Artificial intelligence and the future of endoscopy: should we be quietly excited?

Endoscopy 2019; 51(06): 511-512
DOI: 10.1055/a-0831-2549

Editorial

Referring to Wu L et al. p. 522–531

Michael F. Byrne

Vancouver General Hospital, University of British Columbia, Vancouver, British Columbia, Canada

References
1 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
2 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
3 Sehgal V, Rosenfeld A, Graham DG. et al. Machine learning creates a simple endoscopic classification system that improves dysplasia detection in Barrett’s oesophagus amongst non-expert endoscopists. Gastroenterol Res Pract 2018; 29: 1872437
4 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 2019; 89: 408-415
5 Byrne MF, Shahidi N, Rex DK. Will computer-aided detection and diagnosis revolutionize colonoscopy?. Gastroenterology 2017; 153: 1460-1464
6 Wu L, Zhou W, Wan X. et al. A deep neural network improves endoscopic detection of early gastric cancer without blind spots. Endoscopy 2019; 51: 522-531
7 Holzinger A. Interactive machine learning for health informatics: when do we need the human-in-the-loop?. Brain Inf 2016; 3: 119-131
8 Bewley A, Ge Z, Ott L. et al. Simple online and realtime tracking. 2016 IEEE International Conference on Image Processing 2016; 3464-3468