A Multisite Fusion‐Based Deep Convolutional Neural Network for Classification of Helicobacter pylori Infection Status Using Endoscopic Images: A Multicenter Study

BACKGROUND AND AIM: We aimed to develop a deep convolutional neural network (DCNN) that integrates features from multiple sites of the stomach to classify Hp infection status, distinguishing between uninfected, previously infected, and currently infected. METHODS: Ten deep learning architectures were employed to develop DCNN models using a training dataset comprising 3380 white-light images collected from 676 subjects across eight centers. External validation was conducted with a separate dataset consisting of images from 126 individuals. External testing was subsequently performed to assess and compare the diagnostic efficacy between single-site and multisite fusion DCNN models. RESULTS: Among these models, the DCNN model using Wide-ResNet emerged as the top performer, achieving a high accuracy of 68.11% (95% confidence interval [CI]: 63.36%-73.09%) with an area under the curve (AUC) of 75.06% (95% CI: 70.22%-80.24%) for noninfection, 69.18% (95% CI: 64.51%-74.03%) for past infection, and 77.04% (95% CI: 72.12%-82.39%) for current infection using images from a single site on the lesser gastric curvature. In comparison, the voting-based multisite fusion DCNN model demonstrated superior accuracy (73.83%, 95% CI: 69.12%-78.65%) and AUC (77.51%, 95% CI: 72.89%-82.59%), particularly notable for noninfection and current infection. Additionally, the DCNN model exhibited heightened sensitivity, specificity, and precision compared to experienced endoscopists. CONCLUSIONS: The DCNN model, crafted through a voting-based multisite fusion, displayed stellar performance, excelling in the classification of Hp infection status into uninfected and currently infected.