Machine learning techniques to predict and manage knee injury in sports medicine

Abstract

The aim of this study is to conduct a complete review of the current state of Machine Learning (ML) in injury prediction and prevention. In recent years, there has been a growing importance in the application of ML techniques to find out and reduce risks associated with injuries, particularly in high-risk industries such as sports, healthcare, and manufacturing. The essential part of our body is the knee, sports persons commonly injuries during play games. Sports injuries result in stress & strain connected with athletic events. Sports wounds can affect soft tissue (ligaments, muscles, cartilage, and tendons). Injuries are common in sports and can have significant physical, psychological, and financial consequences. The aim of our study was therefore to perform a systematic review of Machine learning (ML) techniques that could be used to improve injury prediction and prevention in sports. ML algorithms play a crucial role in extracting accurate information from given images and they also handle the complex pattern of MRI knee-related clarifications. In this paper, discuss a real-life imagery rule, ML design used to recognize meniscus tears, bone marrow edema, and general abnormalities on knee MRI tests accessible. The final evaluation demonstrated the highest accuracy achieved by the support vector machine, closely followed by the KNN model and the RF Tree method, all yielding comparable performance levels.

Keywords: MRI images, SVM, Random Forest, KNN, Automated analysis, ACL Injury, Machine Learning