RECENT advancements in machine learning (ML) are offering new opportunities to predict the onset and progression of knee osteoarthritis with greater accuracy, supporting earlier intervention and more personalized care.
Over the past decade, the application of ML to osteoarthritis research has accelerated, with models now capable of addressing multiple clinical endpoints. In clinical outcome prediction, ML has been used to anticipate changes in patient-reported measures such as pain and functional limitations. Radiographic outcomes, including progression in Kellgren Lawrence grading and joint space narrowing, have been modeled using deep learning approaches, providing automated and consistent image-based assessments.
MRI has also emerged as a rich data source for structural prediction. Deep learning tools have enabled automatic quantification of cartilage, bone marrow lesions, and subcutaneous fat, improving scalability and supporting the development of models that predict cartilage loss over time. These imaging-based models have enhanced the ability to track subtle disease changes that may precede noticeable symptoms.
ML applications have also extended to surgical outcomes, particularly in forecasting the need for total knee replacement. Models with strong predictive performance have the potential to guide surgical planning and inform patient counseling, ensuring that intervention is appropriately timed.
Emerging research directions include the integration of multimodal data from imaging, clinical records, and other health measures to improve predictive accuracy. There is also a focus on developing interpretable models that clinicians can trust, as well as leveraging automated ML tools to streamline model creation and testing. Future strategies may target prediction for specific osteoarthritis subtypes, embed ML into clinical workflows, and strengthen external validation to ensure models perform reliably across diverse patient populations.
As these methods evolve, ML holds promise for enhancing early detection, refining individual risk profiles, and guiding tailored interventions that could improve outcomes for patients with knee osteoarthritis.
Reference:
Joseph GB et al. Machine learning models for clinical and structural knee osteoarthritis prediction: Recent advancements and future directions. Osteoarthritis Cartilage Open. 2025;7(3):100654.
