REFERENCES
1. Sung H, Ferlay J, Siegel RL, 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.
2. Cancer Research UK. Bowel cancer statistics. Available from: https://www.cancerresearchuk.org/health-professional/cancer-statistics/statistics-by-cancer-type/bowel-cancer. [Last accessed on 16 Sep 2023].
3. Kader R, Hadjinicolaou AV, Georgiades F, Stoyanov D, Lovat LB. Optical diagnosis of colorectal polyps using convolutional neural networks. World J Gastroenterol 2021;27:5908-18.
4. Shaukat A, Kaltenbach T, Dominitz JA, et al. Endoscopic recognition and management strategies for malignant colorectal polyps: recommendations of the US multi-society task force on colorectal cancer. Gastroenterology 2020;159:1916-34.e2.
6. Williams JG, Pullan RD, Hill J, et al. Management of the malignant colorectal polyp: ACPGBI position statement†. Colorectal Dis 2013;15:1-38.
7. Backes Y, Schwartz MP, ter Borg F, et al. Multicentre prospective evaluation of real-time optical diagnosis of T1 colorectal cancer in large non-pedunculated colorectal polyps using narrow band imaging (the OPTICAL study). Gut 2019;68:271-9.
8. Peery AF, Cools KS, Strassle PD, et al. Increasing rates of surgery for patients with nonmalignant colorectal polyps in the United States. Gastroenterology 2018;154:1352-60.e3.
9. Silver D, Schrittwieser J, Simonyan K, et al. Mastering the game of Go without human knowledge. Nature 2017;550:354-9.
10. Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Acharya UR. Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Med 2020;121:103792.
11. Lee JG, Jun S, Cho YW, et al. Deep learning in medical imaging: general overview. Korean J Radiol 2017;18:570-84.
12. Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer 2018;18:500-10.
13. Paeck KH, Heo WJ, Park DI, et al. Colonoscopy scheduling influences adenoma and polyp detection rates. Hepatogastroenterology 2013;60:1647-52.
14. Glissen Brown JR, Mansour NM, Wang P, et al. Deep learning computer-aided polyp detection reduces adenoma miss rate: a United States multi-center randomized tandem colonoscopy study (CADeT-CS trial). Clin Gastroenterol Hepatol 2022;20:1499-507.e4.
15. Wang P, Liu P, Glissen Brown JR, et al. Lower adenoma miss rate of computer-aided detection-assisted colonoscopy vs routine white-light colonoscopy in a prospective tandem study. Gastroenterology 2020;159:1252-61.e5.
16. Wallace MB, Sharma P, Bhandari P, et al. Impact of artificial intelligence on miss rate of colorectal neoplasia. Gastroenterology 2022;163:295-304.e5.
17. Endoscopic Classification Review Group. Update on the paris classification of superficial neoplastic lesions in the digestive tract. Endoscopy 2005;37:570-8.
18. Krenzer A, Heil S, Fitting D, et al. Automated classification of polyps using deep learning architectures and few-shot learning. Available from: https://doi.org/10.21203/rs.3.rs-2106189/v1. [Last accessed on 16 Sep 2023].
19. Okamoto Y, Yoshida S, Izakura S, et al. Development of multi-class computer-aided diagnostic systems using the NICE/JNET classifications for colorectal lesions. J Gastroenterol Hepatol 2022;37:104-10.
20. 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.
21. Kudo S, Tamura S, Nakajima T, Yamano H, Kusaka H, Watanabe H. Diagnosis of colorectal tumorous lesions by magnifying endoscopy. Gastrointest Endosc 1996;44:8-14.
22. 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;72:1047-51.
23. Patino-Barrientos S, Sierra-Sosa D, Garcia-Zapirain B, Castillo-Olea C, Elmaghraby A. Kudo’s classification for colon polyps assessment using a deep learning approach. Appl Sci 2020;10:501.
24. Lu Z, Xu Y, Yao L, et al. Real-time automated diagnosis of colorectal cancer invasion depth using a deep learning model with multimodal data (with video). Gastrointest Endosc 2022;95:1186-94.e3.
25. Yao L, Lu Z, Yang G, et al. Development and validation of an artificial intelligence-based system for predicting colorectal cancer invasion depth using multi-modal data. Dig Endosc 2023;35:625-35.
26. Karsenti D, Tharsis G, Perrot B, et al. Effect of real-time computer-aided detection of colorectal adenoma in routine colonoscopy (COLO-GENIUS): a single-centre randomised controlled trial. Lancet Gastroenterol Hepatol 2023;8:726-34.
27. Levy I, Bruckmayer L, Klang E, Ben-Horin S, Kopylov U. Artificial intelligence-aided colonoscopy does not increase adenoma detection rate in routine clinical practice. Am J Gastroenterol 2022;117:1871-3.
28. Ladabaum U, Shepard J, Weng Y, Desai M, Singer SJ, Mannalithara A. Computer-aided detection of polyps does not improve colonoscopist performance in a pragmatic implementation trial. Gastroenterology 2023;164:481-3.e6.
29. Kader R, Baggaley RF, Hussein M, et al. Survey on the perceptions of UK gastroenterologists and endoscopists to artificial intelligence. Frontline Gastroenterol 2022;13:423-9.
30. Fazal MI, Patel ME, Tye J, Gupta Y. The past, present and future role of artificial intelligence in imaging. Eur J Radiol 2018;105:246-50.
31. Antonelli G, Rizkala T, Iacopini F, Hassan C. Current and future implications of artificial intelligence in colonoscopy. Ann Gastroenterol 2023;36:114-22.
