Deep contrastive learning improves identification of early‐stage knee osteoarthritis across multicohort X‐ray datasets

作者：Ligan Jia, Guangyuan Du, Zijuan Fan, Xiaoke Li, Haifeng Liu, Jing Zhang, Dijun Li, Lei Yan, Jingwei Jiu, Ruoqi Li, Songyan Li, Yiqi Yang, Huachen Liu, Yijia Ren, Xuanbo Liu, Jiao Jiao Li, Yuqing Zhang, Jianhao Lin, Bin Wang · 发表于：Knee Surgery Sports Traumatology Arthroscopy · 年份：2025 · DOI：10.1002/ksa.70191 · 被引用次数：1 · 研究领域：Osteoarthritis Treatment and Mechanisms、Total Knee Arthroplasty Outcomes、Rheumatoid Arthritis Research and Therapies

PURPOSE: To develop a Kellgren-Lawrence (K-L) grading recognition framework for knee osteoarthritis (KOA) with enhanced capability for early-stage detection and to validate its transferability across three independent cohorts. METHODS: Weight-bearing anteroposterior knee radiographs were obtained from three datasets: the osteoarthritis initiative (OAI), Wuchuan and Shunyi. The OAI dataset included baseline, 72-month, and 96-month follow-up images, while the Wuchuan and Shunyi datasets were collected from Wuchuan (China) and Shunyi District (Beijing), respectively. Contrastive learning was incorporated into model training to construct the Augmented Dataset-Wide-ResMRnet-Contrastive Loss-Cross Entropy (AW2C) framework. RESULTS: The AW2C framework achieved overall classification accuracies of 83.0%, 82.0% and 80.5% on the OAI, Wuchuan and Shunyi datasets, respectively, with corresponding area under the curve (AUC) of 97.0%, 96.7% and 95.6%. Compared with the baseline model, accuracy for K-L grade 2 improved from 64% to 80%, and discrimination between K-L grades 1 and 2 was notably enhanced. CONCLUSIONS: The proposed AW2C framework demonstrated robust and transferable performance for automated radiographic K-L grading of KOA, particularly improving recognition of early-stage and suspected disease. With further optimisation, it holds promise as a reliable tool for large-scale studies and clinical decision support. LEVEL OF EVIDENCE: Level III.