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.