Semi-supervised graph-based deep learning for multi-modal prediction of knee osteoarthritis incidence

Abstract

Purpose: Osteoarthritis (OA) is a multifactorial disorder accompanied by biochemical and morphological changes in the articular cartilage, modulated by skeletal biomechanics and gait. While we can acquire detailed information about OA patients, we are not yet able to leverage the multifactorial factors for diagnosis and disease management of knee OA. Recent advancements in machine learning can help in achieving this goal; however, obstacles in collection of large enough samples to train complex models and lack of established methods for multi-modal data fusion make the prediction of future OA onset still an unmet challenge.