A novel Artificial Intelligence-assisted white light endoscopic model in the diagnosis of cardia atrophy: a multicenter study.

Background and aims: Atrophy extends beyond the cardia is open atrophy and closely related to gastric cancer. However, there is difficulty in determining cardia atrophy under endoscopy. Methods: This is multicenter prospective study, a total of 895 patients were assigned to the training and validation sets to establish a cardia atrophy diagnostic model. A total of 220 patients were included in the internal test set. Additionally, a total of 354 patients were included in the external test set. In the internal and external test sets, three senior and three junior endoscopists made online diagnoses, comparing the performance differences to the artificial intelligence (AI)-assisted system. Results: In the internal test dataset, the accuracy, sensitivity, and specificity of the AI-assisted cardia atrophy diagnostic system were 0.927, 0.941, and 0.919, respectively, with a positivev predictive value (PPV) of 0.878, negative predictive value (NPV) of 0.962, and area under the curve (AUC) of 0.967. In the external test dataset, the accuracy, sensitivity, and specificity were 0.918, 0.894, and 0.931 respectively, with a PPV of 0.874, NPV of 0.943, and AUC of 0.953. Furthermore, it was found that the diagnosis accuracy, sensitivity, specificity, PPV, and NPV of the AI-assisted system were all higher than those of senior endoscopists. The receiver operating characteristic curve area (AUC) of AI-assisted system was 0.967 and 0.953 in the internal and external test sets, respectively. Heat map showed a high consistency between the AI-system and endoscopists. Conclusion: Our newly developed AI-assisted system shows superior performance in identifying cardia atrophy compared to senior endoscopists, and can be used to guide endoscopic judgment and targeted biopsies for cardia atrophy.