A Prediction Model for Primary Anterior Cruciate Ligament Injury Using Artificial Intelligence.

Background: Supervised machine learning models in artificial intelligence (AI) have been increasingly used to predict different types of events. However, their use in orthopaedic surgery has been limited.

Hypothesis: It was hypothesized that supervised learning techniques could be used to build a mathematical model to predict primary anterior cruciate ligament (ACL) injuries using a set of morphological features of the knee.

Lateral Meniscus Height and ACL Reconstruction Failure: A Nested Case-Control Study.

Previous work has shown that the morphology of the knee joint is associated with the risk of primary anterior cruciate ligament (ACL) injury. The objective of this study is to analyze the effect of the meniscal height, anteroposterior distance of the lateral tibial plateau, and other morphological features of the knee joint on risk of ACL reconstruction failure. A nested case-control study was conducted on patients who underwent an ACL reconstruction surgery during the period between 2008 and 2015.