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

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 retrospecti...

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Autores principales: Niikura, Ryota, Aoki, Tomonori, Shichijo, Satoki, Yamada, Atsuo, Kawahara, Takuya, Kato, Yusuke, Hirata, Yoshihiro, Hayakawa, Yoku, Suzuki, Nobumi, Ochi, Masanori, Hirasawa, Toshiaki, Tada, Tomohiro, Kawai, Takashi, Koike, Kazuhiko
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Georg Thieme Verlag KG 2022
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9329064/
https://www.ncbi.nlm.nih.gov/pubmed/34607377
http://dx.doi.org/10.1055/a-1660-6500
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author Niikura, Ryota
Aoki, Tomonori
Shichijo, Satoki
Yamada, Atsuo
Kawahara, Takuya
Kato, Yusuke
Hirata, Yoshihiro
Hayakawa, Yoku
Suzuki, Nobumi
Ochi, Masanori
Hirasawa, Toshiaki
Tada, Tomohiro
Kawai, Takashi
Koike, Kazuhiko
author_facet Niikura, Ryota
Aoki, Tomonori
Shichijo, Satoki
Yamada, Atsuo
Kawahara, Takuya
Kato, Yusuke
Hirata, Yoshihiro
Hayakawa, Yoku
Suzuki, Nobumi
Ochi, Masanori
Hirasawa, Toshiaki
Tada, Tomohiro
Kawai, Takashi
Koike, Kazuhiko
author_sort Niikura, Ryota
collection PubMed
description 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.
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spelling pubmed-93290642022-07-29 Artificial intelligence versus expert endoscopists for diagnosis of gastric cancer in patients who have undergone upper gastrointestinal endoscopy Niikura, Ryota Aoki, Tomonori Shichijo, Satoki Yamada, Atsuo Kawahara, Takuya Kato, Yusuke Hirata, Yoshihiro Hayakawa, Yoku Suzuki, Nobumi Ochi, Masanori Hirasawa, Toshiaki Tada, Tomohiro Kawai, Takashi Koike, Kazuhiko Endoscopy 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. Georg Thieme Verlag KG 2022-05-04 /pmc/articles/PMC9329064/ /pubmed/34607377 http://dx.doi.org/10.1055/a-1660-6500 Text en 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/) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License, which permits unrestricted reproduction and distribution, for non-commercial purposes only; and use and reproduction, but not distribution, of adapted material for non-commercial purposes only, provided the original work is properly cited.
spellingShingle Niikura, Ryota
Aoki, Tomonori
Shichijo, Satoki
Yamada, Atsuo
Kawahara, Takuya
Kato, Yusuke
Hirata, Yoshihiro
Hayakawa, Yoku
Suzuki, Nobumi
Ochi, Masanori
Hirasawa, Toshiaki
Tada, Tomohiro
Kawai, Takashi
Koike, Kazuhiko
Artificial intelligence versus expert endoscopists for diagnosis of gastric cancer in patients who have undergone upper gastrointestinal endoscopy
title Artificial intelligence versus expert endoscopists for diagnosis of gastric cancer in patients who have undergone upper gastrointestinal endoscopy
title_full Artificial intelligence versus expert endoscopists for diagnosis of gastric cancer in patients who have undergone upper gastrointestinal endoscopy
title_fullStr Artificial intelligence versus expert endoscopists for diagnosis of gastric cancer in patients who have undergone upper gastrointestinal endoscopy
title_full_unstemmed Artificial intelligence versus expert endoscopists for diagnosis of gastric cancer in patients who have undergone upper gastrointestinal endoscopy
title_short Artificial intelligence versus expert endoscopists for diagnosis of gastric cancer in patients who have undergone upper gastrointestinal endoscopy
title_sort artificial intelligence versus expert endoscopists for diagnosis of gastric cancer in patients who have undergone upper gastrointestinal endoscopy
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9329064/
https://www.ncbi.nlm.nih.gov/pubmed/34607377
http://dx.doi.org/10.1055/a-1660-6500
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