CC BY-NC-ND 4.0 · Endoscopy 2022; 54(08): 780-784
DOI: 10.1055/a-1660-6500
Innovations and brief communications

Artificial intelligence versus expert endoscopists for diagnosis of gastric cancer in patients who have undergone upper gastrointestinal endoscopy

Ryota Niikura
1   Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Japan
2   Gastroenterological Endoscopy, Tokyo Medical University, Tokyo, Japan
,
Tomonori Aoki
1   Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Japan
,
Satoki Shichijo
3   Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan
,
1   Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Japan
,
Takuya Kawahara
4   Clinical Research Promotion Center, The University of Tokyo Hospital, Tokyo, Japan
,
Yusuke Kato
5   AI Medical Service Inc., Tokyo, Japan
,
Yoshihiro Hirata
6   Division of Advanced Genome Medicine, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
,
Yoku Hayakawa
1   Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Japan
,
Nobumi Suzuki
1   Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Japan
,
Masanori Ochi
1   Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Japan
,
Toshiaki Hirasawa
7   Department of Gastroenterology, Cancer Institute Hospital Ariake, Japanese Foundation for Cancer Research, Tokyo, Japan
,
Tomohiro Tada
5   AI Medical Service Inc., Tokyo, Japan
8   Department of Surgical Oncology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
9   Tada Tomohiro Institute of Gastroenterology and Proctology, Saitama, Japan
,
Takashi Kawai
2   Gastroenterological Endoscopy, Tokyo Medical University, Tokyo, Japan
,
Kazuhiko Koike
1   Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Japan
› Author Affiliations
Supported by: P-CREATE by AMED 21448169

Trial Registration: ClinicalTrials.gov Registration number (trial ID): NCT04040374 Type of study: Retrospective

Abstract

Aims To compare endoscopy gastric cancer images diagnosis rate between artificial intelligence (AI) and expert endoscopists.

Patients and methods We used the retrospective data of 500 patients, including 100 with gastric cancer, matched 1:1 to diagnosis by AI or expert endoscopists. We retrospectively evaluated the noninferiority (prespecified margin 5 %) of the per-patient rate of gastric cancer diagnosis by AI and compared the per-image rate of gastric cancer diagnosis.

Results Gastric cancer was diagnosed in 49 of 49 patients (100 %) in the AI group and 48 of 51 patients (94.12 %) in the expert endoscopist group (difference 5.88, 95 % confidence interval: −0.58 to 12.3). The per-image rate of gastric cancer diagnosis was higher in the AI group (99.87 %, 747 /748 images) than in the expert endoscopist group (88.17 %, 693 /786 images) (difference 11.7 %).

Conclusions Noninferiority of the rate of gastric cancer diagnosis by AI was demonstrated but superiority was not demonstrated.

Supplementary material



Publication History

Received: 30 August 2020

Accepted after revision: 12 October 2021

Accepted Manuscript online:
04 October 2021

Article published online:
04 May 2022

© 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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