Artificial intelligence-based prediction of organ involvement in Sjogren’s syndrome using labial gland biopsy whole-slide images

作者：Yong Ren, Wenqi Xia, Jiayun Wu, Zheng Yang, Ye Jiang, Ya Wen, Qiuquan Guo, Jieruo Gu, Jun Yang, Jun Luo, Qing Lv · 发表于：Clinical Rheumatology · 年份：2025 · DOI：10.1007/s10067-025-07518-5 · 被引用次数：11 · 研究领域：Salivary Gland Disorders and Functions、Systemic Lupus Erythematosus Research、Oral microbiology and periodontitis research

OBJECTIVES: This study aimed to develop a deep learning-based model to predict the risk of high-risk extra-glandular organ involvement (HR-OI) in patients with Sjogren's syndrome (SS) using whole-slide images (WSI) from labial gland biopsies. METHODS: We collected WSI data from 221 SS patients. Pre-trained models, including ResNet50, InceptionV3, and EfficientNet-B5, were employed to extract image features. A classification model was constructed using multi-instance learning and ensemble learning techniques. RESULTS: The ensemble model achieved high area under the receiver operating characteristic (ROC) curve values on both internal and external validation sets, indicating strong predictive performance. Moreover, the model was able to identify key pathological features associated with the risk of HR-OI. CONCLUSIONS: This study demonstrates that a deep learning-based model can effectively predict the risk of HR-OI in SS patients, providing a novel basis for clinical decision-making. Key Points 1. What is already known on this topic? • Sjogren's syndrome (SS) is a chronic autoimmune disease affecting the salivary and lacrimal glands. • Accurate prediction of high-risk extra-glandular organ involvement (HR-OI) is crucial for timely intervention and improved patient outcomes in SS. • Traditional methods for HR-OI prediction rely on clinical data and lack objectivity. 2. What this study adds? • This study proposes a novel deep learning-based model using whole-slide images (WSI) fr...