Gastric cancer incidence based on endoscopic Kyoto classification of gastritis

Toyoshima, Osamu; Nishizawa, Toshihiro; Yoshida, Shuntaro; Matsuno, Tatsuya; Fujisawa, Gota; Toyoshima, Akira; Ebinuma, Hirotoshi; Fujishiro, Mitsuhiro; Saito, Yutaka; Suzuki, Hidekazu

doi:10.3748/wjg.v29.i31.4763

Cited by 4 publications

(1 citation statement)

References 41 publications

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“…WJG v29i31: Some articles are interesting, on average. The most interesting from among this group is “Gastric cancer incidence based on endoscopic Kyoto classification of gastritis”[ 50 ]. The reviewers performed overall reasonable peer reviews, but some are simplistic.…”

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confidence: 99%

Editor-in-Chief articles of choice and comments at the year-end of 2023

Tarnawski

2024

World J Gastroenterol

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As the Editor-in-Chief, every week prior to a new issue’s online publication, I perform a careful review of all encompassed articles, including the title, clinical and/or research importance, originality, novelty, and ratings by the peer reviewers. Based on this review, I select the papers of choice and suggest pertinent changes (e.g. , in the title) to the Company Editors responsible for publication. This process, while time-consuming, is very important for assuring the quality of publications and highlighting important articles that Readers may revisit.

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confidence: 99%

Editor-in-Chief articles of choice and comments at the year-end of 2023

Tarnawski

2024

World J Gastroenterol

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A deep learning approach for gastroscopic manifestation recognition based on Kyoto Gastritis Score

Liu,

Zhang,

Zhong

et al. 2024

Annals of Medicine

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Objective The risk of gastric cancer can be predicted by gastroscopic manifestation recognition and the Kyoto Gastritis Score. This study aims to validate the applicability of AI approaches for recognizing gastroscopic manifestations according to the definition of Kyoto Gastritis Score, with the goal of improving early gastric cancer detection and reducing gastric cancer mortality. Methods In this retrospective study, 29013 gastric endoscopy images were collected and carefully annotated into five categories according to the Kyoto Gastritis Score, i.e. atrophy (A), diffuse redness (DR), enlarged folds (H), intestinal metaplasia (IM), and nodularity (N). As a multi-label recognition task, we propose a deep learning approach composed of five GAM-EfficientNet models, each performing a multiple classification to quantify gastroscopic manifestations, i.e. no presentation or the severity score 0–2. This approach was compared with endoscopists of varying years of experience in terms of accuracy, specificity, precision, recall, and F1 score. Results The approach demonstrated good performance in identifying the five manifestations of the Kyoto Gastritis Score, with an average accuracy, specificity, precision, recall, and F1 score of 78.70%, 91.92%, 80.23%, 78.70%, and 0.78, respectively. The average performance of five experienced endoscopists was 72.63%, 90.00%, 77.68%, 72.63%, and 0.73, while that of five less experienced endoscopists was 66.60%, 87.44%, 70.88%, 66.60%, and 0.66, respectively. The sample t-test indicates that the approach’s average accuracy, specificity, precision, recall, and F1 score for identifying the five manifestations were significantly higher than those of less experienced endoscopists, experienced endoscopists, and all endoscopists on average ( p < 0.05). Conclusion Our study demonstrates the potential of deep learning approaches on gastric manifestation recognition over junior, even senior endoscopists. Thus, the deep learning approach holds potential as an auxiliary tool, although prospective validation is still needed to assess its clinical applicability.

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Cutting-edge insights into Helicobacter research

Mori,

Suzuki

2024

Free Radical Research

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Gastric cancer incidence based on endoscopic Kyoto classification of gastritis

Cited by 4 publications

References 41 publications

Editor-in-Chief articles of choice and comments at the year-end of 2023

Editor-in-Chief articles of choice and comments at the year-end of 2023

A deep learning approach for gastroscopic manifestation recognition based on Kyoto Gastritis Score

Cutting-edge insights into Helicobacter research

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