aceA practical AI model for endoscopic diagnosis of gastric premalignant lesions and its application to predicting metachronous recurrence
Background/aims To evaluate the diagnostic performance of an artificial intelligence (AI)-based optical system for chronic atrophic gastritis (CAG) and intestinal metaplasia (IM), and its ability to predict metachronous recurrence. Methods We analyzed 72,794 endoscopic images from 5,000 patients undergoing esophagogastroduodenoscopy with biopsy-confirmed CAG/IM. A convolutional neural network model was developed and compared with experts (n=16) and novices (n=15). The association between AI-predicted IM extent a…
A neural network classified chronic atrophic gastritis more accurately than both novice and expert endoscopists in a single-center study, and its predicted intestinal metaplasia extent showed a dose-response trend with recurrence.
The model was tested in a single-center study and the authors state it needs multicenter external validation before clinical adoption; the individual hazard ratios for recurrence were not statistically significant.
Evidence
- Peer-reviewedDigestive and Liver Disease2026-10-16
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Truvace Impact Record TRV-2026-1937, v1: “A practical AI model for endoscopic diagnosis of gastric premalignant lesions and its application to predicting metachronous recurrence.” Truvace, 2026-10-19. /record/TRV-2026-1937 (accessed at citation time). sha256 231d368ec51d1a4c…
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