Abstract
Osteoarthritis (OA) is a prevalent and disabling chronic disease, with knee OA being the most common form, affecting approximately 73% of individuals over 55 years. Traditional clinical assessments often fail to predict knee structural progression accurately, highlighting the need for improved prognostic methods. This perspective explores the complexity of stratifying knee OA patients based on rapid structural progression. It underscores the importance of such early identification to enable timely and personalized intervention and optimize disease-modifying OA drug clinical trial design, as many trial participants show minimal progression, complicating the assessment of treatment efficacy. We highlight the potential of machine learning (ML) and deep learning (DL) in overcoming this prognostic challenge, as these methodologies enhance classification/stratification capabilities by leveraging multidimensional data and capturing the intricate relationships between diverse features. These include panels of biochemical markers and imaging markers, such as those from magnetic resonance imaging (MRI), as integrating MRI data into ML/DL prognostic models enhances such prediction performance. These automated ML/DL models will offer a transformative approach to stratifying knee OA patients and represent a paradigm shift in disease management. Ultimately, ML/DL applications will not only improve patient outcomes but will also promote innovation in OA research, clinical practice, and therapeutics.