Knee Osteoarthritis Severity Grading Using Contrastive Learning Image Pre-Training

Background/Objectives: Accurate evaluation of knee osteoarthritis (KOA) severity is critical for optimal patient care, yet manual radiographic grading remains subject to observer variability. This study aims to evaluate the performance of a fine-tuned contrastive language-image pre-training (CLIP) framework designed to assist clinicians in grading KOA severity in plain radiographs using the Kellgren-Lawrence (KL) classification system (Grades 0-4). Methods: The model operates by projecting visual features from radiographs and standard textual clinical descriptions into a shared embedding space. Training was conducted using 8260 posterior-anterior (PA) fixed-flexion X-ray images from the Osteoarthritis Initiative (OAI) dataset. For robust external evaluation across distinct data distributions, the model was tested on an independent dataset consisting of 1650 plain radiographs. Results: When evaluated on the external validation dataset, the fine-tuned CLIP model achieved an accuracy of 76.94% and an F1-score of 76.66%. Comparative analysis demonstrates that these aligned vision-language representations provide competitive, stable diagnostic capabilities even when applied to an entirely independent data distribution. Conclusions: Fine-tuned CLIP architectures offer a viable and valuable foundation for semantically transparent, computer-aided evaluation of KOA.