In Gastrointestinal endoscopy ; h5-index 72.0
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.
Seo Ji Yeon, Hong Hotak, Ryu Wi-Sun, Kim Dongmin, Chun Jaeyoung, Kwak Min-Sun
2023-Jan-11
Artificial intelligence, Endoscopic image, Helicobacter pylori
