Deep learning assistance for the histopathologic diagnosis of Helicobacter pylori

Abstract

Aims: Deep learning (DL), a sub-area of artificial intelligence, has demonstrated great promise at automating diagnostic tasks in pathology, yet its translation into clinical settings has been slow. Few studies have examined its impact on pathologist performance, when embedded into clinical workflows. The identification of H. pylori on H&E stain is a tedious, imprecise task which might benefit from DL assistance. In this study, a DL assistant was developed to diagnose H. pylori in gastric biopsies, and its impact on pathologist diagnostic accuracy and turnaround time was tested. Methods and results: H&E-stained whole-slide images (WSI) of 303 gastric biopsies with ground truth confirmation by immunohistochemistry formed the study dataset; 47 and 126 WSI were respectively used to train and optimize the DL assistant to detect H. pylori, and 130 were used in a clinical experiment in which 3 experienced GI pathologists reviewed the same test set with and without assistance. On the test set, the assistant achieved high performance, with a WSI-level area under the receiver-operating-characteristic curve (AUROC) of 0.965 (95% CI 0.934–0.987). On H. pylori-positive cases, assisted diagnoses were faster (βˆ, the fixed effect size for assistance ​= ​−0.557, p ​= ​0.003) and much more accurate (OR ​= ​13.37, p ​< ​0.001) than unassisted diagnoses. However, assistance increased diagnostic uncertainty on H. pylori-negative cases, resulting in an overall decrease in assisted accuracy (OR ​= ​0.435, p ​= ​0.016) and negligible impact on overall turnaround time (βˆ for assistance ​= ​0.010, p ​= ​0.860). Conclusions: DL can assist pathologists with H. pylori diagnosis, but its integration into clinical workflows requires optimization to mitigate diagnostic uncertainty as a potential consequence of assistance.