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Open Access 05-06-2025 | Primary Sjögren Syndrome | ORIGINAL ARTICLE
Artificial intelligence-based prediction of organ involvement in Sjogren’s syndrome using labial gland biopsy whole-slide images
Authors: Yong Ren, Wenqi Xia, Jiayun Wu, Zheng Yang, Ye Jiang, Ya Wen, Qiuquan Guo, Jieruo Gu, Jun Yang, Jun Luo, Qing Lv
Published in: Clinical Rheumatology
Login to get accessAbstract
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.
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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) from labial gland biopsies for predicting HR-OI in SS patients.
• Our model utilizes pre-trained convolutional neural networks (CNNs) and a Vision Transformer (ViT) module to extract informative features from WSI data.
• The ensemble model achieves high accuracy in predicting HR-OI, outperforming traditional methods.
• The model can identify key pathological features in WSI data associated with HR-OI risk.
3. How this study might affect research, practice or policy?
• This study provides a novel and objective approach for predicting HR-OI in SS patients, potentially leading to improved clinical decision-making and personalized treatment strategies.
• Our findings encourage further investigation into the role of deep learning and WSI analysis in SS diagnosis and risk stratification.
• The development of a non-invasive and objective diagnostic tool based on WSI analysis could benefit clinical practice and inform policy decisions regarding patient care for SS.The development of a non-invasive and objective diagnostic tool based on WSI analysis could benefit clinical practice and inform policy decisions regarding patient care for SS.
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