Uncertainty-Quantified and Explainable Age- and Sex-Aware Contrastive Learning for Knee Osteoarthritis Classification

Fengyi Gao, Farnaz Rezvani, Yushui Han, Michael Kann, Nickolas Littlefield, Hilal Maradit Kremers, Adolph J.Yates, Johannes F. Plate, Ahmad P. Tafti

Keywords: UNCERTAINTY-AWARE AI, KNEE OA, PLAIN RADIOGRAPHS, AI EXPLAINABILITY

TL;DR: Integrating demographic awareness, uncertainty quantification, and explainable contrastive learning into a unified computational framework to classify Knee OA

Abstract: Automated knee osteoarthritis (OA) assessment demands models that are not only accurate, but also trustworthy and demographically robust. To meet this need, we propose XAS-SupCon, an uncertainty-aware, explainable, and age- and sex-aware supervised contrastive learning framework. Using plain knee radiographs from the Osteoarthritis Initiative (OAI), we incorporate age and sex directly into the contrastive objective to strengthen representation learning while mitigating demographic bias. Uncertainty is quantified via Monte Carlo Dropout with risk–coverage analysis and selective prediction. Compared with conventional CNN and contrastive baselines, XAS-SupCon achieves the highest accuracy (0.8419) and F1-score (0.8192), while maintaining lower risk across coverage levels, supporting more reliable and explainable AI-driven knee OA assessment.

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Submission Number: 3