Advancing Automatic Gastritis Diagnosis: An Interpretable Multilabel Deep Learning Framework for the Simultaneous Assessment of Multiple Indicators

Abstract

The evaluation of morphological features such as inflammation, gastric atrophy, and intestinal metaplasia is crucial for diagnosing gastritis. However, artificial intelligence (AI) analysis for nontumor diseases like gastritis is limited. Previous deep learning models have omitted important morphological indicators and cannot simultaneously diagnose gastritis indicators or provide interpretable labels. To address this, an attention-based multi-instance multilabel learning network (AMMNet) was developed to simultaneously achieve the multilabel diagnosis of activity, atrophy, and intestinal metaplasia with only slide-level weak labels. To evaluate AMMNet's real-world performance, a diagnostic test was designed to observe improvements in junior pathologists' diagnostic accuracy and efficiency with and without AMMNet assistance. In this study of 1,096 patients from 7 independent medical centers, AMMNet performed well in assessing activity (area under the curve (AUC): 0.93), atrophy (AUC: 0.97), and intestinal metaplasia (AUC: 0.93). The false-negative rates (FNRs) of these indicators were only 0.04, 0.08, and 0.18, respectively, and junior pathologists had lower FNRs with model assistance (0.15 vs. 0.10). Furthermore, AMMNet reduced the time required per whole-slide image (WSI) from 5.46 minutes to only 2.85 minutes, enhancing diagnostic efficiency. In block-level clustering analysis, AMMNet effectively visualized task-related patches within WSIs, improving interpretability. These findings highlight AMMNet’s effectiveness in accurately evaluating gastritis morphological indicators on multicenter datasets. Using multi-instance multilabel learning strategies to support routine diagnostic pathology deserves further evaluation.