Addressing false-positive findings with artificial intelligence for polyp detection

Endoscopy 2021; 53(09): 941-942
DOI: 10.1055/a-1381-7849

Editorial

Referring to Holzwanger E et al. p. 937–940

Authors

Yuichi Mori

¹Clinical Effectiveness Research Group, Institute of Health and Society, Faculty of Medicine, University of Oslo, Oslo, Norway

²Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
Michael Bretthauer

¹Clinical Effectiveness Research Group, Institute of Health and Society, Faculty of Medicine, University of Oslo, Oslo, Norway

³Department of Transplantation Medicine, Oslo University Hospital, Oslo, Norway

References
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2 Bisschops R, East JE, Hassan C. et al. Advanced imaging for detection and differentiation of colorectal neoplasia: European Society of Gastrointestinal Endoscopy (ESGE) Guideline – Update 2019. Endoscopy 2019; 51: 1155-1179
3 Berzin TM, Parasa S, Wallace MB. et al. Position statement on priorities for artificial intelligence in GI endoscopy: a report by the ASGE Task Force. Gastrointest Endosc 2020; 92: 951-959
4 Ahmad OF, Mori Y, Misawa M. et al. Establishing key research questions for the implementation of artificial intelligence in colonoscopy – a modified Delphi method. Endoscopy 2020;
5 Holzwanger EA, Bilal M, Glissen Brown JR. et al. Benchmarking definitions of false-positive alerts during computer-aided polyp detection in colonoscopy. Endoscopy 2021; 53: 937-940
6 Misawa M, Kudo SE, Mori Y. et al. Development of a computer-aided detection system for colonoscopy and a publicly accessible large colonoscopy video database (with video). Gastrointest Endosc 2020;
7 Bernal J, Sanchez FJ, Fernandez-Esparrach G. et al. WM-DOVA maps for accurate polyp highlighting in colonoscopy: validation vs. saliency maps from physicians. Comput Med Imaging Graph 2015; 43: 99-111