PROTO researchers Matej Skrobot, Georg Duda and Nicholas Brisson will be presenting their novel machine learning-based classification of pathological gait in persons with anterior cruciate ligament reconstruction (ACLR) at the OARSI 2024 congress.
Presentation summary
ACLR can result in altered gait patterns, leading to knee osteoarthritis (OA). In ACLR patients, machine learning may identify gait features associated with knee OA onset. Here, we applied machine learning techniques to derive a biomechanical score discriminating pathological gait in ACLR patients from healthy gait, and examined the relationship between this novel biomechanical score and the 1-year change in tibiofemoral cartilage thickness. A higher score was associated with greater cartilage thickness loss, indicating that greater gait abnormalities were linked to increased knee cartilage degeneration. This novel biomechanical score provides a new metric that independently quantifies the severity of gait pathology in ACLR patients. This score can be used to classify ACLR patients, and may prove useful in implementing personalized therapies to correct aberrant gait patterns implicated in post-traumatic knee OA.