Anterior Cruciate Ligament Tear Detection: ML and DL Approaches

Anterior cruciate ligament (ACL) injuries are a major problem in sports and physical activities; prompt and precise diagnosis is generally necessary for appropriate treatment and recovery. Even if they are trustworthy, traditional diagnostic techniques can be expensive, time-consuming, and prone to human mistake. Artificial intelligence (AI) technologies, including machine learning (ML) and deep learning (DL), are viable options for ACL injury categorization and detection. This study summarizes the state-of-the-art research on the use of ML and DL for ACL injury identification, emphasizing different approaches, datasets, and performance measures. We explore the benefits and drawbacks of current methods and point out directions for further study to improve clinical integration and diagnostic precision.