Knee Surg Sports Traumatol Arthrosc. 2025 Nov 14. doi: 10.1002/ksa.70191. Online ahead of print.
ABSTRACT
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.
PMID:41235478 | DOI:10.1002/ksa.70191
