% in standard colonoscopy, RR=1.55, p<0.001).29 AI utility in screening colonoscopy to improve ADR looks optimistic.

2. AI for characterization of colorectal neoplasm

Accurate histologic diagnosis of the colorectal polyp before resection is desired because endoscopic resection and pathologic evaluation of lesions with very low risk of malignant potential may result in waste of time and cost. On the other hand, incomplete resection of lesions with high risk of malignancy should be avoided. Therefore, various advanced endoscopic techniques with image enhancement for optical diagnosis have been introduced, and various studies concerning the application of AI to these endoscopic systems have also been steadily reported to date (Table 2).

Narrow band imaging (NBI; Olympus Corp., Tokyo, Japan), an image-enhanced endoscopy used for the observation of microstructure or capillaries of colorectal neoplasm, could discriminate each polyp well, based on the pit and vascular pattern.30 Tischendorf et al. 31 and Gross et al. 32 investigated AI application to NBI (excluding its performance enhancement) for the characterization of colon polyps. They both extracted nine vessel features from the NBI images in a similar manner and sorted them into neoplastic and non-neoplastic lesions using a support vector machine (SVM), a discriminated ML model.15 The diagnostic accuracies of the two studies were 85.3% and 93.1%, respectively. In addition, Gross et al. 32 proved superior accuracy of AI-assisted diagnosis compared to that of nonexperts in differentiating colorectal polyps smaller than 10 mm in size, suggesting that AI could be a good support for the beginners of endoscopy.

Another Japanese group developed a real-time image recognition system that could achieve 93.2% accuracy in real-time prediction of diminutive polyp pathology.33 In addition, the follow-up recommendation based on the prediction by that model showed 92.7% consistency with Preservation and Incorporation of Valuable Endoscopic Innovations (PIVI)-2 criteria of American Society for Gastrointestinal Endoscopy for “resect-and-discard” strategy.34 Resect and discard strategy can bring a substantial economic benefit.

Two groups carried out retrospective studies based on DL. Byrne et al. 35 developed CNN model using NBI video frames. Although the model could not make adequate credence of histology prediction in 15% of polyps, it could differentiate diminutive adenomas from hyperplastic polyps with 94% accuracy. The sensitivity and specificity for detecting adenoma were 98% and 83%, respectively. Chen et al. 36 also developed a similar model, a DNN-computer aided diagnosis (CAD), with 2,157 images for identification of about 284 neoplastic or hyperplastic polyps smaller than 5 mm in size, with 89.6% positive predictive value (PPV) and 91.5% negative predictive value (NPV). Consequently, among 117 tubular adenomas, DNN-CAD could diagnose high grade dysplasia with 100% sensitivity and 94% specificity. The performance of DNN-CAD, when compared to that of four endoscopists with less than 1 year of experience, was superior with a shorter procedure time and perfect intra-observer agreement (kappa score of 1). The result of the above-mentioned two studies could satisfy the PIVI-2 threshold (90% NPV for adenoma detection) for “leave-in-place” strategy for diminutive hyperplastic polyps.34

Although there’s a lack of recent further studies, CAD of pit pattern by magnifying chromoendoscopy was done by quantitative analysis of pit structure or texture analysis of endoscopic images.37,38 Takemura et al. 39 reported 98.5% diagnostic accuracy after automatically evaluating the area, perimeter, major/minor fit ellipse and circularity of the pit using software.

Recently, novel introduction of in vivo contact microscopic imaging modalities, such as endocytoscopy (H290ECI; Olympus Corp.) and confocal laser endomicroscopy (Cellvizio; Mauna Kea Technologies Inc, Paris, France), enabled real-time diagnosis of cellular images.40,41 Because both endocytoscopy and endomicroscopy could enhance image analysis with focused fixed-size images, they were ideal to be used in combination with the AI system. They magnified the image with 500- or 1,000-fold power, respectively, during colonoscopy and showed diagnostic accuracy comparable to that of pathologists. The first application of AI in endocytoscopy was assessed by quantitative analysis of six nuclear features, and the accuracy for the detection of neoplastic change was 89.2%.42 Takeda et al. 43 trained the computer-aided ultrahigh (approximately ×400) magnification endoscopy system for the diagnosis of invasive CRC with 5,543 endocytoscopic images. This system, when assessed using 200 endocytoscopic test images, could discriminate invasive cancers with 89.4% sensitivity, 98.9% specificity, and 94.1% accuracy. Mori et al. 44 assessed the efficacy of an endocytoscopy-based CAD with 466 cases of diminutive polyps, and the NPV for diminutive rectosigmoid adenomas was 93.7%. They also proved that polyp diagnosis with AI, an add-on analysis, could reduce the cost of annual reimbursement for colonoscopy by 18.9%, by leaving 145 rectosigmoid diminutive polyps based on the AI support.45 However, AI endocytoscopy had a limitation–it needed pre-staining with crystal violet and methylene blue before the extraction of images. Misawa et al. 46 upgraded the system by combining endocytoscopy with NBI, eliminating the pre-staining step and resulting in 90.0% overall accuracy and 84.5% sensitivity within 0.3 seconds.

AI application with confocal endomicroscopy was assessed in several studies, in experimental setting, with promising accuracy.47 50 AI in other advanced endoscopies, including laser-induced fluorescence spectroscopy and autofluorescence endoscopy, was also evaluated retrospectively or prospectively.51-57 We can expect that the use of AI in these advanced endoscopies for optical diagnosis could aid the real-time decision making of endoscopists.

3. AI for combination of detection and characterization of colorectal polyps

For an endoscopist, both polyp detection and characterization are essential in clinical practice. An ideal scenario would be an AI-assisted immediate detection and characterization of the colorectal polyp. A Japanese group developed novel technologies that included two algorithm systems–one, based on DL algorithm, for the detection of polyps in white light images, and the other, for the prediction of pathology by endocytoscopic images generated by a photograph.58 According to the most recent study using CNN, AI system (Single Shot Multibox Detector) could detect 1,246 polyps with 92% sensitivity, 86% PPV, and 83% accuracy in polyp classification.59 Although more studies are needed, an ideal colonoscopy for the detection and characterization of colorectal neoplasm seems to be achieved with AI assistance.

4. Prediction of prognosis

Prior to DL, SVM was a highly efficient computational tool that classified and regressed best by optimizing a hyperplane with largest functional margin.15 Ichimasa et al. 60 evaluated the predictive factors for lymph node metastasis from endoscopically resected T1 CRC using SVM. Their result showed better sensitivity (100%), specificity (66%), and accuracy (69%) of the AI model than most of the current guidelines. In addition, this model could reduce the unnecessary additional colectomy after endoscopic resection of T1 CRC compared to the current guidelines that lead to misdiagnosis.

Although AI-assisted detection and diagnosis of colorectal neoplasm are promising, most of the studies are retrospective and covered lesions which might have been selected with bias. Well-designed prospective studies that present more reliable data compared to the previous retrospective studies are needed. Most of the studies regarding efficacy of AI deal with polypoid lesions. However, for practical utilization, the efficacy of AI needs to be consistent irrespective of the shape of the polyps. Therefore, more studies with all types of polyps, including polypoid, depressed, or flat type, are needed because these non-polypoid type lesions are often more aggressive.61

In addition, as AI is trained with high-quality images, the system has to overcome the blurry vision, inadequate preparation status, and variable unpredictable hurdles observed in actual practice. There is a need for real-time application of CAD and randomized controlled comparative study between usage and avoidance of AI.

Previous studies, conducted in a variable design, resulted in different primary outcomes. Because AI development needed both engineers, who dealt with the software, and clinicians, who contributed to the clinical use, the outcomes and study design could be different depending on the study conductors.11 Communication and collaboration between these different groups are also needed.

To apply AI in real clinical practice, a regulatory approval of AI-based decision making, the rules of which vary for each country, is an essential step. For that, we need to prove the minimal risk of treatment failure due to the misdiagnosis by AI.22,62 So, we need the evaluation of risk stratification for AI system through well-designed randomized clinical trials.

Because colonoscopy cannot completely prevent CRC, AI application in the field of colorectal neoplasm could be one of the promising options for enhancing the efficiency of colonosocpy. Owing to easy accessibility of big data and computer science, the AI technologies for the detection and classification of colorectal polyps have developed rapidly, with studies supporting the advantage of AI use in colonoscopy. However, obstacles, such as insufficient evidence of practical clinical usefulness, lack of consensus on standardized utilization, and need for regulatory approval, exist. For the ideal implementation of AI in actual clinical practice, comprehensive understanding of the strengths and weaknesses of the technology, qualified real-time studies, and accumulation of experience are warranted.


E.Y.K. is an editorial board member of the journal but did not involve in the peer reviewer selection, evaluation, or decision process of this article. No other potential conflicts of interest relevant to this article were reported.

Clinical Studies of Artificial Intelligence for the Detection of Colorectal Polyps

Author (year) Study design Algorithm type Dataset Processing time Results
Wang et al.
(2019)
21
Randomized
controlled study
Convolutional neural network 5,545 Images 25 fps with 77 ms latency 9% Increase of ADR
Klare et al.
(2019)
22
Prospective
In vivo
Convolutional neural network 55 Live colonoscopies 50 ms latency Sensitivity 75%/polyp
ADR 29% (31% in endoscopist)
Urban et al.
(2018)
20
Retrospective
Ex vivo
Convolutional neural network Image dataset: 8,641 polyps
Video: 20 colonoscopies
10 ms/frame
(real-time)
Image dataset: accuracy 96.4%
AUROC 0.991
Misawa et al.
(2018)19
Retrospective
Ex vivo
Convolutional neural network 135 Video clips No description Sensitivity 90%
Specificity 63.3%
Accuracy 76.5%
Zhang et al.
(2017)
23
Retrospective
Ex vivo
Convolutional neural network 150 Random+30 NBI images No description Sensitivity 98%
PPV 99%
AUROC 1.00
Yu et al.
(2017)
24
Retrospective
Ex vivo
Convolutional neural network ASU-Mayo 20 videos 1.23 s/frame Sensitivity 7%
PPV 88%
Angermann et al. (2017)
25
Retrospective
Ex vivo
Hand-crafted No description 20–185 ms
0.3-1.8 s delay
Sensitivity 100%/polyp
PPV50%
Tajbakhsh et al. (2015)
26
Retrospective
Ex vivo
Hand-crafted No description 2.6 s/frame Sensitivity 48% on proprietary database
Sensitivity 88% in CVC-colon DB
Karkanis et al. (2003)
18
Retrospective
Ex vivo
Hand-crafted 180 Still images 1.5 m/video Sensitivity 94%
Specificity 99%

ADR, adenoma detection rate; AUROC, area under the receiver operating characteristics; NBI, narrow band imaging; PPV, positive predictive value; ASU, Arizona State University; CVC, computer vision center; DB, data base.


Clinical Studies of Artificial Intelligence for Characterization of Colorectal Polyps

Author (year) Study design Classification target and base Algorithm type Image modality Dataset Results
Byrne et al. (2019)
35
Retrospective Histology of diminutive polyp Convolutional neural network NBI video frames 125 Diminutive polyp videos Sensitivity 98%
Specificity 83%
Accuracy 94%
Chen et al. (2018)
36
Retrospective Neoplastic or hyperplastic polyp <5 mm Convolutional neural network Magnifying NBI 284 Diminutive polyps image Sensitivity 96.3%
Specificity 78.1%
Accuracy 90.1%
Mori et al. (2018)
44
Prospective Diagnosis of neoplastic diminutive polyp SVM Endocytoscopy with NBI and stained images 466 Diminutive polyps from
325 patients
Prediction rate 98.1%
Takeda et al. (2017)
43
Retrospective Invasive CRC SVM Endocytoscopy with NBI and stained images 200 Images Sensitivity 89.4%
Specificity 98.9%
Accuracy 94.1%
Kominami et al. (2016)
33
Prospective Histology SVM with logistic regression Magnifying NBI 118 Colorectal lesions Sensitivity 95.9%
Specificity 93.3%
Accuracy 94.9%
Misawa et al. (2016)
46
Retrospective Microvascular findings SVM Endocytoscopy with NBI 100 Images Sensitivity 84.5%
Specificity 97.6%
Accuracy 90.0%
Mori et al. (2015)
42
Retrospective Neoplastic changes in small polyps Multivariate regression analysis Endocytoscopy 176 Polyps from 152 patients Sensitivity 92%
Specificity 79.5%
Accuracy 89.2%
Takemura et al. (2012)
57
Retrospective Pit pattern SVM Magnifying NBI 371 Images Sensitivity 97.8%
Specificity 97.9%
Accuracy 97.8%
Gross et al. (2011)
32
Prospective Small colonic polyp <10 mm SVM Magnifying NBI 434 Polyps from 214 patients Sensitivity 95%
Specificity 90.3%
Accuracy 93.1%
Tischendorf et al. (2010)
31
Prospective
pilot
Vascularization features SVM Magnifying NBI 209 Polyps from 128 patients Sensitivity 90%
Specificity 70.2%
Accuracy 85.3%
Takemura et al. (2010)
39
Retrospective Pit pattern HuPAS software version 1.3 Magnifying NBI with chromoendoscopy (crystal violet) 134 Images Accuracy 98.5%

NBI, narrow band imaging; SVM, support vector machine; CRC, colorectal cancer.


  1. American Cancer Society. Cancer Facts & Figures 2019. Atlanta: American Cancer Society, 2019.
  2. Winawer SJ, Zauber AG, Ho MN, et al. Prevention of colorectal cancer by colonoscopic polypectomy. The National Polyp Study Workgroup. N Engl J Med 1993;329:1977-1981.
    Pubmed CrossRef
  3. Nishihara R, Wu K, Lochhead P, et al. Long-term colorectal-cancer incidence and mortality after lower endoscopy. N Engl J Med 2013;369:1095-1105.
    Pubmed KoreaMed CrossRef
  4. Rex DK, Schoenfeld PS, Cohen J, et al. Quality indicators for colonoscopy. Gastrointest Endosc 2015;81:31-53.
    Pubmed CrossRef
  5. Kaminski MF, Regula J, Kraszewska E, et al. Quality indicators for colonoscopy and the risk of interval cancer. N Engl J Med 2010;362:1795-1803.
    Pubmed CrossRef
  6. Castaneda D, Popov VB, Verheyen E, Wander P, Gross SA. New technologies improve adenoma detection rate, adenoma miss rate, and polyp detection rate: a systematic review and meta-analysis. Gastrointest Endosc 2018;88:209-222.
    Pubmed CrossRef
  7. Liedlgruber M, Uhl A. Computer-aided decision support systems for endoscopy in the gastrointestinal tract: a review. IEEE Rev Biomed Eng 2011;4:73-88.
    Pubmed CrossRef
  8. Kudo SE, Mori Y, Misawa M, et al. Artificial intelligence and colonoscopy: current status and future perspectives. Dig Endosc 2019;31:363-371.
    Pubmed CrossRef
  9. Wikipedia. Artificial intelligence [Internet]. San Francisco: Wikipedia Foundation, Inc.; c2020 [cited 2020 Mar 30].
    Available from: https://en.wikipedia.org/wiki/Artificial_intelligence.
  10. Russell S, Norvig P. Artificial intelligence: a modern approach. 3rd ed. Harlow: Pearson Education Limited, 2009.
  11. Hoerter N, Gross SA, Liang PS. Artificial intelligence and polyp detection. Curr Treat Options Gastroenterol 2020;18:120-136.
    Pubmed KoreaMed CrossRef
  12. Ahmad OF, Soares AS, Mazomenos E, et al. Artificial intelligence and computer-aided diagnosis in colonoscopy: current evidence and future directions. Lancet Gastroenterol Hepatol 2019;4:71-80.
    Pubmed CrossRef
  13. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521:436-444.
    Pubmed CrossRef
  14. Takiyama H, Ozawa T, Ishihara S, et al. Automatic anatomical classification of esophagogastroduodenoscopy images using deep convolutional neural networks. Sci Rep 2018;8:7497.
    Pubmed KoreaMed CrossRef
  15. Yang YJ, Bang CS. Application of artificial intelligence in gastroenterology. World J Gastroenterol 2019;25:1666-1683.
    Pubmed KoreaMed CrossRef
  16. Yoon HJ, Kim JH. Lesion-based convolutional neural network in diagnosis of early gastric cancer. Clin Endosc 2020;53:127-131.
    Pubmed KoreaMed CrossRef
  17. Choi J, Shin K, Jung J, et al. Convolutional neural network technology in endoscopic imaging: artificial intelligence for endoscopy. Clin Endosc 2020;53:117-126.
    Pubmed KoreaMed CrossRef
  18. Karkanis SA, Iakovidis DK, Maroulis DE, Karras DA, Tzivras M. Computer-aided tumor detection in endoscopic video using color wavelet features. IEEE Trans Inf Technol Biomed 2003;7:141-152.
    Pubmed CrossRef
  19. Misawa M, Kudo SE, Mori Y, et al. Artificial intelligence-assisted polyp detection for colonoscopy: initial experience. Gastroenterology 2018;154:2027-2029.
    Pubmed CrossRef
  20. Urban G, Tripathi P, Alkayali T, et al. Deep learning localizes and identifies polyps in real time with 96% accuracy in screening colonoscopy. Gastroenterology 2018;155:1069-1078.
    Pubmed KoreaMed CrossRef
  21. Wang P, Berzin TM, Glissen Brown JR, et al. Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study. Gut 2019;68:1813-1819.
    Pubmed KoreaMed CrossRef
  22. Klare P, Sander C, Prinzen M, et al. Automated polyp detection in the colorectum: a prospective study (with videos). Gastrointest Endosc 2019;89:576-582.
    Pubmed CrossRef
  23. Zhang R, Zheng Y, Mak TW, et al. Automatic detection and classification of colorectal polyps by transferring low-level CNN features from nonmedical domain. IEEE J Biomed Health Inform 2017;21:41-47.
    Pubmed CrossRef
  24. Yu L, Chen H, Dou Q, Qin J, Heng PA. Integrating online and offline three-dimensional deep learning for automated polyp detection in colonoscopy videos. IEEE J Biomed Health Inform 2017;21:65-75.
    Pubmed CrossRef
  25. Angermann Q, Bernal J, Sánchez-Montes C, et al. Towards real-time polyp detection in colonoscopy videos: adapting still frame-based methodologies for video sequences analysis. In: Cardoso MJ, Arbel T, Luo X, eds. Computer assisted and robotic endoscopy and clinical image-based procedures. Cham: Springer, 2017:29-41.
    CrossRef
  26. Tajbakhsh N, Gurudu SR, Liang J. Automated polyp detection in colonoscopy videos using shape and context information. IEEE Trans Med Imaging 2016;35:630-644.
    Pubmed CrossRef
  27. Noshirwani KC, van Stolk RU, Rybicki LA, Beck GJ. Adenoma size and number are predictive of adenoma recurrence: implications for surveillance colonoscopy. Gastrointest Endosc 2000;51(4 Pt 1):433-437.
    Pubmed CrossRef
  28. Martínez ME, Baron JA, Lieberman DA, et al. A pooled analysis of advanced colorectal neoplasia diagnoses after colonoscopic polypectomy. Gastroenterology 2009;136:832-841.
    Pubmed KoreaMed CrossRef
  29. Aziz M, Fatima R, Dong C, Lee-Smith W, Nawras A. The impact of deep convolutional neural network-based artificial intelligence on colonoscopy outcomes: a systematic review with meta-analysis. J Gastroenterol Hepatol 2020;35:1676-1683.
    Pubmed CrossRef
  30. Tanaka S, Sano Y. Aim to unify the narrow band imaging (NBI) magnifying classification for colorectal tumors: current status in Japan from a summary of the consensus symposium in the 79th Annual Meeting of the Japan Gastroenterological Endoscopy Society. Dig Endosc 2011;23 Suppl 1:131-139.
    Pubmed CrossRef
  31. Tischendorf JJ, Gross S, Winograd R, et al. Computer-aided classification of colorectal polyps based on vascular patterns: a pilot study. Endoscopy 2010;42:203-207.
    Pubmed CrossRef
  32. Gross S, Trautwein C, Behrens A, et al. Computer-based classification of small colorectal polyps by using narrow-band imaging with optical magnification. Gastrointest Endosc 2011;74:1354-1359.
    Pubmed CrossRef
  33. Kominami Y, Yoshida S, Tanaka S, et al. Computer-aided diagnosis of colorectal polyp histology by using a real-time image recognition system and narrow-band imaging magnifying colonoscopy. Gastrointest Endosc 2016;83:643-649.
    Pubmed CrossRef
  34. Rex DK, Kahi C, O’Brien M, et al. The American Society for Gastrointestinal Endoscopy PIVI (Preservation and Incorporation of Valuable Endoscopic Innovations) on real-time endoscopic assessment of the histology of diminutive colorectal polyps. Gastrointest Endosc 2011;73:419-422.
    Pubmed CrossRef
  35. Byrne MF, Chapados N, Soudan F, et al. Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model. Gut 2019;68:94-100.
    Pubmed KoreaMed CrossRef
  36. Chen PJ, Lin MC, Lai MJ, Lin JC, Lu HH, Tseng VS. Accurate classification of diminutive colorectal polyps using computer-aided analysis. Gastroenterology 2018;154:568-575.
    Pubmed CrossRef
  37. Kudo S, Rubio CA, Teixeira CR, Kashida H, Kogure E. Pit pattern in colorectal neoplasia: endoscopic magnifying view. Endoscopy 2001;33:367-373.
    Pubmed CrossRef
  38. Kudo SE, Mori Y, Wakamura K, et al. Endocytoscopy can provide additional diagnostic ability to magnifying chromoendoscopy for colorectal neoplasms. J Gastroenterol Hepatol 2014;29:83-90.
    Pubmed CrossRef
  39. Takemura Y, Yoshida S, Tanaka S, et al. Quantitative analysis and development of a computer-aided system for identification of regular pit patterns of colorectal lesions. Gastrointest Endosc 2010;72:1047-1051.
    Pubmed CrossRef
  40. Kiesslich R, Burg J, Vieth M, et al. Confocal laser endoscopy for diagnosing intraepithelial neoplasias and colorectal cancer in vivo. Gastroenterology 2004;127:706-713.
    Pubmed CrossRef
  41. Mori Y, Kudo S, Ikehara N, et al. Comprehensive diagnostic ability of endocytoscopy compared with biopsy for colorectal neoplasms: a prospective randomized noninferiority trial. Endoscopy 2013;45:98-105.
    Pubmed CrossRef
  42. Mori Y, Kudo SE, Wakamura K, et al. Novel computer-aided diagnostic system for colorectal lesions by using endocytoscopy (with videos). Gastrointest Endosc 2015;81:621-629.
    Pubmed CrossRef
  43. Takeda K, Kudo SE, Mori Y, et al. Accuracy of diagnosing invasive colorectal cancer using computer-aided endocytoscopy. Endoscopy 2017;49:798-802.
    Pubmed CrossRef
  44. Mori Y, Kudo SE, Misawa M, et al. Real-time use of artificial intelligence in identification of diminutive polyps during colonoscopy: a prospective study. Ann Intern Med 2018;169:357-366.
    Pubmed CrossRef
  45. Mori Y, Kudo SE, East JE, et al. Cost savings in colonoscopy with artificial intelligence-aided polyp diagnosis: an add-on analysis of a clinical trial (with video). Gastrointest Endosc 2020;92:905-911.e1.
    Pubmed CrossRef
  46. Misawa M, Kudo SE, Mori Y, et al. Characterization of colorectal lesions using a computer-aided diagnostic system for narrow-band imaging endocytoscopy. Gastroenterology 2016;150:1531-1532.
    Pubmed CrossRef
  47. André B, Vercauteren T, Buchner AM, Krishna M, Ayache N, Wallace MB. Software for automated classification of probe-based confocal laser endomicroscopy videos of colorectal polyps. World J Gastroenterol 2012;18:5560-5569.
    Pubmed KoreaMed CrossRef
  48. Ştefănescu D, Streba C, Cârţână ET, Săftoiu A, Gruionu G, Gruionu LG. Computer aided diagnosis for confocal laser endomicroscopy in advanced colorectal adenocarcinoma. PLoS One 2016;11:e0154863.
    Pubmed KoreaMed CrossRef
  49. Tafreshi MK, Linard N, André B, Ayache N, Vercauteren T. Semi-automated query construction for content-based endomicroscopy video retrieval. Med Image Comput Comput Assist Interv 2014;17(Pt 1):89-96.
    Pubmed CrossRef
  50. Prieto SP, Lai KK, Laryea JA, Mizell JS, Muldoon TJ. Quantitative analysis of ex vivo colorectal epithelium using an automated feature extraction algorithm for microendoscopy image data. J Med Imaging (Bellingham) 2016;3:024502.
    Pubmed KoreaMed CrossRef
  51. Kuiper T, Alderlieste YA, Tytgat KM, et al. Automatic optical diagnosis of small colorectal lesions by laser-induced autofluorescence. Endoscopy 2015;47:56-62.
    Pubmed CrossRef
  52. Rath T, Tontini GE, Vieth M, Nägel A, Neurath MF, Neumann H. In vivo real-time assessment of colorectal polyp histology using an optical biopsy forceps system based on laser-induced fluorescence spectroscopy. Endoscopy 2016;48:557-562.
    Pubmed CrossRef
  53. Renkoski TE, Banerjee B, Graves LR, et al. Ratio images and ultraviolet C excitation in autofluorescence imaging of neoplasms of the human colon. J Biomed Opt 2013;18:16005.
    Pubmed KoreaMed CrossRef
  54. Arita K, Mitsuyama K, Kawano H, et al. Quantitative analysis of colorectal mucosal lesions by autofluorescence endoscopy: discrimination of carcinomas from other lesions. Oncol Rep 2011;26:43-48.
    Pubmed CrossRef
  55. Aihara H, Saito S, Inomata H, et al. Computer-aided diagnosis of neoplastic colorectal lesions using ‘real-time’ numerical color analysis during autofluorescence endoscopy. Eur J Gastroenterol Hepatol 2013;25:488-494.
    Pubmed CrossRef
  56. Inomata H, Tamai N, Aihara H, et al. Efficacy of a novel auto-fluorescence imaging system with computer-assisted color analysis for assessment of colorectal lesions. World J Gastroenterol 2013;19:7146-7153.
    Pubmed KoreaMed CrossRef
  57. Takemura Y, Yoshida S, Tanaka S, et al. Computer-aided system for predicting the histology of colorectal tumors by using narrow-band imaging magnifying colonoscopy (with video). Gastrointest Endosc 2012;75:179-185.
    Pubmed CrossRef
  58. Mori Y, Kudo SE, Misawa M, Mori K. Simultaneous detection and characterization of diminutive polyps with the use of artificial intelligence during colonoscopy. VideoGIE 2019;4:7-10.
    Pubmed KoreaMed CrossRef
  59. Ozawa T, Ishihara S, Fujishiro M, Kumagai Y, Shichijo S, Tada T. Automated endoscopic detection and classification of colorectal polyps using convolutional neural networks. Therap Adv Gastroenterol 2020;13:1756284820910659.
    Pubmed KoreaMed CrossRef
  60. Ichimasa K, Kudo SE, Mori Y, et al. Artificial intelligence may help in predicting the need for additional surgery after endoscopic resection of T1 colorectal cancer. Endoscopy 2018;50:230-240.
    Pubmed CrossRef
  61. Kudo SE, Takemura O, Ohtsuka K. Flat and depressed types of early colorectal cancers: from East to West. Gastrointest Endosc Clin N Am 2008;18:581-593.
    Pubmed CrossRef
  62. Chinzei K, Shimizu K, Mori K, et al. Regulatory science on AI-based medical devices and systems. Adv Biomed Eng 2018;7:118-123.
    CrossRef

Article

Review Article

Gut and Liver 2021; 15(3): 346-353

Published online May 15, 2021 https://doi.org/10.5009/gnl20186

Copyright © Gut and Liver.

Application of Artificial Intelligence in the Detection and Characterization of Colorectal Neoplasm

Kyeong Ok Kim1 , Eun Young Kim2

1Division of Gastroenterology and Hepatology, Department of Internal Medicine, Yeungnam University College of Medicine, and 2Division of Gastroenterology and Hepatology, Department of Internal Medicine, Daegu Catholic University School of Medicine, Daegu, Korea

Correspondence to:Eun Young Kim
ORCID https://orcid.org/0000-0003-3965-9964
E-mail kimey@cu.ac.kr

Received: June 12, 2020; Accepted: June 28, 2020

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

Endoscpists always have tried to pursue a perfect colonoscopy, and application of artificial intelligence (AI) using deep-learning algorithms is one of the promising supportive options for detection and characterization of colorectal polyps during colonoscopy. Many retrospective studies conducted with real-time application of AI using convolutional neural networks have shown improved colorectal polyp detection. Moreover, a recent randomized clinical trial reported additional polyp detection with shorter analysis time. Studies conducted regarding polyp characterization provided additional promising results. Application of AI with narrow band imaging in real-time prediction of the pathology of diminutive polyps resulted in high diagnostic accuracy. In addition, application of AI with endocytoscopy or confocal laser endomicroscopy was investigated for realtime cellular diagnosis, and the diagnostic accuracy of some studies was comparable to that of pathologists. With AI technology, we can expect a higher polyp detection rate with reduced time and cost by avoiding unnecessary procedures, resulting in enhanced colonoscopy efficiency. However, for AI application in actual daily clinical practice, more prospective studies with minimized selection bias, consensus on standardized utilization, and regulatory approval are needed.

Keywords: Polyp, Colon, Colonoscopy, Artificial intelligence, Convolutional neural network

INTRODUCTION

The incidence of colorectal cancer (CRC), the third most common cancer worldwide,1 has been steadily increasing in the Republic of Korea recently. Because most of the CRC arise from adenomas, detection and complete removal of these precancerous lesions can reduce the incidence and mortality associated with CRC.2,3 However, for the effective prevention of CRC, high-quality colonoscopy that detects all the polyps, is a prerequisite. Adenoma detection rate (ADR) has been considered as one of the important quality indicators of colonoscopy, and the inverse association between ADR and incidence of interval CRC has been established.4,5 Because colonoscopy may not be perfect, many efforts to enhance ADR have been made.6 Artificial intelligence (AI), one of the promising technologies, can mitigate the shortcomings of colonoscopy. As it is easy to obtain polyp images to provide enough data for AI training, many studies using this technology have been reported lately.7 Well-trained AI modelling can increase polyp identification and optical diagnosis. Especially, its use can increase the detection rate of polyps in the right colon, where a higher miss rate is understandably anticipated with conventional colonoscopy.8

In this review, we aim to contemplate the current status and future directions of AI applications, including technologies, applications, efficacy, and unmet needs, regarding colorectal neoplasm.

COMPUTER-AIDED DIAGNOSIS AND DEEP-LEARNING FOR DETECTION AND CHARACTERIZATION OF THE COLORECTAL POLYPS

AI, having machine intelligence that is different from the natural intelligence displayed by humans and other animals,9 can learn and solve problems.10 Earlier, machine learning (ML) had been the main focus of AI. A computer-vision algorithm, developed by computer scientists based on earlier ML research for the detection of colorectal polyps was “hand-crafted,” based on the features adopted by the designers.11 In other words, human efforts or instructions were needed for extracting features, such as color, shape, or texture of polyps, because the earlier ML could learn only the classification of the extracted images.12 Although the “hand-crafted” algorithm showed high accuracy, the risk of missing the lesions without those extracted features or obtaining false positive results was of concern because it was designed to detect the lesions with certain features chosen by designers. In addition, actual clinical application was limited due to the difference in image quality and also slow processing time.11

Deep-learning (DL) algorithm, one of the subtype of ML introduced in the 1980s, could overcome the limitation of earlier ML by combining both the extraction and classification of image features using deep neural networks (DNN).13 The innovative method of DNN gained significant attention because of self-learning capability of DL that could automatically identify polyp and non-polyp features from the huge dataset, instead of capturing specific features of a polyp using several networks. Self-extraction, the key feature, was achieved using backpropagation algorithm and changing the internal parameters of each neural network layer.14

Among the variable classes of DNN for image and video applications, convolutional neural network (CNN), the most popular method, can carry out layers of convolutions and can completely connect layers to unite all features in the final outcome.15,16 Provided with sufficient annotated data, CNNs can be trained to describe, in detail, what they see and discriminate polyps from non-polypoid lesions.11 Currently, AI can be trained with enough input data, thanks to the easy accessibility of big data, aiding rapid progress in the research and application of AI in colonoscopic polyp detection and characterization.17

STUDIES RELATED TO APPLICATION OF AI IN COLONOSCOPY

1. AI for detection of colorectal polyp

Since the initial study regarding computer-aided detection based on “hand-crafted” data,18 the performance of AI has improved, especially with the introduction of DL, for colorectal neoplasm detection.19,20 Table 1 shows the summary of the clinical studies of AI for the detection of colorectal polyps.18-26

Urban et al. 20 reported the first real-time application. They initially pretrained AI using ImageNet and then trained the deep CNNs. The algorithm was tested with multiple sets of colonoscopic images and 11 challenging videos sets. The result was very promising with 97% sensitivity, 95% specificity, and 96% overall accuracy. For the practical utilization in real-world, the CNN-assisted video was reviewed by experts. During the index colonoscopy, 28 polyps were noted in nine standard colonoscopic videos. Experts could identify 36 polyps without and 45 polyps with CNN assistance. The additional 17 polyps identified with CNN assistance were not larger than 10 mm in size. Further, the algorithm was faster than the real-time analysis by endoscopists (10 ms/frame vs 33–40 ms/frame). Meanwhile, Yu et al. 24 developed a novel three-dimensional CNN algorithm that could learn more representative spatiotemporal features and improve performance of automated polyp detection.

Wang et al. 21 developed a CNN system that processed data in real-time with a 77 millisecond delay on monitor, in which, a blue box appeared around the area of the polyp upon its detection, along with an alarm. They randomized the colonoscopy of about 1,058 patients into those using this system and the standard one and proved an increased ADR in AI (29.1% vs 20.3%, respectively) with minimal delay in examining time. However, the robust detection rate was limited to polyps smaller than 10 mm in size, and 43.6% of the polyps were hyperplastic polyps with low malignant potential.27,28 The most recent meta-analysis of three randomized controlled trial of AI-assisted colonoscopy reported 32.9% of ADR (20.8% in standard colonoscopy, risk ratio [RR]=1.58, p<0.001) and 43.0% of polyp detection rate (27.8% in standard colonoscopy, RR=1.55, p<0.001).29 AI utility in screening colonoscopy to improve ADR looks optimistic.

2. AI for characterization of colorectal neoplasm

Accurate histologic diagnosis of the colorectal polyp before resection is desired because endoscopic resection and pathologic evaluation of lesions with very low risk of malignant potential may result in waste of time and cost. On the other hand, incomplete resection of lesions with high risk of malignancy should be avoided. Therefore, various advanced endoscopic techniques with image enhancement for optical diagnosis have been introduced, and various studies concerning the application of AI to these endoscopic systems have also been steadily reported to date (Table 2).

Narrow band imaging (NBI; Olympus Corp., Tokyo, Japan), an image-enhanced endoscopy used for the observation of microstructure or capillaries of colorectal neoplasm, could discriminate each polyp well, based on the pit and vascular pattern.30 Tischendorf et al. 31 and Gross et al. 32 investigated AI application to NBI (excluding its performance enhancement) for the characterization of colon polyps. They both extracted nine vessel features from the NBI images in a similar manner and sorted them into neoplastic and non-neoplastic lesions using a support vector machine (SVM), a discriminated ML model.15 The diagnostic accuracies of the two studies were 85.3% and 93.1%, respectively. In addition, Gross et al. 32 proved superior accuracy of AI-assisted diagnosis compared to that of nonexperts in differentiating colorectal polyps smaller than 10 mm in size, suggesting that AI could be a good support for the beginners of endoscopy.

Another Japanese group developed a real-time image recognition system that could achieve 93.2% accuracy in real-time prediction of diminutive polyp pathology.33 In addition, the follow-up recommendation based on the prediction by that model showed 92.7% consistency with Preservation and Incorporation of Valuable Endoscopic Innovations (PIVI)-2 criteria of American Society for Gastrointestinal Endoscopy for “resect-and-discard” strategy.34 Resect and discard strategy can bring a substantial economic benefit.

Two groups carried out retrospective studies based on DL. Byrne et al. 35 developed CNN model using NBI video frames. Although the model could not make adequate credence of histology prediction in 15% of polyps, it could differentiate diminutive adenomas from hyperplastic polyps with 94% accuracy. The sensitivity and specificity for detecting adenoma were 98% and 83%, respectively. Chen et al. 36 also developed a similar model, a DNN-computer aided diagnosis (CAD), with 2,157 images for identification of about 284 neoplastic or hyperplastic polyps smaller than 5 mm in size, with 89.6% positive predictive value (PPV) and 91.5% negative predictive value (NPV). Consequently, among 117 tubular adenomas, DNN-CAD could diagnose high grade dysplasia with 100% sensitivity and 94% specificity. The performance of DNN-CAD, when compared to that of four endoscopists with less than 1 year of experience, was superior with a shorter procedure time and perfect intra-observer agreement (kappa score of 1). The result of the above-mentioned two studies could satisfy the PIVI-2 threshold (90% NPV for adenoma detection) for “leave-in-place” strategy for diminutive hyperplastic polyps.34

Although there’s a lack of recent further studies, CAD of pit pattern by magnifying chromoendoscopy was done by quantitative analysis of pit structure or texture analysis of endoscopic images.37,38 Takemura et al. 39 reported 98.5% diagnostic accuracy after automatically evaluating the area, perimeter, major/minor fit ellipse and circularity of the pit using software.

Recently, novel introduction of in vivo contact microscopic imaging modalities, such as endocytoscopy (H290ECI; Olympus Corp.) and confocal laser endomicroscopy (Cellvizio; Mauna Kea Technologies Inc, Paris, France), enabled real-time diagnosis of cellular images.40,41 Because both endocytoscopy and endomicroscopy could enhance image analysis with focused fixed-size images, they were ideal to be used in combination with the AI system. They magnified the image with 500- or 1,000-fold power, respectively, during colonoscopy and showed diagnostic accuracy comparable to that of pathologists. The first application of AI in endocytoscopy was assessed by quantitative analysis of six nuclear features, and the accuracy for the detection of neoplastic change was 89.2%.42 Takeda et al. 43 trained the computer-aided ultrahigh (approximately ×400) magnification endoscopy system for the diagnosis of invasive CRC with 5,543 endocytoscopic images. This system, when assessed using 200 endocytoscopic test images, could discriminate invasive cancers with 89.4% sensitivity, 98.9% specificity, and 94.1% accuracy. Mori et al. 44 assessed the efficacy of an endocytoscopy-based CAD with 466 cases of diminutive polyps, and the NPV for diminutive rectosigmoid adenomas was 93.7%. They also proved that polyp diagnosis with AI, an add-on analysis, could reduce the cost of annual reimbursement for colonoscopy by 18.9%, by leaving 145 rectosigmoid diminutive polyps based on the AI support.45 However, AI endocytoscopy had a limitation–it needed pre-staining with crystal violet and methylene blue before the extraction of images. Misawa et al. 46 upgraded the system by combining endocytoscopy with NBI, eliminating the pre-staining step and resulting in 90.0% overall accuracy and 84.5% sensitivity within 0.3 seconds.

AI application with confocal endomicroscopy was assessed in several studies, in experimental setting, with promising accuracy.47 50 AI in other advanced endoscopies, including laser-induced fluorescence spectroscopy and autofluorescence endoscopy, was also evaluated retrospectively or prospectively.51-57 We can expect that the use of AI in these advanced endoscopies for optical diagnosis could aid the real-time decision making of endoscopists.

3. AI for combination of detection and characterization of colorectal polyps

For an endoscopist, both polyp detection and characterization are essential in clinical practice. An ideal scenario would be an AI-assisted immediate detection and characterization of the colorectal polyp. A Japanese group developed novel technologies that included two algorithm systems–one, based on DL algorithm, for the detection of polyps in white light images, and the other, for the prediction of pathology by endocytoscopic images generated by a photograph.58 According to the most recent study using CNN, AI system (Single Shot Multibox Detector) could detect 1,246 polyps with 92% sensitivity, 86% PPV, and 83% accuracy in polyp classification.59 Although more studies are needed, an ideal colonoscopy for the detection and characterization of colorectal neoplasm seems to be achieved with AI assistance.

4. Prediction of prognosis

Prior to DL, SVM was a highly efficient computational tool that classified and regressed best by optimizing a hyperplane with largest functional margin.15 Ichimasa et al. 60 evaluated the predictive factors for lymph node metastasis from endoscopically resected T1 CRC using SVM. Their result showed better sensitivity (100%), specificity (66%), and accuracy (69%) of the AI model than most of the current guidelines. In addition, this model could reduce the unnecessary additional colectomy after endoscopic resection of T1 CRC compared to the current guidelines that lead to misdiagnosis.

UNMET NEEDS AND FUTURE PERSPECTIVES OF AI FOR COLORECTAL NEOPLASM

Although AI-assisted detection and diagnosis of colorectal neoplasm are promising, most of the studies are retrospective and covered lesions which might have been selected with bias. Well-designed prospective studies that present more reliable data compared to the previous retrospective studies are needed. Most of the studies regarding efficacy of AI deal with polypoid lesions. However, for practical utilization, the efficacy of AI needs to be consistent irrespective of the shape of the polyps. Therefore, more studies with all types of polyps, including polypoid, depressed, or flat type, are needed because these non-polypoid type lesions are often more aggressive.61

In addition, as AI is trained with high-quality images, the system has to overcome the blurry vision, inadequate preparation status, and variable unpredictable hurdles observed in actual practice. There is a need for real-time application of CAD and randomized controlled comparative study between usage and avoidance of AI.

Previous studies, conducted in a variable design, resulted in different primary outcomes. Because AI development needed both engineers, who dealt with the software, and clinicians, who contributed to the clinical use, the outcomes and study design could be different depending on the study conductors.11 Communication and collaboration between these different groups are also needed.

To apply AI in real clinical practice, a regulatory approval of AI-based decision making, the rules of which vary for each country, is an essential step. For that, we need to prove the minimal risk of treatment failure due to the misdiagnosis by AI.22,62 So, we need the evaluation of risk stratification for AI system through well-designed randomized clinical trials.

CONCLUSION

Because colonoscopy cannot completely prevent CRC, AI application in the field of colorectal neoplasm could be one of the promising options for enhancing the efficiency of colonosocpy. Owing to easy accessibility of big data and computer science, the AI technologies for the detection and classification of colorectal polyps have developed rapidly, with studies supporting the advantage of AI use in colonoscopy. However, obstacles, such as insufficient evidence of practical clinical usefulness, lack of consensus on standardized utilization, and need for regulatory approval, exist. For the ideal implementation of AI in actual clinical practice, comprehensive understanding of the strengths and weaknesses of the technology, qualified real-time studies, and accumulation of experience are warranted.

CONFLICTS OF INTEREST


E.Y.K. is an editorial board member of the journal but did not involve in the peer reviewer selection, evaluation, or decision process of this article. No other potential conflicts of interest relevant to this article were reported.

Tables

Clinical Studies of Artificial Intelligence for the Detection of Colorectal Polyps

Author (year) Study design Algorithm type Dataset Processing time Results
Wang et al.
(2019)
21
Randomized
controlled study
Convolutional neural network 5,545 Images 25 fps with 77 ms latency 9% Increase of ADR
Klare et al.
(2019)
22
Prospective
In vivo
Convolutional neural network 55 Live colonoscopies 50 ms latency Sensitivity 75%/polyp
ADR 29% (31% in endoscopist)
Urban et al.
(2018)
20
Retrospective
Ex vivo
Convolutional neural network Image dataset: 8,641 polyps
Video: 20 colonoscopies
10 ms/frame
(real-time)
Image dataset: accuracy 96.4%
AUROC 0.991
Misawa et al.
(2018)19
Retrospective
Ex vivo
Convolutional neural network 135 Video clips No description Sensitivity 90%
Specificity 63.3%
Accuracy 76.5%
Zhang et al.
(2017)
23
Retrospective
Ex vivo
Convolutional neural network 150 Random+30 NBI images No description Sensitivity 98%
PPV 99%
AUROC 1.00
Yu et al.
(2017)
24
Retrospective
Ex vivo
Convolutional neural network ASU-Mayo 20 videos 1.23 s/frame Sensitivity 7%
PPV 88%
Angermann et al. (2017)
25
Retrospective
Ex vivo
Hand-crafted No description 20–185 ms
0.3-1.8 s delay
Sensitivity 100%/polyp
PPV50%
Tajbakhsh et al. (2015)
26
Retrospective
Ex vivo
Hand-crafted No description 2.6 s/frame Sensitivity 48% on proprietary database
Sensitivity 88% in CVC-colon DB
Karkanis et al. (2003)
18
Retrospective
Ex vivo
Hand-crafted 180 Still images 1.5 m/video Sensitivity 94%
Specificity 99%

ADR, adenoma detection rate; AUROC, area under the receiver operating characteristics; NBI, narrow band imaging; PPV, positive predictive value; ASU, Arizona State University; CVC, computer vision center; DB, data base.

Clinical Studies of Artificial Intelligence for Characterization of Colorectal Polyps

Author (year) Study design Classification target and base Algorithm type Image modality Dataset Results
Byrne et al. (2019)
35
Retrospective Histology of diminutive polyp Convolutional neural network NBI video frames 125 Diminutive polyp videos Sensitivity 98%
Specificity 83%
Accuracy 94%
Chen et al. (2018)
36
Retrospective Neoplastic or hyperplastic polyp <5 mm Convolutional neural network Magnifying NBI 284 Diminutive polyps image Sensitivity 96.3%
Specificity 78.1%
Accuracy 90.1%
Mori et al. (2018)
44
Prospective Diagnosis of neoplastic diminutive polyp SVM Endocytoscopy with NBI and stained images 466 Diminutive polyps from
325 patients
Prediction rate 98.1%
Takeda et al. (2017)
43
Retrospective Invasive CRC SVM Endocytoscopy with NBI and stained images 200 Images Sensitivity 89.4%
Specificity 98.9%
Accuracy 94.1%
Kominami et al. (2016)
33
Prospective Histology SVM with logistic regression Magnifying NBI 118 Colorectal lesions Sensitivity 95.9%
Specificity 93.3%
Accuracy 94.9%
Misawa et al. (2016)
46
Retrospective Microvascular findings SVM Endocytoscopy with NBI 100 Images Sensitivity 84.5%
Specificity 97.6%
Accuracy 90.0%
Mori et al. (2015)
42
Retrospective Neoplastic changes in small polyps Multivariate regression analysis Endocytoscopy 176 Polyps from 152 patients Sensitivity 92%
Specificity 79.5%
Accuracy 89.2%
Takemura et al. (2012)
57
Retrospective Pit pattern SVM Magnifying NBI 371 Images Sensitivity 97.8%
Specificity 97.9%
Accuracy 97.8%
Gross et al. (2011)
32
Prospective Small colonic polyp <10 mm SVM Magnifying NBI 434 Polyps from 214 patients Sensitivity 95%
Specificity 90.3%
Accuracy 93.1%
Tischendorf et al. (2010)
31
Prospective
pilot
Vascularization features SVM Magnifying NBI 209 Polyps from 128 patients Sensitivity 90%
Specificity 70.2%
Accuracy 85.3%
Takemura et al. (2010)
39
Retrospective Pit pattern HuPAS software version 1.3 Magnifying NBI with chromoendoscopy (crystal violet) 134 Images Accuracy 98.5%

NBI, narrow band imaging; SVM, support vector machine; CRC, colorectal cancer.

Table 1 Clinical Studies of Artificial Intelligence for the Detection of Colorectal Polyps

Author (year)Study designAlgorithm typeDatasetProcessing timeResults
Wang et al.
(2019)
21
Randomized
controlled study
Convolutional neural network5,545 Images25 fps with 77 ms latency9% Increase of ADR
Klare et al.
(2019)
22
Prospective
In vivo
Convolutional neural network55 Live colonoscopies50 ms latencySensitivity 75%/polyp
ADR 29% (31% in endoscopist)
Urban et al.
(2018)
20
Retrospective
Ex vivo
Convolutional neural networkImage dataset: 8,641 polyps
Video: 20 colonoscopies
10 ms/frame
(real-time)
Image dataset: accuracy 96.4%
AUROC 0.991
Misawa et al.
(2018)19
Retrospective
Ex vivo
Convolutional neural network135 Video clipsNo descriptionSensitivity 90%
Specificity 63.3%
Accuracy 76.5%
Zhang et al.
(2017)
23
Retrospective
Ex vivo
Convolutional neural network150 Random+30 NBI imagesNo descriptionSensitivity 98%
PPV 99%
AUROC 1.00
Yu et al.
(2017)
24
Retrospective
Ex vivo
Convolutional neural networkASU-Mayo 20 videos1.23 s/frameSensitivity 7%
PPV 88%
Angermann et al. (2017)
25
Retrospective
Ex vivo
Hand-craftedNo description20–185 ms
0.3-1.8 s delay
Sensitivity 100%/polyp
PPV50%
Tajbakhsh et al. (2015)
26
Retrospective
Ex vivo
Hand-craftedNo description2.6 s/frameSensitivity 48% on proprietary database
Sensitivity 88% in CVC-colon DB
Karkanis et al. (2003)
18
Retrospective
Ex vivo
Hand-crafted180 Still images1.5 m/videoSensitivity 94%
Specificity 99%

ADR, adenoma detection rate; AUROC, area under the receiver operating characteristics; NBI, narrow band imaging; PPV, positive predictive value; ASU, Arizona State University; CVC, computer vision center; DB, data base.


Table 2 Clinical Studies of Artificial Intelligence for Characterization of Colorectal Polyps

Author (year)Study designClassification target and baseAlgorithm typeImage modalityDatasetResults
Byrne et al. (2019)
35
RetrospectiveHistology of diminutive polypConvolutional neural networkNBI video frames125 Diminutive polyp videosSensitivity 98%
Specificity 83%
Accuracy 94%
Chen et al. (2018)
36
RetrospectiveNeoplastic or hyperplastic polyp <5 mmConvolutional neural networkMagnifying NBI284 Diminutive polyps imageSensitivity 96.3%
Specificity 78.1%
Accuracy 90.1%
Mori et al. (2018)
44
ProspectiveDiagnosis of neoplastic diminutive polypSVMEndocytoscopy with NBI and stained images466 Diminutive polyps from
325 patients
Prediction rate 98.1%
Takeda et al. (2017)
43
RetrospectiveInvasive CRCSVMEndocytoscopy with NBI and stained images200 ImagesSensitivity 89.4%
Specificity 98.9%
Accuracy 94.1%
Kominami et al. (2016)
33
ProspectiveHistologySVM with logistic regressionMagnifying NBI118 Colorectal lesionsSensitivity 95.9%
Specificity 93.3%
Accuracy 94.9%
Misawa et al. (2016)
46
RetrospectiveMicrovascular findingsSVMEndocytoscopy with NBI100 ImagesSensitivity 84.5%
Specificity 97.6%
Accuracy 90.0%
Mori et al. (2015)
42
RetrospectiveNeoplastic changes in small polypsMultivariate regression analysisEndocytoscopy176 Polyps from 152 patientsSensitivity 92%
Specificity 79.5%
Accuracy 89.2%
Takemura et al. (2012)
57
RetrospectivePit patternSVMMagnifying NBI371 ImagesSensitivity 97.8%
Specificity 97.9%
Accuracy 97.8%
Gross et al. (2011)
32
ProspectiveSmall colonic polyp <10 mmSVMMagnifying NBI434 Polyps from 214 patientsSensitivity 95%
Specificity 90.3%
Accuracy 93.1%
Tischendorf et al. (2010)
31
Prospective
pilot
Vascularization featuresSVMMagnifying NBI209 Polyps from 128 patientsSensitivity 90%
Specificity 70.2%
Accuracy 85.3%
Takemura et al. (2010)
39
RetrospectivePit patternHuPAS software version 1.3Magnifying NBI with chromoendoscopy (crystal violet)134 ImagesAccuracy 98.5%

NBI, narrow band imaging; SVM, support vector machine; CRC, colorectal cancer.


References

  1. American Cancer Society. Cancer Facts & Figures 2019. Atlanta: American Cancer Society, 2019.
  2. Winawer SJ, Zauber AG, Ho MN, et al. Prevention of colorectal cancer by colonoscopic polypectomy. The National Polyp Study Workgroup. N Engl J Med 1993;329:1977-1981.
    Pubmed CrossRef
  3. Nishihara R, Wu K, Lochhead P, et al. Long-term colorectal-cancer incidence and mortality after lower endoscopy. N Engl J Med 2013;369:1095-1105.
    Pubmed KoreaMed CrossRef
  4. Rex DK, Schoenfeld PS, Cohen J, et al. Quality indicators for colonoscopy. Gastrointest Endosc 2015;81:31-53.
    Pubmed CrossRef
  5. Kaminski MF, Regula J, Kraszewska E, et al. Quality indicators for colonoscopy and the risk of interval cancer. N Engl J Med 2010;362:1795-1803.
    Pubmed CrossRef
  6. Castaneda D, Popov VB, Verheyen E, Wander P, Gross SA. New technologies improve adenoma detection rate, adenoma miss rate, and polyp detection rate: a systematic review and meta-analysis. Gastrointest Endosc 2018;88:209-222.
    Pubmed CrossRef
  7. Liedlgruber M, Uhl A. Computer-aided decision support systems for endoscopy in the gastrointestinal tract: a review. IEEE Rev Biomed Eng 2011;4:73-88.
    Pubmed CrossRef
  8. Kudo SE, Mori Y, Misawa M, et al. Artificial intelligence and colonoscopy: current status and future perspectives. Dig Endosc 2019;31:363-371.
    Pubmed CrossRef
  9. Wikipedia. Artificial intelligence [Internet]. San Francisco: Wikipedia Foundation, Inc.; c2020 [cited 2020 Mar 30]. Available from: https://en.wikipedia.org/wiki/Artificial_intelligence.
  10. Russell S, Norvig P. Artificial intelligence: a modern approach. 3rd ed. Harlow: Pearson Education Limited, 2009.
  11. Hoerter N, Gross SA, Liang PS. Artificial intelligence and polyp detection. Curr Treat Options Gastroenterol 2020;18:120-136.
    Pubmed KoreaMed CrossRef
  12. Ahmad OF, Soares AS, Mazomenos E, et al. Artificial intelligence and computer-aided diagnosis in colonoscopy: current evidence and future directions. Lancet Gastroenterol Hepatol 2019;4:71-80.
    Pubmed CrossRef
  13. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521:436-444.
    Pubmed CrossRef
  14. Takiyama H, Ozawa T, Ishihara S, et al. Automatic anatomical classification of esophagogastroduodenoscopy images using deep convolutional neural networks. Sci Rep 2018;8:7497.
    Pubmed KoreaMed CrossRef
  15. Yang YJ, Bang CS. Application of artificial intelligence in gastroenterology. World J Gastroenterol 2019;25:1666-1683.
    Pubmed KoreaMed CrossRef
  16. Yoon HJ, Kim JH. Lesion-based convolutional neural network in diagnosis of early gastric cancer. Clin Endosc 2020;53:127-131.
    Pubmed KoreaMed CrossRef
  17. Choi J, Shin K, Jung J, et al. Convolutional neural network technology in endoscopic imaging: artificial intelligence for endoscopy. Clin Endosc 2020;53:117-126.
    Pubmed KoreaMed CrossRef
  18. Karkanis SA, Iakovidis DK, Maroulis DE, Karras DA, Tzivras M. Computer-aided tumor detection in endoscopic video using color wavelet features. IEEE Trans Inf Technol Biomed 2003;7:141-152.
    Pubmed CrossRef
  19. Misawa M, Kudo SE, Mori Y, et al. Artificial intelligence-assisted polyp detection for colonoscopy: initial experience. Gastroenterology 2018;154:2027-2029.
    Pubmed CrossRef
  20. Urban G, Tripathi P, Alkayali T, et al. Deep learning localizes and identifies polyps in real time with 96% accuracy in screening colonoscopy. Gastroenterology 2018;155:1069-1078.
    Pubmed KoreaMed CrossRef
  21. Wang P, Berzin TM, Glissen Brown JR, et al. Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study. Gut 2019;68:1813-1819.
    Pubmed KoreaMed CrossRef
  22. Klare P, Sander C, Prinzen M, et al. Automated polyp detection in the colorectum: a prospective study (with videos). Gastrointest Endosc 2019;89:576-582.
    Pubmed CrossRef
  23. Zhang R, Zheng Y, Mak TW, et al. Automatic detection and classification of colorectal polyps by transferring low-level CNN features from nonmedical domain. IEEE J Biomed Health Inform 2017;21:41-47.
    Pubmed CrossRef
  24. Yu L, Chen H, Dou Q, Qin J, Heng PA. Integrating online and offline three-dimensional deep learning for automated polyp detection in colonoscopy videos. IEEE J Biomed Health Inform 2017;21:65-75.
    Pubmed CrossRef
  25. Angermann Q, Bernal J, Sánchez-Montes C, et al. Towards real-time polyp detection in colonoscopy videos: adapting still frame-based methodologies for video sequences analysis. In: Cardoso MJ, Arbel T, Luo X, eds. Computer assisted and robotic endoscopy and clinical image-based procedures. Cham: Springer, 2017:29-41.
    CrossRef
  26. Tajbakhsh N, Gurudu SR, Liang J. Automated polyp detection in colonoscopy videos using shape and context information. IEEE Trans Med Imaging 2016;35:630-644.
    Pubmed CrossRef
  27. Noshirwani KC, van Stolk RU, Rybicki LA, Beck GJ. Adenoma size and number are predictive of adenoma recurrence: implications for surveillance colonoscopy. Gastrointest Endosc 2000;51(4 Pt 1):433-437.
    Pubmed CrossRef
  28. Martínez ME, Baron JA, Lieberman DA, et al. A pooled analysis of advanced colorectal neoplasia diagnoses after colonoscopic polypectomy. Gastroenterology 2009;136:832-841.
    Pubmed KoreaMed CrossRef
  29. Aziz M, Fatima R, Dong C, Lee-Smith W, Nawras A. The impact of deep convolutional neural network-based artificial intelligence on colonoscopy outcomes: a systematic review with meta-analysis. J Gastroenterol Hepatol 2020;35:1676-1683.
    Pubmed CrossRef
  30. Tanaka S, Sano Y. Aim to unify the narrow band imaging (NBI) magnifying classification for colorectal tumors: current status in Japan from a summary of the consensus symposium in the 79th Annual Meeting of the Japan Gastroenterological Endoscopy Society. Dig Endosc 2011;23 Suppl 1:131-139.
    Pubmed CrossRef
  31. Tischendorf JJ, Gross S, Winograd R, et al. Computer-aided classification of colorectal polyps based on vascular patterns: a pilot study. Endoscopy 2010;42:203-207.
    Pubmed CrossRef
  32. Gross S, Trautwein C, Behrens A, et al. Computer-based classification of small colorectal polyps by using narrow-band imaging with optical magnification. Gastrointest Endosc 2011;74:1354-1359.
    Pubmed CrossRef
  33. Kominami Y, Yoshida S, Tanaka S, et al. Computer-aided diagnosis of colorectal polyp histology by using a real-time image recognition system and narrow-band imaging magnifying colonoscopy. Gastrointest Endosc 2016;83:643-649.
    Pubmed CrossRef
  34. Rex DK, Kahi C, O’Brien M, et al. The American Society for Gastrointestinal Endoscopy PIVI (Preservation and Incorporation of Valuable Endoscopic Innovations) on real-time endoscopic assessment of the histology of diminutive colorectal polyps. Gastrointest Endosc 2011;73:419-422.
    Pubmed CrossRef
  35. Byrne MF, Chapados N, Soudan F, et al. Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model. Gut 2019;68:94-100.
    Pubmed KoreaMed CrossRef
  36. Chen PJ, Lin MC, Lai MJ, Lin JC, Lu HH, Tseng VS. Accurate classification of diminutive colorectal polyps using computer-aided analysis. Gastroenterology 2018;154:568-575.
    Pubmed CrossRef
  37. Kudo S, Rubio CA, Teixeira CR, Kashida H, Kogure E. Pit pattern in colorectal neoplasia: endoscopic magnifying view. Endoscopy 2001;33:367-373.
    Pubmed CrossRef
  38. Kudo SE, Mori Y, Wakamura K, et al. Endocytoscopy can provide additional diagnostic ability to magnifying chromoendoscopy for colorectal neoplasms. J Gastroenterol Hepatol 2014;29:83-90.
    Pubmed CrossRef
  39. Takemura Y, Yoshida S, Tanaka S, et al. Quantitative analysis and development of a computer-aided system for identification of regular pit patterns of colorectal lesions. Gastrointest Endosc 2010;72:1047-1051.
    Pubmed CrossRef
  40. Kiesslich R, Burg J, Vieth M, et al. Confocal laser endoscopy for diagnosing intraepithelial neoplasias and colorectal cancer in vivo. Gastroenterology 2004;127:706-713.
    Pubmed CrossRef
  41. Mori Y, Kudo S, Ikehara N, et al. Comprehensive diagnostic ability of endocytoscopy compared with biopsy for colorectal neoplasms: a prospective randomized noninferiority trial. Endoscopy 2013;45:98-105.
    Pubmed CrossRef
  42. Mori Y, Kudo SE, Wakamura K, et al. Novel computer-aided diagnostic system for colorectal lesions by using endocytoscopy (with videos). Gastrointest Endosc 2015;81:621-629.
    Pubmed CrossRef
  43. Takeda K, Kudo SE, Mori Y, et al. Accuracy of diagnosing invasive colorectal cancer using computer-aided endocytoscopy. Endoscopy 2017;49:798-802.
    Pubmed CrossRef
  44. Mori Y, Kudo SE, Misawa M, et al. Real-time use of artificial intelligence in identification of diminutive polyps during colonoscopy: a prospective study. Ann Intern Med 2018;169:357-366.
    Pubmed CrossRef
  45. Mori Y, Kudo SE, East JE, et al. Cost savings in colonoscopy with artificial intelligence-aided polyp diagnosis: an add-on analysis of a clinical trial (with video). Gastrointest Endosc 2020;92:905-911.e1.
    Pubmed CrossRef
  46. Misawa M, Kudo SE, Mori Y, et al. Characterization of colorectal lesions using a computer-aided diagnostic system for narrow-band imaging endocytoscopy. Gastroenterology 2016;150:1531-1532.
    Pubmed CrossRef
  47. André B, Vercauteren T, Buchner AM, Krishna M, Ayache N, Wallace MB. Software for automated classification of probe-based confocal laser endomicroscopy videos of colorectal polyps. World J Gastroenterol 2012;18:5560-5569.
    Pubmed KoreaMed CrossRef
  48. Ştefănescu D, Streba C, Cârţână ET, Săftoiu A, Gruionu G, Gruionu LG. Computer aided diagnosis for confocal laser endomicroscopy in advanced colorectal adenocarcinoma. PLoS One 2016;11:e0154863.
    Pubmed KoreaMed CrossRef
  49. Tafreshi MK, Linard N, André B, Ayache N, Vercauteren T. Semi-automated query construction for content-based endomicroscopy video retrieval. Med Image Comput Comput Assist Interv 2014;17(Pt 1):89-96.
    Pubmed CrossRef
  50. Prieto SP, Lai KK, Laryea JA, Mizell JS, Muldoon TJ. Quantitative analysis of ex vivo colorectal epithelium using an automated feature extraction algorithm for microendoscopy image data. J Med Imaging (Bellingham) 2016;3:024502.
    Pubmed KoreaMed CrossRef
  51. Kuiper T, Alderlieste YA, Tytgat KM, et al. Automatic optical diagnosis of small colorectal lesions by laser-induced autofluorescence. Endoscopy 2015;47:56-62.
    Pubmed CrossRef
  52. Rath T, Tontini GE, Vieth M, Nägel A, Neurath MF, Neumann H. In vivo real-time assessment of colorectal polyp histology using an optical biopsy forceps system based on laser-induced fluorescence spectroscopy. Endoscopy 2016;48:557-562.
    Pubmed CrossRef
  53. Renkoski TE, Banerjee B, Graves LR, et al. Ratio images and ultraviolet C excitation in autofluorescence imaging of neoplasms of the human colon. J Biomed Opt 2013;18:16005.
    Pubmed KoreaMed CrossRef
  54. Arita K, Mitsuyama K, Kawano H, et al. Quantitative analysis of colorectal mucosal lesions by autofluorescence endoscopy: discrimination of carcinomas from other lesions. Oncol Rep 2011;26:43-48.
    Pubmed CrossRef
  55. Aihara H, Saito S, Inomata H, et al. Computer-aided diagnosis of neoplastic colorectal lesions using ‘real-time’ numerical color analysis during autofluorescence endoscopy. Eur J Gastroenterol Hepatol 2013;25:488-494.
    Pubmed CrossRef
  56. Inomata H, Tamai N, Aihara H, et al. Efficacy of a novel auto-fluorescence imaging system with computer-assisted color analysis for assessment of colorectal lesions. World J Gastroenterol 2013;19:7146-7153.
    Pubmed KoreaMed CrossRef
  57. Takemura Y, Yoshida S, Tanaka S, et al. Computer-aided system for predicting the histology of colorectal tumors by using narrow-band imaging magnifying colonoscopy (with video). Gastrointest Endosc 2012;75:179-185.
    Pubmed CrossRef
  58. Mori Y, Kudo SE, Misawa M, Mori K. Simultaneous detection and characterization of diminutive polyps with the use of artificial intelligence during colonoscopy. VideoGIE 2019;4:7-10.
    Pubmed KoreaMed CrossRef
  59. Ozawa T, Ishihara S, Fujishiro M, Kumagai Y, Shichijo S, Tada T. Automated endoscopic detection and classification of colorectal polyps using convolutional neural networks. Therap Adv Gastroenterol 2020;13:1756284820910659.
    Pubmed KoreaMed CrossRef
  60. Ichimasa K, Kudo SE, Mori Y, et al. Artificial intelligence may help in predicting the need for additional surgery after endoscopic resection of T1 colorectal cancer. Endoscopy 2018;50:230-240.
    Pubmed CrossRef
  61. Kudo SE, Takemura O, Ohtsuka K. Flat and depressed types of early colorectal cancers: from East to West. Gastrointest Endosc Clin N Am 2008;18:581-593.
    Pubmed CrossRef
  62. Chinzei K, Shimizu K, Mori K, et al. Regulatory science on AI-based medical devices and systems. Adv Biomed Eng 2018;7:118-123.
    CrossRef
Gut and Liver

Vol.19 No.1
January, 2025

pISSN 1976-2283
eISSN 2005-1212

qrcode
qrcode

Share this article on :

  • line

Popular Keywords

Gut and LiverQR code Download
qr-code

Editorial Office