MACHINE LEARNING-BASED BIOMECHANICAL SCORE OF PATHOLOGICAL GAIT IN PERSONS WITH ANTERIOR CRUCIATE LIGAMENT RECONSTRUCTION

Purpose (the aim of the study): Anterior cruciate ligament (ACL) ruptures often result in altered gait and may lead to knee osteoarthritis (OA). While ACL reconstruction (ACLR) is often advised, it may not prevent post-traumatic knee OA. Machine learning (ML) techniques are beneficial for analyzing gait biomechanics data that comprise many characteristic kinematic and kinetic waveforms, and can unbiasedly distinguish between individuals or groups based on waveform features. In ACLR patients, ML may help to identify abnormal gait features associated with the knee OA onset.