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Abstract Objective Using endoscopic images, we have previously developed computer‐aided diagnosis models to predict the histopathology of gastric neoplasms. However, no model that categorizes every stage of gastric carcinogenesis has been published. In this study, a deep‐learning‐based diagnosis model was developed and validated to automatically classify all stages of gastric carcinogenesis, including atrophy and intestinal metaplasia, in endoscopy images. Design A total of 18,701 endoscopic images were collected retrospectively and randomly divided into train, validation, and internal‐test datasets in an 8:1:1 ratio. The primary outcome was lesion‐classification accuracy in six categories: normal/atrophy/intestinal metaplasia/dysplasia/early /advanced gastric cancer. External‐validation of performance in the established model used 1427 novel images from other institutions that were not used in training, validation, or internal‐tests. Results The internal‐test lesion‐classification accuracy was 91.2% (95% confidence interval: 89.9%–92.5%). For performance validation, the established model achieved an accuracy of 82.3% (80.3%–84.3%). The external‐test per‐class receiver operating characteristic in the diagnosis of atrophy and intestinal metaplasia was 93.4 ± 0% and 91.3 ± 0%, respectively. Conclusions The established model demonstrated high performance in the diagnosis of preneoplastic lesions (atrophy and intestinal metaplasia) as well as gastric neoplasms.
Gong et al. (Sat,) studied this question.