CC BY-NC-ND 4.0 · Endosc Int Open 2021; 09(07): E1004-E1011
DOI: 10.1055/a-1475-3624
Original article

Artificial intelligence-assisted colonic endocytoscopy for cancer recognition: a multicenter study

Yuichi Mori
 1  Clinical Effectiveness Research Group, Institute of Health and Society, University of Oslo, Oslo, Norway
 2  Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
,
Shin-ei Kudo
 2  Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
,
Masashi Misawa
 2  Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
,
Kinichi Hotta
 3  Division of Endoscopy, Shizuoka Cancer Center Hospital, Shizuoka, Japan
,
Ohtsuka Kazuo
 4  Department of Endoscopy, Tokyo Medical and Dental University, Tokyo, Japan
,
Shoichi Saito
 5  Department of Gastroenterology, The Cancer Institute Hospital, Tokyo, Japan
,
Hiroaki Ikematsu
 6  Department of Gastroenterology and Endoscopy, National Cancer Center Hospital East, Kashiwa, Japan
,
Yutaka Saito
 7  Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
,
Takahisa Matsuda
 7  Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
 8  Cancer Screening Center, National Cancer Center Hospital, Tokyo, Japan
 9  Division of Screening Technology, Center for Public Health Sciences, National Cancer Center, Tokyo, Japan
,
Takeda Kenichi
 2  Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
,
Toyoki Kudo
 2  Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
,
Tetsuo Nemoto
10  Department of Diagnostic Pathology, School of Medicine, Showa University, Yokohama Northern Hospital, Kanagawa, Japan
,
Hayato Itoh
11  Graduate School of Informatics, Nagoya University, Nagoya, Japan
,
Kensaku Mori
11  Graduate School of Informatics, Nagoya University, Nagoya, Japan
› Author Affiliations

Abstract

Background and study aims Large adenomas are sometimes misidentified as cancers during colonoscopy and are surgically removed. To address this overtreatment, we developed an artificial intelligence (AI) tool that identified cancerous pathology in vivo with high specificity. We evaluated our AI tool under the supervision of a government agency to obtain regulatory approval.

Patients and methods The AI tool outputted three pathological class predictions (cancer, adenoma, or non-neoplastic) for endocytoscopic images obtained at 520-fold magnification and previously trained on 68,082 images from six academic centers. A validation test was developed, employing 500 endocytoscopic images taken from various parts of randomly selected 50 large (≥ 20 mm) colorectal lesions (10 images per lesion). An expert board labelled each of the 500 images with a histopathological diagnosis, which was made using endoscopic and histopathological images. The validation test was performed using the AI tool under a controlled environment. The primary outcome measure was the specificity in identifying cancer.

Results The validation test consisted of 30 cancers, 15 adenomas, and five non-neoplastic lesions. The AI tool could analyze 83.6 % of the images (418/500): 231 cancers, 152 adenomas, and 35 non-neoplastic lesions. Among the analyzable images, the AI tool identified the three pathological classes with an overall accuracy of 91.9 % (384/418, 95 % confidence interval [CI]: 88.8 %–94.3 %). Its sensitivity and specificity for differentiating cancer was 91.8 % (212/231, 95 % CI: 87.5 %–95.0 %) and 97.3 % (182/187, 95 % CI: 93.9 %–99.1 %), respectively.

Conclusions The newly developed AI system designed for endocytoscopy showed excellent specificity in identifying colorectal cancer.



Publication History

Received: 21 December 2020

Accepted: 23 February 2021

Publication Date:
17 June 2021 (online)

© 2021. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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