Constructing and validating vision transformer-based assisted detection models for atrophic gastritis: A retrospective study.

ObjectiveTraining and validating vision transformer-based endoscopic assisted detection models for chronic atrophic gastritis (CAG) to assist endoscopists in detecting and localizing atrophic lesions.MethodsIn this retrospective study, gastroscopy images stored in the endoscopy center were collected between June 2019 and March 2023. On the basis of pathological findings, the images were manually classified into CAG and chronic nonatrophic gastritis (CNAG) using Labelme software, and the atrophic areas were further manually annotated in the CAG images. Furthermore, the anatomical structures were meticulously documented on the CNAG images. The labeled images were subsequently employed to train the Swin transformer and SSFormer for the task of detecting the anatomical structures of the stomach, CAG and atrophic lesion regions.ResultsThe test results revealed that the trained Swin transformer model had an accuracy of 0.98 in recognizing the anatomical structures of the stomach (gastric sinus, stomach angle, lesser curvature, cardia fundus, and greater curvature). Moreover, the accuracy, specificity, and sensitivity of the model in recognizing the CAG and CNAG images were 0.91, 0.95, and 0.86, respectively, which were significantly superior to those of the junior endoscopists who participated in the test (