Real-Time Prediction of Helicobacter pylori Infection Using a Deep Learning Model During Esophagogastroduodenoscopy:...

Real-time assessment of Helicobacter pylori infection during esophagogastroduodenoscopy (EGD) is clinically valuable but remains technically challenging. We developed a deep learning-based system to predict H. pylori infection directly from EGD videos.

This prospective multicenter diagnostic study enrolled patients undergoing EGD at three hospitals between September and December 2024. All patients underwent the 14C-urea breath test as the reference standard. The model integrated deep learning-based video analysis to predict gastric regions with H. pylori infection in real time. The primary outcomes were diagnostic accuracy, sensitivity, and specificity. Secondary outcomes included the positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC). Logistic regression was used to explore factors associated with diagnostic performance.

Among the cohort of 701 patients, 42.4% were positive for H. pylori infection. The model achieved an AUC of 0.918 (95% CI: 0.895-0.937), with an accuracy of 86.3% (95% CI: 83.5%-88.8%), sensitivity of 86.9% (95% CI: 82.5%-90.5%), and specificity of 85.9% (95% CI: 82.1%-89.1%). By multivariate analysis, mucosal atrophy was independently associated with an increased diagnostic error (OR = 1.788, p = 0.014), while a higher examination quality score was protective (OR = 0.600, p < 0.001).

This deep learning model demonstrated high diagnostic performance for real-time H. pylori detection during EGD across multiple centers and should be considered to improve diagnostic efficiency and consistency of clinical endoscopy.

Chinese Clinical Trial Registry registration number: ChiCTR2400088612.