1)Kumar S, Thosani N, Ladabaum U, et al. Adenoma miss rates associated with a 3-minute versus 6-minute colonoscopy withdrawal time:a prospective, randomized trial. Gastrointest Endosc 85:1273-1280, 2017
2)Ladabaum U, Fioritto A, Mitani A, et al. Real-time optical biopsy of colon polyps with narrow band imaging in community practice does not yet meet key thresholds for clinical decisions. Gastroenterology 144:81-91, 2013
3)Khan S, Yong SP. A Comparison of Deep Learning and Hand Crafted Features in Medical Image Modality Classification. 3rd. International Conference on Computer and Information Sciences(ICCOINS)pp 15-17, 2016
4)Fernández-Esparrach G, Bernal J, López-Cerón M, et al. Exploring the clinical potential of an automatic colonic polyp detection method based on the creation of energy maps. Endoscopy 48:837-842, 2016
5)Tajbakhsh N, Gurudu SR, Liang J. Automatic polyp detection using global geometric constraints and local intensity variation patterns. Med Image Comput Comput Assist Interv 17:179-187, 2014
6)Misawa M, Kudo SE, Mori Y, et al. Artificial intelligence-assisted polyp detection for colonoscopy:initial experience. Gastroenterology 154:2027-2029, 2018
7)Urban G, Tripathi P, Alkayali T, et al. Deep learning localizes and identifies polyps in real time with 96% accuracy in screening colonoscopy. Gastroenterology 155:1069-1078, 2018
8)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 68:1813-1819, 2019
9)Klare P, Sander C, Prinzen M, et al. Automated polyp detection in the colorectum:a prospective study(with videos). Gastrointest Endosc 89:576-582, 2019
10)Häfner M, Liedlgruber M, Uhl A, et al. Delaunay triangulation-based pit density estimation for the classification of polyps in high-magnification chromo-colonoscopy. Comput Methods Programs Biomed 107:565-581, 2012
11)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 72:1047-1051, 2010
12)Tischendorf JJ, Gross S, Winograd R, et al. Computer-aided classification of colorectal polyps based on vascular patterns:a pilot study. Endoscopy 42:203-207, 2010
13)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 83:643-649, 2016
14)Chen PJ, Lin MC, Lai MJ, et al. Accurate classification of diminutive colorectal polyps using computer-aided analysis. Gastroenterology 154:568-575, 2018
15)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 68:94-100, 2019
16)Tamai N, Saito Y, Sakamoto T, et al. Effectiveness of computer-aided diagnosis of colorectal lesions using novel software for magnifying narrow-band imaging:a pilot study. Endosc Int Open 5:E690-694, 2017
17)Mori Y, Kudo SE, Chiu PW, et al. Impact of an automated system for endocytoscopic diagnosis of small colorectal lesions:an international web-based study. Endoscopy 48:1110-1118, 2016
18)Mori Y, Kudo SE, Wakamura K, et al. Novel computer-aided diagnostic system for colorectal lesions by using endocytoscopy(with videos). Gastrointest Endosc 81:621-629, 2015
19)Misawa M, Kudo SE, Mori Y, et al. Characterization of colorectal lesions using a computer-aided diagnostic system for narrow-band imaging endocytoscopy. Gastroenterology 150:1531-1532, 2016
20)Misawa M, Kudo SE, Mori Y, et al. Accuracy of computer-aided diagnosis based on narrow-band imaging endocytoscopy for diagnosing colorectal lesions:comparison with experts. Int J Comput Assist Radiol Surg 12:757-766, 2017
21)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 169:357-366, 2018
22)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 25:488-494, 2013
23)Horiuchi H, Tamai N, Kamba S, et al. Real-time computer-aided diagnosis of diminutive rectosigmoid polyps using an auto-fluorescence imaging system and novel color intensity analysis software. Scand J Gastroenterol 54:800-805, 2019
24)Mori Y, Kudo SE, Berzin TM, et al. Computer-aided diagnosis for colonoscopy. Endoscopy 49:813-819, 2017
25)Komeda Y, Handa H, Watanabe T, et al. Computer-aided diagnosis based on convolutional neural network system for colorectal polyp classification:preliminary experience. Oncology 93(Suppl 1):30-34, 2017
26)Renner J, Phlipsen H, Haller B, et al. Optical classification of neoplastic colorectal polyps-a computer-assisted approach(the COACH study). Scand J Gastroenterol 53:1100-1106, 2018
27)Sánchez-Montes C, Sánchez FJ, Bernal J, et al. Computer-aided prediction of polyp histology on white-light colonoscopy using surface pattern analysis. Endoscopy 51:261-265, 2019
28)Takeda K, Kudo SE, Mori Y, et al. Accuracy of diagnosing invasive colorectal cancer using computer-aided endocytoscopy. Endoscopy 49:798-802, 2017
29)Ito N, Kawahira H, Nakashima H, et al. Endoscopic diagnostic support system for cT1b colorectal cancer using deep learning. Oncology 96:44-50, 2019
30)Lui TKL, Wong KKY, Mak LLY, et al. Endoscopic prediction of deeply submucosal invasive carcinoma with use of artificial intelligence. Endosc Int Open 7:E514-520, 2019
31)Ozawa T, Ishihara S, Fujishiro M, et al. Novel computer-assisted diagnosis system for endoscopic disease activity in patients with ulcerative colitis. Gastrointest Endosc 89:416-421, 2019
32)Stidham RW, Liu W, Bishu S, et al. Performance of a deep learning model vs human reviewers in grading endoscopic disease severity of patients with ulcerative colitis. JAMA Netw Open 2:e193963, 2019
33)Maeda Y, Kudo SE, Mori Y, et al. Fully automated diagnostic system with artificial intelligence using endocytoscopy to identify the presence of histologic inflammation associated with ulcerative colitis(with video). Gastrointest Endosc 89:408-415, 2019
34)Quénéhervé L, David G, Bourreille A, et al. Quantitative assessment of mucosal architecture using computer-based analysis of confocal laser endomicroscopy in inflammatory bowel diseases. Gastrointest Endosc 89:626-636, 2019
35)Wu L, Zhang J, Zhou W, et al. Randomised controlled trial of WISENSE, a real-time quality improving system for monitoring blind spots during esophagogastroduodenoscopy. Gut 68:2161-2169, 2019
36)Rees CJ, Koo S. Artificial intelligence-upping the game in gastrointestinal endoscopy? Nat Rev Gastroenterol Hepatol 16:584-585, 2019