Biomechanical modeling and imaging for knee osteoarthritis – is there a role for AI?

Abstract

Abstract

Objective: This mini review aims to assess the latest advancements in the field of osteoarthritis (OA) research, particularly focusing on the early detection and prediction of disease progression through the use of advanced imaging technologies utilizing biomechanical modeling and artificial intelligence (AI).

Design: The review consolidates and discusses findings from studies that utilize biomechanical modeling and/or machine learning algorithms to identify pathological changes in joint tissues indicative of OA or prediction of disease progression. It also briefly reviews future research and how these methods could be used as a part of OA management.

Results: AI algorithms have proven highly effective in recognizing the subtle changes in joint tissues associated with OA and in identifying patients at high risk for the disease. While these automated tools facilitate early diagnosis, they typically do not provide personalized intervention strategies to prevent disease progression. AI-enhanced biomechanical modeling has the potential to simulate the effects of various conservative interventions (e.g., weight management, optimal footwear, and gait retraining) on slowing OA progression, which could be pivotal for patient engagement and preventive care.

Conclusions: The integration of AI with biomechanical modeling holds significant promise for enhancing the management of OA by not only predicting disease onset and progression but also by enabling personalized intervention plans. Future research should focus on the development of these models to include personalized, preventive strategies that could effectively engage patients and potentially delay or prevent the onset of OA. This approach could revolutionize patient care by making early, targeted intervention feasible.