The application of artificial intelligence in improving colonoscopic adenoma detection rate: Where are we and where are we going

WHO cancer statistics. https://www.who.int/news-room/fact-sheets/detail/cancer [accessed 26.10.21]

Stadthagen, 2013, Loss of miR-10a activates lpo and collaborates with activated Wnt signaling in inducing intestinal neoplasia in female mice, PLoS Genet, 9, 10.1371/journal.pgen.1003913

Corley, 2014, Adenoma detection rate and risk of colorectal cancer and death, N Engl J Med, 370, 2541, 10.1056/NEJMoa1309086

Kaminski, 2010, Quality indicators for colonoscopy and the risk of interval cancer, N Engl J Med, 362, 1795, 10.1056/NEJMoa0907667

Corley, 2014, Adenoma detection rate and risk of colorectal cancer and death, N Engl J Med, 370, 1298, 10.1056/NEJMoa1309086

Mahmud, 2015, Computer vision and augmented reality in gastrointestinal endoscopy, Gastroenterol Rep, 3, 179, 10.1093/gastro/gov027

Ahn, 2012, The miss rate for colorectal adenoma determined by quality-adjusted, back-to-back colonoscopies, Gut Liver, 6, 64, 10.5009/gnl.2012.6.1.64

Rogart, 2008, Fellow involvement may increase adenoma detection rates during colonoscopy, Am J Gastroenterol, 103, 2841, 10.1111/j.1572-0241.2008.02085.x

Wolfe, 2006, Why don’t we see changes?: the role of attentional bottlenecks and limited visual memory, Vis Cogn, 14, 749, 10.1080/13506280500195292

Memmert, 2010, The impact of regulatory fit on performance in an inattentional blindness paradigm, J Gen Psychol, 137, 129, 10.1080/00221301003645061

Hazewinkel, 2013, Endoscopic features of sessile serrated adenomas: validation by international experts using high-resolution white-light endoscopy and narrow-band imaging, Gastrointest Endosc, 77, 916, 10.1016/j.gie.2012.12.018

Chen, 2018, Accurate classification of diminutive colorectal polyps using computer-aided analysis, Gastroenterology, 154, 568, 10.1053/j.gastro.2017.10.010

Mori, 2019, Simultaneous detection and characterization of diminutive polyps with the use of artificial intelligence during colonoscopy, VideoGIE, 4, 7, 10.1016/j.vgie.2018.10.006

Byrne, 2019, Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model, Gut, 68, 94, 10.1136/gutjnl-2017-314547

Klare, 2019, Automated polyp detection in the colorectum: a prospective study (with videos), Gastrointest Endosc, 89, 576, 10.1016/j.gie.2018.09.042

Hinton, 2006, A fast learning algorithm for deep belief nets, Neur Comput, 18, 1527, 10.1162/neco.2006.18.7.1527

Liedlgruber, 2011, Computer-aided decision support systems for endoscopy in the gastrointestinal tract: a review, IEEE Rev Biomed Eng, 4, 73, 10.1109/RBME.2011.2175445

Mori, 2017, Computer-aided diagnosis for colonoscopy, Endoscopy, 49, 813, 10.1055/s-0043-109430

Butterly, 2014, Serrated and adenomatous polyp detection increases with longer withdrawal time: results from the New Hampshire Colonoscopy Registry, Am J Gastroenterol, 109, 417, 10.1038/ajg.2013.442

Shaukat, 2015, Longer withdrawal time is associated with a reduced incidence of interval cancer after screening colonoscopy, Gastroenterology, 149, 952, 10.1053/j.gastro.2015.06.044

Fernández-Esparrach, 2016, Exploring the clinical potential of an automatic colonic polyp detection method based on the creation of energy maps, Endoscopy, 48, 837, 10.1055/s-0042-108434

Misawa, 2018, Artificial intelligence-assisted polyp detection for colonoscopy: initial experience, Gastroenterology, 154, 2027, 10.1053/j.gastro.2018.04.003

Urban, 2018, Deep learning localizes and identifies polyps in real time with 96% accuracy in screening colonoscopy, Gastroenterology, 155, 10.1053/j.gastro.2018.06.037

Wang, 2018, Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy, Nature Biomed Eng, 2, 741, 10.1038/s41551-018-0301-3

Yamada, 2019, Development of a real-time endoscopic image diagnosis support system using deep learning technology in colonoscopy, Scient Rep, 9, 14465, 10.1038/s41598-019-50567-5

Wang, 2019, Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study, Gut, 68, 1813, 10.1136/gutjnl-2018-317500

Su, 2020, Impact of a real-time automatic quality control system on colorectal polyp and adenoma detection: a prospective randomized controlled study (with videos), Gastrointest Endosc, 91, 415, 10.1016/j.gie.2019.08.026

Liu, 2020, Study on detection rate of polyps and adenomas in artificial-intelligence-aided colonoscopy, Saudi J Gastroenterol, 26, 13, 10.4103/sjg.SJG_377_19

Wang, 2020, Lower adenoma miss rate of computer-aided detection-assisted colonoscopy vs routine white-light colonoscopy in a prospective tandem study, Gastroenterology, 159, 1252, 10.1053/j.gastro.2020.06.023

Hassan, 2021, Performance of artificial intelligence in colonoscopy for adenoma and polyp detection: a systematic review and meta-analysis, Gastrointest Endosc, 93, 77, 10.1016/j.gie.2020.06.059

Komeda, 2017, Computer-aided diagnosis based on convolutional neural network system for colorectal polyp classification: preliminary experience, Oncology, 93, 30, 10.1159/000481227

Sánchez-Montes, 2019, Computer-aided prediction of polyp histology on white light colonoscopy using surface pattern analysis, Endoscopy, 51, 261, 10.1055/a-0732-5250

Tischendorf, 2010, Computer-aided classification of colorectal polyps based on vascular patterns: a pilot study, Endoscopy, 42, 203, 10.1055/s-0029-1243861

Kominami, 2016, Computer-aided diagnosis of colorectal polyp histology by using a real-time image recognition system and narrow-band imaging magnifying colonoscopy, Gastrointest Endosc, 83, 643, 10.1016/j.gie.2015.08.004

Zhang, 2017, Automatic detection and classification of colorectal polyps by transferring low-level CNN features from nonmedical domain, IEEE J Biomed Health Inform, 21, 41, 10.1109/JBHI.2016.2635662

Kudo, 2014, Endocytoscopy can provide additional diagnostic ability to magnifying chromoendoscopy for colorectal neoplasms, J Gastroenterol Hepatol, 29, 83, 10.1111/jgh.12374

Uchiyama, 2017, Assessment of endoscopic mucosal healing of ulcerative colitis using linked colour imaging, a novel endoscopic enhancement system, J Crohn's Col, 11, 963, 10.1093/ecco-jcc/jjx026

Min, 2019, Computer-aided diagnosis of colorectal polyps using linked color imaging colonoscopy to predict histology, Scient Rep, 9, 2881, 10.1038/s41598-019-39416-7

Mori, 2013, Comprehensive diagnostic ability of endocytoscopy compared with biopsy for colorectal neoplasms: a prospective randomized noninferiority trial, Endoscopy, 45, 98, 10.1055/s-0032-1325932

Mori, 2015, Novel computer-aided diagnostic system for colorectal lesions by using endocytoscopy (with videos), Gastrointest Endosc, 81, 621, 10.1016/j.gie.2014.09.008

Mori, 2016, Impact of an automated system for endocytoscopic diagnosis of small colorectal lesions: an international web-based study, Endoscopy, 48, 1110, 10.1055/s-0042-113609

Takeda, 2017, Accuracy of diagnosing invasive colorectal cancer using computer-aided endocytoscopy, Endoscopy, 49, 798, 10.1055/s-0043-105486

Misawa, 2016, Characterization of colorectal lesions using a computer-aided diagnostic system for narrow-band imaging endocytoscopy, Gastroenterology, 150, 1531, 10.1053/j.gastro.2016.04.004

Mori, 2018, Real-time use of artificial intelligence in identification of diminutive polyps during colonoscopy: a prospective study, Ann Intern Med, 169, 357, 10.7326/M18-0249

André, 2012, Software for automated classification of probe-based confocal laser endomicroscopy videos of colorectal polyps, World J Gastroenterol, 18, 5560, 10.3748/wjg.v18.i39.5560

Rath, 2016, In vivo real-time assessment of colorectal polyp histology using an optical biopsy forceps system based on laser-induced fluorescence spectroscopy, Endoscopy, 48, 557, 10.1055/s-0042-102251

Ozawa, 2020, Automated endoscopic detection and classification of colorectal polyps using convolutional neural networks, Therapeut Adv Gastroenterol, 13, 10.1177/1756284820910659