Predicting Knee Osteoarthritis Progression: A Multimodal Approach Integrating Unadorned Radiographs and Medical Data for Enhanced Machine Learning
Keywords:
Knee Osteoarthritis Progression, Multimodal, CNN, MOST, GBMAbstract
Knee osteoarthritis (OA) is a debilitating degenerative joint disease affecting millions worldwide, presenting significant challenges in patient management and healthcare resource allocation. Accurate prediction of disease progression is essential for personalized treatment strategies and timely interventions. In this study, we propose a novel multimodal approach for predicting knee OA progression, integrating clinical, imaging, and biomarker data. Leveraging advanced machine learning techniques, including deep learning and ensemble models, we demonstrate the efficacy of our approach in accurately forecasting disease progression trajectories. Our findings underscore the potential of multimodal data fusion in improving predictive modeling for knee OA progression, offering new insights for clinical decision-making and personalized patient care. Our approach achieved an average AUC of 0.810 (0.790–0.820) and AP of 0.700 (0.680–0.720) in predicting knee OA progression, outperforming existing methods and highlighting its clinical utility.
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