Development and validation of a convolutional neural network model for diagnosing Helicobacter pylori infections with endoscopic images: a multicenter study

Background and Aims Insufficient validation limits the generalizability of deep learning in diagnosing Helicobacter pylori (H. pylori) infection with endoscopic images. The aim of this study was to develop a deep learning model for the diagnosis of H. pylori infection using endoscopic images and validate the model with internal and external datasets. Methods A convolutional neural network (CNN) model was developed based on a training dataset comprising 13,403 endoscopic images from 952 patients who underwent endoscopy at Seoul National University Hospital Gangnam Center. Internal validation was performed using a separate dataset comprising the images of 411 individuals of Korean descent and 131 of non-Korean descent. External validation was performed with the images of 160 patients in Gangnam Severance Hospital. Gradient-weighted class activation mapping (Grad-CAM) was performed to visually explain the model. Results In predicting H. pylori ever-infected status, the sensitivity, specificity and accuracy of internal validation for people of Korean descent were 0.96 (95% CI 0.93–0.98), 0.90 (95% CI 0.85–0.95), and 0.94 (95% CI, 0.91-0.96), respectively. In the internal validation for people of non-Korean descent, the sensitivity, specificity and accuracy in predicting H. pylori ever-infected status were 0.92 (95% CI, 0.86-0.98), 0.79 (95% CI, 0.67-0.91) and 0.88 (95% CI, 0.82-0.93), respectively. In the external validation cohort, they were 0.86 (95% CI, 0.80-0.93), 0.88 (95% CI, 0.79-0.96), and 0.87 (95% CI, 0.82-0.92), respectively, when performing two-group categorization. The Grad-CAM showed that the CNN model captured the characteristic findings of each group. Conclusions This CNN model for diagnosing H. pylori infection showed good overall performance in internal and external validation datasets, particularly in categorizing patients into the never- versus ever-infected groups.