Sarah Tham; Frederick H. Koh; Jasmine Ladlad; Koy-Min Chue; Cui-Li Lin; Eng-Kiong Teo; Fung-Joon Foo

doi:10.3393/ac.2022.00878.0125

Articles

Page Path: HOME > Ann Coloproctol > Volume 39(5); 2023 > Article

Review AI colonoscopy The imitation game: a review of the use of artificial intelligence in colonoscopy, and endoscopists’ perceptions thereof: Sarah Tham^1,*, Frederick H. Koh^2,3,*, Jasmine Ladlad^2,3, Koy-Min Chue^1,3, ³, Cui-Li Lin^3,4, Eng-Kiong Teo^3,4, Fung-Joon Foo^2,3SKH Endoscopy Centre; Annals of Coloproctology 2023;39(5):385-394.
DOI: https://doi.org/10.3393/ac.2022.00878.0125
Published online: March 10, 2023

¹Department of General Surgery, Sengkang General Hospital, SingHealth Services, Singapore

²Colorectal Service, Department of General Surgery, Sengkang General Hospital, SingHealth Services, Singapore

³SKH Endoscopy Centre, Division of Hyperacute Care, Sengkang General Hospital, SingHealth Services, Singapore

⁴Department of Gastroenterology and Hepatology, Sengkang General Hospital, SingHealth Services, Singapore

Correspondence to: Frederick H. Koh, FRCS Colorectal Service, Department of General Surgery, Sengkang General Hospital, 110 Sengkang East Way, Singapore 544886 Email: frederickkohhx@gmail.com

*Sarah Tham and Frederick H. Koh contributed equally to this study as co-first authors.

• Received: October 30, 2022 • Revised: December 22, 2022 • Accepted: January 9, 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.

prev next

5,277 Views
137 Download
3 Web of Science
3 Crossref
2 Scopus

Full Article

Download PDF

Abstract
INTRODUCTION
AI IN COLONOSCOPY
OUR CENTER’S EXPERIENCE WITH AIAIDED COLONOSCOPY
KNOWLEDGE, PERCEPTIONS, AND BEHAVIORS OF ENDOSCOPISTS TOWARDS AI-AIDED COLONOSCOPY
INCREASING THE ADOPTION OF AI IN COLONOSCOPY
FUTURE DIRECTIONS FOR AI IN COLONOSCOPY
CONCLUSION
ARTICLE INFORMATION
REFERENCES

Abstract

The development of deep learning systems in artificial intelligence (AI) has enabled advances in endoscopy, and AI-aided colonoscopy has recently been ushered into clinical practice as a clinical decision-support tool. This has enabled real-time AI-aided detection of polyps with a higher sensitivity than the average endoscopist, and evidence to support its use has been promising thus far. This review article provides a summary of currently published data relating to AI-aided colonoscopy, discusses current clinical applications, and introduces ongoing research directions. We also explore endoscopists’ perceptions and attitudes toward the use of this technology, and discuss factors influencing its uptake in clinical practice.
Keywords: Artificial intelligence; Colonic polyps; Adenoma; Colonoscopy; Biodmedical technology assessment

INTRODUCTION

“Can machines think?” These were the opening words of Alan Turing’s landmark paper “Computing Machinery and Intelligence” in 1950 [1], where he introduced the concept of using computers to simulate intelligent behavior and critical thinking [2, 3]. In that paper, he described the “Turing test,” or the “imitation game,” a simple test of a machine’s ability to exhibit intelligent behavior indistinguishable from that of a human—a machine would pass the test if a human could not reliably tell apart a machine from a human [1]. The term “artificial intelligence (AI)” was subsequently coined by John McCarthy in 1956, referring to “the science and engineering of making intelligent machines” [4, 5]. Seventy years later, we have arrived in an era in which machines can not only simulate human intelligence, but can even supersede it with increased speed, accuracy, and reproducibility [6].

AI systems are built on techniques that mimic facets of human intelligence, such as machine learning and deep learning [7]. Machine learning involves automatically identifying patterns within datasets, building algorithms from them, and “learning” to apply these predictive models to future scenarios for improved decision-making [8]. Deep learning involves the self-creation of an artificial neural network, a multilayered network in which various algorithms, like neurons of a human brain, can interconnect via hidden neural layers, to improve the overall efficiency of the network and process vast amounts of data [7, 9, 10].

The widespread application of AI to medicine has ballooned In the past 2 decades as a result of advances in information technology, big data collection and processing, and increased electronification of medical data [6]. The advent of deep learning in particular has significantly advanced the field of AI by rendering AI systems capable of analyzing complex algorithms and self-learning [2].

AI IN COLONOSCOPY

Within medicine, one of the most successful uses of AI has been in the field of computer-aided diagnostics (CAD) in colonoscopy. The use of AI in colonoscopy has far-reaching clinical benefits.

Colorectal cancer is the third most diagnosed cancer in the world, with the second highest mortality rate [11]. In Singapore, it is the most common cancer, accounting for 16.9% of all cancer diagnoses in men and 13.1% in women [12]. Colonoscopy remains the gold standard for detection of colonic polyps and colorectal cancer, with both diagnostic (biopsy) and potentially therapeutic (polyp removal) advantages [13–15]. Periodic colonoscopy assessments have been shown to play an important preventive role in decreasing the incidence of colorectal cancer by detecting precancerous adenomas at an early stage [16, 17], and thereby also reducing mortality from colorectal cancer [18, 19].

However, postcolonoscopy colorectal cancer (PCCRC) remains recognized problem, in which colorectal cancer is diagnosed after a colonoscopy in which no cancer was found [20]. A retrospective single-center study of 107 PCCRCs in England found that 73% of PCCRCs were determined to be affected by technical endoscopic factors and 27% by decision-making factors. Nineteen percent of index colonoscopies had poor bowel preparation and 85% of PCCRCs were classified as possible missed lesions [21]. A meta-analysis by Zhao et al. [22] of more than 15,000 tandem colonoscopies showed an adenoma miss rate (AMR) of 26%—in other words, about one-fourth of adenomas are missed during colonoscopy. These results show that the effectiveness of colonoscopy hinges upon quality indicators, such as the adenoma detection rate (ADR), complete resection rate, adequate bowel preparation, cecal intubation rate (i.e., completion of colonoscopy), and withdrawal time, as suggested by the European Society of Gastrointestinal Endoscopy guidelines [23]. This is where AI comes into play—to improve the quality of colonoscopies.

The use of AI in colonoscopy is dependent on deep convoluted neural networks for real-time image analysis (Fig. 1) [24]. There are 2 major CAD systems thus far: computer-aided detection (CADe) and computer-aided diagnosis (CADx).

Computer-aided detection

CADe involves real-time image analysis to detect polyps, with the aim of increasing the ADR and decreasing the AMR [7, 25]. Studies have shown the ADR to be inversely related to the risk of interval colorectal cancer and mortality [23, 26], and a US study of more than 300,000 colonoscopies reported a 3% decrease in the risk of interval colorectal cancer for every 1% increase in the ADR [27]. CADe systems have enabled the real-time AI-aided detection of adenomas with higher sensitivity than the average endoscopist [28]. Examples include Medtronic’s “GI Genius,” Pentax’s “Discovery AI,” and Fujifilm’s “CAD-EYE.”

This has been well supported by many randomized controlled trials (RCTs) comparing human to AI endoscopists in terms of ADRs [29–31]. A meta-analysis [32] of 5 RCTs [33–37] consisting of a total of 4,354 patients showed that the pooled ADR was significantly higher in the CADe group (36.6%) than in the control group (25.2%; relative risk, 1.44). Sessile serrated lesions, which are notoriously frequently missed on colonoscopy [38], were also detected at a higher rate using CADe (relative risk, 1.52) [32].

The first CADe system to be approved by the U.S. Food and Drug Administration was Medtronic’s GI Genius, which has been shown to have a 99.7% sensitivity rate [39]. A multicenter randomized trial of 685 subjects conducted by Repici et al. [37] showed a significantly higher ADR in patients who underwent GI Genius-aided colonoscopy (54.8%) than in patients who underwent colonoscopy without GI Genius assistance (40.4%; relative risk, 1.30). A more recent study by Wallace et al. [40] showed a nearly 50% reduction in the AMR with GI Genius (15.5%) compared to unassisted colonoscopy (32.4%).

Computer-aided diagnosis

CADx involves characterizing polyps based on morphological parameters, such as surface, vascular patterns, shape, size, and location, to generate probability scores for malignancy or nonmalignancy [41]. This helps to improve the accuracy of optical biopsies, which refer to the in vivo prediction of polyp histology before resection and formal histological analysis [42]. Most of these systems use image-enhanced endoscopy techniques such as narrow-band imaging and blue laser imaging (BLI) to enhance the accuracy of predictions.

The goal is to reduce unnecessary polypectomies for nonneoplastic lesions, such that colonoscopy becomes more cost-effective and timesaving, and potentially avoiding the rare but significant complications that come with polypectomy, such as bleeding, infection and perforation of the bowel [43]. These methods can potentially allow the implementation of a “resect and discard” [44] or “detect and leave” [45] strategy for diminutive polyps (5 mm or smaller), using technology that provides a negative predictive value (NPV) of more than 90% for adenomatous histology, according to the thresholds set by the American Society for Gastrointestinal Endoscopy (ASGE) Preservation and Incorporation of Valuable Endoscopic Innovations (PIVI) recommendations [46].

There is currently intense interest in the area of developing realtime CADx systems. Fujifilm’s CAD-EYE is the first AI system to include both CADe and CADx (using BLI) on the same platform. CAD-EYE’s CADx module obtained an accuracy of 93.2% with white-light endoscopy and 94.9% with BLI [47]. These results exceed the ASGE PIVI thresholds of 90% NPV, and the system has been approved for clinical use in the European Union. Medtronic’s GI Genius has also included a CADx module in its newest iteration, but it has so far only managed to achieve 85% accuracy with white-light endoscopy in the recent study conducted by Biffi et al. [48].

Besides distinguishing neoplastic from nonneoplastic lesions, CADx has been explored as a way to diagnose the depth of cancer invasion. Tamai et al. [49] developed a CADx system that is able to distinguish colorectal lesions with deep submucosal invasion (T1b cancer) with 83.9% sensitivity and 82.6% specificity, achieving a diagnostic accuracy greater than that of clinicians (reported to be less than 80% [20]). This might prove to be very useful in clinical practice if it can potentially advise on the need for advanced resection methods such as endoscopic submucosal dissection and surgery [25].

Computer-aided quality control

Adenoma detection relies upon 2 main factors: the identification of mucosal abnormalities and adequate colonic mucosal exposure [50]. While CADe and CADx target the former, the use of AI has also been explored for the latter. AI has been used to automate and enhance quality control in colonoscopy, by monitoring technical and mechanical factors of colonoscopy, such as withdrawal time and the adequacy of bowel preparation.

For example, the ENDOANGEL system, developed by Renmin Hospital of Wuhan University in China, is a real-time quality improvement system that provides automated monitoring of the withdrawal time, withdrawal speed, and adequacy of mucosal exposure, and relays this information to the endoscopist in real time. It has been shown to result in a significantly longer mean withdrawal time (6.38 minutes vs. 4.76 minutes) and significantly higher ADR (16% vs. 8%) than unassisted colonoscopy [51].

OUR CENTER’S EXPERIENCE WITH AIAIDED COLONOSCOPY

Commercial AI-aided colonoscopy systems have only been introduced to Singapore fairly recently, since 2021. Their use has mostly been limited to product trials, and only Medtronic’s GI Genius CADe system has been formally evaluated. In 2021, our center conducted a single-institution cohort study of 24 consultant-grade endoscopists [52], of whom 18 performed 5 or more GI Genius-aided colonoscopies over a period of 3 months.

We examined the effects of GI Genius on ADR on both a collective and individual level. Collectively, the median ADR of 30.4% with GI Genius was higher than the baseline polypectomy rate of 24.3% (P = 0.02). Individually, 13 out of 18 endoscopists with 5 or more GI Genius-aided colonoscopies achieved a higher ADR rate with GI Genius, with 2 experiencing significant improvement in the ADR (39% and 40%). The median improvement was 8.5% (interquartile range, –2.8% to 17.8%), and 14 of all 250 polypectomies (5.6%) performed were also found to be sessile serrated lesions on histology, which was higher than the expected rate of 2% to 3% [53].

These results serve to highlight the benefits of CADe in helping even experienced endoscopists detect more adenomatous lesions, including those that are notoriously difficult to detect with the naked eye (Fig. 2). These results also concur with those achieved by multiple existing RCTs on CADe, as mentioned previously [32].

KNOWLEDGE, PERCEPTIONS, AND BEHAVIORS OF ENDOSCOPISTS TOWARDS AI-AIDED COLONOSCOPY

Physician sentiment is often a significant determinant of how quickly technologies are deployed in a clinical setting [54]. Despite evidence proving the benefit of AI-aided colonoscopy systems, not all endoscopists seem to welcome the advent of such systems.

An online survey conducted amongst 124 gastroenterologists in the United States from 2018 to 2019 [55] showed that while most indicated interest in the application of AI to colonoscopy (86.0%) and felt that CADe would improve their endoscopic performance (84.7%), there were significant concerns about cost (75.2%), operator dependence or “deskilling” due to over-reliance on AI (62.8%), and increased procedural time (60.3%). In contrast to the support for CADe, only 57.2% of respondents felt comfortable using CADx to support a “diagnose and leave” strategy for hyperplastic polyps, which may indicate that while endoscopists may be more welcoming towards the use of AI as an adjunct for diagnosis, there is still a significant proportion who are skeptical toward relying on AI solely to make decisions on intervention. Interestingly, it was shown that postfellowship experience of less than 15 years was the most important factor in determining whether physicians were likely to believe that CADe would lead to more removed polyps (odds ratio, 5.09; P=0.01), which serves to highlight how “expert” endoscopists may find AI less beneficial than “novice” endoscopists in elevating their endoscopic practice.

While the above survey was a prospective one undertaken prior to the use of actual AI in endoscopy, our center conducted a retrospective survey of endoscopists who had already experienced using AI-aided colonoscopy. We also sought to determine whether one’s existing experience with AI influenced the uptake of AI-aided colonoscopy. Using the same sample of 24 consultant-grade endoscopists from the study by Koh et al. [52], our center conducted a survey on the knowledge of AI, perceptions of AI in medicine, and behaviors regarding use of AI-aided colonoscopy, 2 months after the implementation of Medtronic’s GI Genius in colonoscopy, with a response rate of 66.7% (16 of 24) [56]. The parts of our survey pertaining to knowledge and perceptions of AI were modeled after the survey administered by Mehta et al. [57] to investigate knowledge and perceptions of AI amongst medical students in Ontario.

Knowledge of AI varied amongst endoscopists. Most (100%) understood common terms like “artificial intelligence” and “machine learning,” but only 9 (56.3%) understood more in-depth terms like “neural network” and “deep learning.”

Regarding perceptions of AI in medicine, most endoscopists were optimistic about AI’s capabilities in performing objective administrative (81.3% to 93.8%) and clinical tasks (62.5% to 93.8%). However, most (93.8%) were reserved about AI providing personalized, empathetic care. A minority (18.8%) of endoscopists perceived that AI would reduce the number of jobs available to them.

Regarding behaviors involving the use of AI-aided colonoscopy, only 11 endoscopists (68.8%) agreed or strongly agreed that GI Genius should be used as an adjunct in colonoscopy. Analyzing the 5 endoscopists (31.3%) who disagreed or were ambivalent about its use, there was no significant correlation with their knowledge or perceptions of AI, but a significant number did not enjoy using the program (P=0.01) and did not think it improved the quality of colonoscopy (P=0.03). We thus concluded that the acceptance of AI-aided colonoscopy systems is largely related to the endoscopist’s experience with using the program, rather than general knowledge or perceptions towards AI. The uptake of such systems will thus rely greatly on how the device is delivered to the end user.

INCREASING THE ADOPTION OF AI IN COLONOSCOPY

There is no doubt that AI in colonoscopy is a rapidly developing field, and it will likely find its way into mainstream colonoscopy practice and guidelines in the future. It is our opinion that AI should be embraced, having already proven its clinical benefits, and with the potential to do so much more. Time will tell if AI will help usher in a new era of enhanced colonoscopy surveillance and significant reductions in colorectal cancer mortality, which would indeed be practice-changing.

The benefits of AI in colonoscopy could also extend beyond the clinical, as a US modeling study [58] estimated that the addition of AI support to guideline-based screening for 60% of eligible US adults would cost USD 250 million per year, but could prevent approximately 7,000 colorectal cancer cases and 2,000 deaths every year. This would translate into net cost savings of more than USD 300 million per year, after accounting for the costs of missed cancers. These savings could make AI-aided colonoscopy attractive from a longer-term health economics standpoint, and similar data would be valuable in obtaining the necessary buy-in from policymakers in supporting AI technologies in colonoscopy.

While the incidence rates of colorectal cancer have traditionally been highest in developed countries, these rates have been notably increasing in developing countries [59], as they undergo economic growth, with increased adoption of a “western” lifestyle and dietary habits characterized by higher meat, fat, and total caloric intake, along with increased life expectancy and population growth [60]. However, this has rarely been accompanied by the implementation of appropriate colonoscopy screening programs [61], due to issues pertaining to as scarcity of resources and governance. Thus, the increased incidence of colorectal cancer in developing countries has also been paralleled by increasing mortality rates [60, 62] and represents a significant health burden. The implementation of AI technologies in colonoscopy, which is often first introduced in developed countries, hence presents issues with equity and access. Efforts to bridge these gaps are underway, including programs such as the Medtronic Health Equity Assistance Programme, which has delivered GI Genius modules to 62 facilities performing colonoscopies in less-developed communities [43].

Enabling AI modules to be compatible with various endoscopy stacks would certainly also help to increase uptake. Medtronic takes the lead in this—the GI Genius Intelligent Endoscopy Module is “brand agnostic” and compatible with most existing endoscopy stacks from all companies in use across the world [20, 52].

On an individual level, building on the conclusions of our research on endoscopists’ perceptions of AI in colonoscopy [56], it would be important for companies to focus on optimizing the user experience of their AI-aided colonoscopy products. There will be value in undertaking research pertaining to user experience and using implementational frameworks to improve uptake of AI-aided colonoscopy systems. Implementational frameworks, such as the integrated-PARIHS (integrated-Promoting Action on Research Implementation in Health Services) framework, focus on facilitation, whereby external facilitators train internal facilitators to become local experts) [63]. Similarly, priority could be given to providing hands-on sessions for endoscopists to build familiarity with the system, which is likely to improve their eventual acceptance and uptake of it. A summary of the measures that may increase institutional AI uptake and implementation for colonoscopies is depicted in Fig. 3.

FUTURE DIRECTIONS FOR AI IN COLONOSCOPY

There is still much room for improvement, especially in the field of CADx. Besides Fujifilm’s CAD-EYE system, there has not been another commercial real-time colonoscopy system with both CADe and CADx functions that has been able to achieve the ASGE PIVI threshold of an NPV greater than 90% for resecting and discarding diminutive polyps without a pathologic assessment [20].

Furthermore, current CADx systems are only able to binarily distinguish “adenoma” from “nonadenoma”, but not able to perform more granular and informative classification such as “hyperplastic,” “sessile serrated,” “carcinoma,” and so on [43]. Current systems are also not able to further stratify lesions in terms of severity of dysplasia and depth of invasion. These limitations have several implications for practice. For instance, sessile serrated lesions are considered preneoplastic, whilst hyperplastic polyps are considered nonneoplastic; however, both are currently grouped together by current CADx under the umbrella term “nonadenoma” even though their management is vastly different. In truth, it is difficult to differentiate sessile serrated lesions from hyperplastic polyps, as they have similar surface structures, and current CADx systems will require more training or the use of alternative image-enhancing methods to differentiate these lesions with greater accuracy [20]. Other areas where CADx can potentially prove more beneficial are in the detection of submucosal tumors (in which the overlying mucosa may resemble normal mucosa), in differentiating adenoma from T1 cancer, and T1a from T1b cancer (which will have implications for the treatment strategy—namely, whether to perform endoscopic mucosal resection, endoscopic submucosal dissection, or surgery) [20], and in evaluating the adequacy of large polyp resection to ensure clear margins [7].

Moving forward, another key area to explore would be the use of AI in transforming endoscopy education. Colonoscopy is known to be a challenging procedure with a steep learning curve, and it is demanding of both cognitive and technical abilities [64]. Mastering colonoscopy necessitates accurate identification of colonic polyps and meticulous mucosal exposure, all of which require hands-on experience and time [52, 65]. Studies have reported a minimum number of 100 to 200 colonoscopy procedures to reach technical competence [66–68], with an average training period of 4 years in the United Kingdom [69]. Thus, there is bound to be great variation amongst endoscopists in terms of skill [26]. A US study reported differential ADR rates of 7.4% to 52.5% between endoscopists [27]. AI can potentially act as a levelling tool between novice and expert endoscopists, as a study by Jin et al. [70] in 2020 evaluating the efficacy of a CADx tool noted that the use of CADx in colonoscopy led to the greatest improvement in novice endoscopists (73.8% to 85.6%, P<0.05), who almost reached the accuracy of experts (89.0%, P=0.10). AI can potentially also help novice endoscopists achieve competency faster, through CADe, CADx, and real-time feedback on the quality of one’s endoscopy. Studies evaluating the learning curve of novice versus expert endoscopists with and without AI-aided colonoscopy are still underway, and it would be interesting to see how AI can help novice endoscopists mount the learning curve.

CONCLUSION

In conclusion, AI-aided colonoscopy is an expanding and exciting field of development that has shown promise in improving the quality of screening and diagnosis of colorectal cancer. Further studies are required to evaluate its real-world impact on colorectal cancer incidence and mortality rates and cost-effectiveness for implementation. The technology is still early on the adoption curve, and efforts to increase the uptake of the technology should take into account accessibility, usability, and physician sentiment.

ARTICLE INFORMATION

Conflict of interest

Frederick H. Koh serves on the Editorial Board of Annals of Coloproctology, but was not involved in the reviewing or decision process of this manuscript. No other potential conflict of interest relevant to this article was reported.

Funding

None.

Author contributions

Conceptualization: all authors; Data curation: all authors; Writing–original draft: all authors; Writing–review & editing: all authors. All authors read and approved the final manuscript.

Additional information

SKH Endoscopy Centre members are listed as follows: Fung-Joon Foo, Winson J. Tan, Sharmini S. Sivarajah, Leonard M.L. Ho, Jia-Lin Ng, Frederick H. Koh, Cheryl Chong, Darius Aw, Nathanelle Khoo, Juinn-Haur Kam, Alvin Y.H. Tan, Tousif Kabir, Choon-Chieh Tan, Baldwin P.M. Yeung, Wai-Keong Wong, Bin-Chet Toh, Lester Ong, Jasmine Ladlad, Koy-Min Chue, Faith Leong, Hui-Wen Chua, Sabrina Ngaserin, Cui-Li Lin, Eng-Kiong Teo, Yi-Kang Ng, Tze-Tong Tey, Marianne A. De-Roza, Jonathan Lum, Kalki R. Chandrasekaran, Xiaoke Li, Pei-Shi Goh, Jinliang Li, Nazeemah B. Mohd-Nor, Siok-Peng Ng.

Fig. 1.

How artificial intelligence (AI) in colonoscopy works. CADe, computer-aided detection; CADx, computer-aided diagnosis.

Fig. 2.

Benefits and drawbacks of artificial intelligence (AI) in colonoscopy (data from Hassan et al. [32]). ADR, adenoma detection rate; CADe, computer-aided detection; CADx, computer-aided diagnosis.

Fig. 3.

Increasing artificial intelligence (AI) uptake and implementation.

REFERENCES

1. Turing AM. Computing machinery and intelligence. Mind 1950;59:433–60.
2. Kaul V, Enslin S, Gross SA. History of artificial intelligence in medicine. Gastrointest Endosc 2020;92:807–12.Article PubMed
3. Greenhill AT, Edmunds BR. A primer of artificial intelligence in medicine. Tech Innov Gastrointest Endosc 2020;22:85–9.Article
4. Malik P, Pathania M, Rathaur VK. Overview of artificial intelligence in medicine. J Family Med Prim Care 2019;8:2328–31.Article PubMed PMC
5. Roberts J. Thinking machines: the search for artificial intelligence [Internet]. Distillations; 2016.Available from: https://www.sciencehistory.org/distillations/thinking-machines-the-search-for-artificial-intelligence
6. El Hajjar A, Rey JF. Artificial intelligence in gastrointestinal endoscopy: general overview. Chin Med J (Engl) 2020;133:326–34.Article PubMed PMC
7. Yoo BS, D’Souza SM, Houston K, Patel A, Lau J, Elmahdi A, et al. Artificial intelligence and colonoscopy: enhancements and improvements. Artif Intell Gastrointest Endosc 2021;2:157–67.Article
8. Deo RC. Machine learning in medicine. Circulation 2015;132:1920–30.Article PubMed PMC
9. Yang YJ, Bang CS. Application of artificial intelligence in gastroenterology. World J Gastroenterol 2019;25:1666–83.Article PubMed PMC
10. Min JK, Kwak MS, Cha JM. Overview of deep learning in gastrointestinal endoscopy. Gut Liver 2019;13:388–93.Article PubMed PMC
11. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2021;71:209–49.Article PubMed PDF
12. Health Promotion Board. Singapore cancer registry annual report 2019. National Registry of Diseases Office of Singapore; 2019.
13. Abu-Freha N, Katz LH, Kariv R, Vainer E, Laish I, Gluck N, et al. Post-polypectomy surveillance colonoscopy: comparison of the updated guidelines. United European Gastroenterol J 2021;9:681–7.Article PubMed PMC PDF
14. Bisschops R, East JE, Hassan C, Hazewinkel Y, Kamiński MF, Neumann H, et al. Advanced imaging for detection and differentiation of colorectal neoplasia: European Society of Gastrointestinal Endoscopy (ESGE) guideline: update 2019. Endoscopy 2019;51:1155–79.Article PubMed
15. Jorgensen B, Knudtson J. Stop cancer colon. Colorectal cancer screening: updated guidelines. S D Med 2015;Spec No:82–7.
16. Bretthauer M, Kaminski MF, Løberg M, Zauber AG, Regula J, Kuipers EJ, et al. Population-based colonoscopy screening for colorectal cancer: a randomized clinical trial. JAMA Intern Med 2016;176:894–902.Article PubMed PMC
17. Hassan C, Wysocki PT, Fuccio L, Seufferlein T, Dinis-Ribeiro M, Brandão C, et al. Endoscopic surveillance after surgical or endoscopic resection for colorectal cancer: European Society of Gastrointestinal Endoscopy (ESGE) and European Society of Digestive Oncology (ESDO) guideline. Endoscopy 2019;51:C1. Article PubMed
18. Writing Committee of the Cancer Intervention and Surveillance Modeling Network (CISNET) Colorectal Cancer Working Group. Technical report. Evaluating the benefits and harms of colorectal cancer screening strategies: a collaborative modeling approach. Agency for Healthcare Research and Quality (AHRQ); 2015.
19. Knudsen AB, Zauber AG, Rutter CM, Naber SK, Doria-Rose VP, Pabiniak C, et al. Estimation of benefits, burden, and harms of colorectal cancer screening strategies: modeling study for the US Preventive Services Task Force. JAMA 2016;315:2595–609.Article PubMed PMC
20. Kamitani Y, Nonaka K, Isomoto H. Current status and future perspectives of artificial intelligence in colonoscopy. J Clin Med 2022;11:2923. Article PubMed PMC
21. Anderson R, Burr NE, Valori R. Causes of post-colonoscopy colorectal cancers based on World Endoscopy Organization system of analysis. Gastroenterology 2020;158:1287–99.Article PubMed
22. Zhao S, Wang S, Pan P, Xia T, Chang X, Yang X, et al. Magnitude, risk factors, and factors associated with adenoma miss rate of tandem colonoscopy: a systematic review and meta-analysis. Gastroenterology 2019;156:1661–74.Article PubMed
23. Kaminski MF, Thomas-Gibson S, Bugajski M, Bretthauer M, Rees CJ, Dekker E, et al. Performance measures for lower gastrointestinal endoscopy: a European Society of Gastrointestinal Endoscopy (ESGE) quality improvement initiative. United European Gastroenterol J 2017;5:309–34.Article PubMed PMC PDF
24. Choi J, Shin K, Jung J, Bae HJ, Kim DH, Byeon JS, et al. Convolutional neural network technology in endoscopic imaging: artificial intelligence for endoscopy. Clin Endosc 2020;53:117–26.Article PubMed PMC PDF
25. Vinsard DG, Mori Y, Misawa M, Kudo SE, Rastogi A, Bagci U, et al. Quality assurance of computer-aided detection and diagnosis in colonoscopy. Gastrointest Endosc 2019;90:55–63.Article PubMed
26. Larsen SL, Mori Y. Artificial intelligence in colonoscopy: a review on the current status. DEN Open 2022;2:e109.Article PubMed PMC PDF
27. Corley DA, Jensen CD, Marks AR, Zhao WK, Lee JK, Doubeni CA, et al. Adenoma detection rate and risk of colorectal cancer and death. N Engl J Med 2014;370:1298–306.Article PubMed PMC
28. Lui TK, Leung WK. Is artificial intelligence the final answer to missed polyps in colonoscopy? World J Gastroenterol 2020;26:5248–55.Article PubMed PMC
29. Attardo S, Chandrasekar VT, Spadaccini M, Maselli R, Patel HK, Desai M, et al. Artificial intelligence technologies for the detection of colorectal lesions: the future is now. World J Gastroenterol 2020;26:5606–16.Article PubMed PMC
30. Ishiyama M, Kudo SE, Misawa M, Mori Y, Maeda Y, Ichimasa K, et al. Impact of the clinical use of artificial intelligence-assisted neoplasia detection for colonoscopy: a large-scale prospective, propensity score-matched study (with video). Gastrointest Endosc 2022;95:155–63.Article PubMed
31. Huang D, Shen J, Hong J, Zhang Y, Dai S, Du N, et al. Effect of artificial intelligence-aided colonoscopy for adenoma and polyp detection: a meta-analysis of randomized clinical trials. Int J Colorectal Dis 2022;37:495–506.Article PubMed PDF
32. Hassan C, Spadaccini M, Iannone A, Maselli R, Jovani M, Chandrasekar VT, et al. Performance of artificial intelligence in colonoscopy for adenoma and polyp detection: a systematic review and meta-analysis. Gastrointest Endosc 2021;93:77–85.Article PubMed
33. Wang P, Berzin TM, Glissen Brown JR, Bharadwaj S, Becq A, Xiao X, et al. Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study. Gut 2019;68:1813–9.Article PubMed
34. Wang P, Liu X, Berzin TM, Glissen Brown JR, Liu P, Zhou C, et al. Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study. Lancet Gastroenterol Hepatol 2020;5:343–51.Article PubMed
35. Liu WN, Zhang YY, Bian XQ, Wang LJ, Yang Q, Zhang XD, et al. Study on detection rate of polyps and adenomas in artificial-intelligence-aided colonoscopy. Saudi J Gastroenterol 2020;26:13–9.Article PubMed
36. Su JR, Li Z, Shao XJ, Ji CR, Ji R, Zhou RC, et al. Impact of a realtime automatic quality control system on colorectal polyp and adenoma detection: a prospective randomized controlled study (with videos). Gastrointest Endosc 2020;91:415–24.Article PubMed
37. Repici A, Badalamenti M, Maselli R, Correale L, Radaelli F, Rondonotti E, et al. Efficacy of real-time computer-aided detection of colorectal neoplasia in a randomized trial. Gastroenterology 2020;159:512–20.Article PubMed
38. Obuch JC, Pigott CM, Ahnen DJ. Sessile serrated polyps: detection, eradication, and prevention of the evil twin. Curr Treat Options Gastroenterol 2015;13:156–70.Article PubMed PMC PDF
39. Hassan C, Wallace MB, Sharma P, Maselli R, Craviotto V, Spadaccini M, et al. New artificial intelligence system: first validation study versus experienced endoscopists for colorectal polyp detection. Gut 2020;69:799–800.Article PubMed
40. Wallace MB, Sharma P, Bhandari P, East J, Antonelli G, Lorenzetti R, et al. Impact of artificial intelligence on miss rate of colorectal neoplasia. Gastroenterology 2022;163:295–304.Article PubMed
41. Vulpoi RA, Luca M, Ciobanu A, Olteanu A, Barboi OB, Drug VL. Artificial intelligence in digestive endoscopy: where are we and where are we going? Diagnostics (Basel) 2022;12:927. Article PubMed PMC
42. Li JW, Wang LM, Ang TL. Artificial intelligence-assisted colonoscopy: a narrative review of current data and clinical applications. Singapore Med J 2022;63:118–24.Article PubMed PMC
43. Diao JA, Kvedar JC. Computer copilots for endoscopic diagnosis. NPJ Digit Med 2022;5:129. Article PubMed PMC PDF
44. von Renteln D, Kaltenbach T, Rastogi A, Anderson JC, Rösch T, Soetikno R, et al. Simplifying resect and discard strategies for real-time assessment of diminutive colorectal polyps. Clin Gastroenterol Hepatol 2018;16:706–14.Article PubMed
45. Hassan C, Balsamo G, Lorenzetti R, Zullo A, Antonelli G. Artificial intelligence allows leaving-in-situ colorectal polyps. Clin Gastroenterol Hepatol 2022;20:2505–13.Article PubMed
46. Rex DK, Kahi C, O’Brien M, Levin TR, Pohl H, Rastogi A, 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;73:419–22.Article PubMed
47. Weigt J, Repici A, Antonelli G, Afifi A, Kliegis L, Correale L, et al. Performance of a new integrated computer-assisted system (CADe/CADx) for detection and characterization of colorectal neoplasia. Endoscopy 2022;54:180–4.Article PubMed
48. Biffi C, Salvagnini P, Dinh NN, Hassan C, Sharma P; GI Genius CADx Study Group, et al. A novel AI device for real-time optical characterization of colorectal polyps. NPJ Digit Med 2022;5:84. Article PubMed PMC PDF
49. Tamai N, Saito Y, Sakamoto T, Nakajima T, Matsuda T, Sumiyama K, et al. Effectiveness of computer-aided diagnosis of colorectal lesions using novel software for magnifying narrow-band imaging: a pilot study. Endosc Int Open 2017;5:E690–4.Article PubMed PMC
50. Hill A, Horswill MS, Plooy AM, Watson MO, Rowlands LN, Wallis GM, et al. Assessment of colorectal polyp recognition skill: development and validation of an objective test. Surg Endosc 2017;31:2426–36.Article PubMed PDF
51. Gong D, Wu L, Zhang J, Mu G, Shen L, Liu J, et al. Detection of colorectal adenomas with a real-time computer-aided system (ENDOANGEL): a randomised controlled study. Lancet Gastroenterol Hepatol 2020;5:352–61.Article PubMed
52. Koh FH, Ladlad J; SKH Endoscopy Centre, Lin CL, Foo FJ. Real-time artificial intelligence (AI)-aided endoscopy improves adenoma detection rates even in experienced endoscopists: a cohort study in Singapore. Surg Endosc 2023;37:165–71.Article PubMed
53. Desai M, Anderson JC, Kaminski M, Thoguluva Chandrasekar V, Fathallah J, Hassan C, et al. Sessile serrated lesion detection rates during average risk screening colonoscopy: a systematic review and meta-analysis of the published literature. Endosc Int Open 2021;9:E610–20.Article PubMed PMC
54. Li JW, Ang TL. Colonoscopy and artificial intelligence: bridging the gap or a gap needing to be bridged? Artif Intell Gastrointest Endosc 2021;2:36–49.Article
55. Wadhwa V, Alagappan M, Gonzalez A, Gupta K, Brown JR, Cohen J, et al. Physician sentiment toward artificial intelligence (AI) in colonoscopic practice: a survey of US gastroenterologists. Endosc Int Open 2020;8:E1379–84.Article PubMed PMC
56. Tham S, Koh FH, Teo EK, Lin CL, Foo FJ. Knowledge, perceptions and behaviours of endoscopists towards the use of artificial intelligence aided colonoscopy. Endoscopy 2022;54(S 01): S180–1.Article
57. Mehta N, Harish V, Bilimoria K, Morgado F, Ginsburg S, Law M, et al. Knowledge and attitudes on artificial intelligence in healthcare: a provincial survey study of medical students [version 1]. MedEdPublish 2021;10:75.
58. Areia M, Mori Y, Correale L, Repici A, Bretthauer M, Sharma P, et al. Cost-effectiveness of artificial intelligence for screening colonoscopy: a modelling study. Lancet Digit Health 2022;4:e436–44.Article PubMed
59. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2018;68:394–424.Article PubMed PDF
60. Bishehsari F, Mahdavinia M, Vacca M, Malekzadeh R, MarianiCostantini R. Epidemiological transition of colorectal cancer in developing countries: environmental factors, molecular pathways, and opportunities for prevention. World J Gastroenterol 2014;20:6055–72.Article PubMed PMC
61. Gellad ZF, Provenzale D. Colorectal cancer: national and international perspective on the burden of disease and public health impact. Gastroenterology 2010;138:2177–90.Article PubMed
62. Center MM, Jemal A, Smith RA, Ward E. Worldwide variations in colorectal cancer. CA Cancer J Clin 2009;59:366–78.Article PubMed
63. Hunter SC, Kim B, Mudge A, Hall L, Young A, McRae P, et al. Experiences of using the i-PARIHS framework: a co-designed case study of four multi-site implementation projects. BMC Health Serv Res 2020;20:573. Article PubMed PMC PDF
64. Lee SH, Park YK, Lee DJ, Kim KM. Colonoscopy procedural skills and training for new beginners. World J Gastroenterol 2014;20:16984–95.Article PubMed PMC
65. Yoon JY, Cha JM. Can we measure the learning curve of colonoscopy using polyp detection rate? Clin Endosc 2016;49:6–7.Article PubMed PMC PDF
66. Lee SH, Chung IK, Kim SJ, Kim JO, Ko BM, Hwangbo Y, et al. An adequate level of training for technical competence in screening and diagnostic colonoscopy: a prospective multicenter evaluation of the learning curve. Gastrointest Endosc 2008;67:683–9.Article PubMed
67. Chung JI, Kim N, Um MS, Kang KP, Lee D, Na JC, et al. Learning curves for colonoscopy: a prospective evaluation of gastroenterology fellows at a single center. Gut Liver 2010;4:31–5.Article PubMed PMC
68. American Society for Gastrointestinal Endoscopy. The role of colonoscopy in the management of patients with colonic polyps neoplasia: guidelines for clinical application. Gastrointest Endosc 1999;50:921–4.Article PubMed
69. Siau K, Anderson JT, Valori R, Feeney M, Hawkes ND, Johnson G, et al. Certification of UK gastrointestinal endoscopists and variations between trainee specialties: results from the JETS e-portfolio. Endosc Int Open 2019;7:E551–60.Article PubMed PMC
70. Jin EH, Lee D, Bae JH, Kang HY, Kwak MS, Seo JY, et al. Improved accuracy in optical diagnosis of colorectal polyps using convolutional neural networks with visual explanations. Gastroenterology 2020;158:2169–79.Article PubMed

Figure & Data

References

Citations

Citations to this article as recorded by

New Concept of Colonoscopy Assisted by a Microwave-Based Accessory Device: First Clinical Experience
Oswaldo Ortiz, Oriol Sendino, Silvia Rivadulla, Alejandra Garrido, Luz María Neira, Josep Sanahuja, Pilar Sesé, Marta Guardiola, Glòria Fernández-Esparrach
Cancers.2025; 17(7): 1073. CrossRef
Deep learning model for gastrointestinal polyp segmentation
Zitong Wang, Zeyi Wang, Pengyu Sun
PeerJ Computer Science.2025; 11: e2924. CrossRef
Does AI have utility in medical student surgical education? A comparative analysis of chatbots in answering standardized surgical multiple-choice questions
Natalia DaFonte, Angelo Cadiente, Catherine Implicito, Natasha Becker, Burton Surick
Global Surgical Education - Journal of the Association for Surgical Education.2025;[Epub] CrossRef

Cite this Article

Cite this Article: export Copy Download Format; Close

Download Citation

Download a citation file in RIS format that can be imported by all major citation management software, including EndNote, ProCite, RefWorks, and Reference Manager.

Format:

RIS — For EndNote, ProCite, RefWorks, and most other reference management software
BibTeX — For JabRef, BibDesk, and other BibTeX-specific software

Include:

Citation for the content below
Citation and abstract for the content below

The imitation game: a review of the use of artificial intelligence in colonoscopy, and endoscopists’ perceptions thereof

Ann Coloproctol. 2023;39(5):385-394. Published online March 10, 2023

DOI: https://doi.org/10.3393/ac.2022.00878.0125

XML Download

Figure

The imitation game: a review of the use of artificial intelligence in colonoscopy, and endoscopists’ perceptions thereof

Fig. 1. How artificial intelligence (AI) in colonoscopy works. CADe, computer-aided detection; CADx, computer-aided diagnosis.

Fig. 2. Benefits and drawbacks of artificial intelligence (AI) in colonoscopy (data from Hassan et al. [32]). ADR, adenoma detection rate; CADe, computer-aided detection; CADx, computer-aided diagnosis.

Fig. 3. Increasing artificial intelligence (AI) uptake and implementation.

Fig. 1.

Fig. 2.

Fig. 3.

The imitation game: a review of the use of artificial intelligence in colonoscopy, and endoscopists’ perceptions thereof