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

Ryota Niikura, Tomonori Aoki, Satoki Shichijo, Atsuo Yamada, Takuya Kawahara, Yusuke Kato, Yoshihiro Hirata, Yoku Hayakawa, Nobumi Suzuki, Masanori Ochi, Toshiaki Hirasawa, Tomohiro Tada, Takashi Kawai, Kazuhiko Koike, Ryota Niikura, Tomonori Aoki, Satoki Shichijo, Atsuo Yamada, Takuya Kawahara, Yusuke Kato, Yoshihiro Hirata, Yoku Hayakawa, Nobumi Suzuki, Masanori Ochi, Toshiaki Hirasawa, Tomohiro Tada, Takashi Kawai, Kazuhiko Koike

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.

Trial registration: ClinicalTrials.gov NCT04040374.

Conflict of interest statement

The authors declare that they have no conflict of interest.

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/).

Figures

Fig. 1
Fig. 1
Study flow diagram.
Fig. 2
Fig. 2
Images of gastric cancer used for diagnostic purposes by the artificial intelligence (AI) diagnosis group. Green boxes, gold-standard bounding boxes; red boxes, AI-detected bounding boxes. Source: Keita Otani.

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Source: PubMed

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