An Educational App for ACL Injury Risk Assessment Using Machine Learning: A Proof of Concept

ACL Injury Assessment Using ML/AI

Abstract

ACL injuries remain a significant concern in both male and female populations, with incidence rates showing little decline despite extensive research and prevention programs. This suggests a need for novel, accessible methods to tackle this issue. In this proof-of-concept (POC) study, we introduce a simple, web-based application designed to educate individuals at risk of ACL injuries. Using machine learning (ML) techniques, specifically Google’s Teachable Machine, the app provides real-time feedback on movement patterns, classifying them as high-risk or low-risk for injury. While this study is limited by the size and diversity of its dataset, it demonstrates the potential of ML and AI models in enhancing education and injury prevention. Future iterations with larger datasets and advanced AI techniques could improve the app’s precision, scalability, and applicability in real-world scenarios. We hypothesize that ACL injuries, like many others, can be reduced through proper education, personalized feedback, and training. This POC lays the foundation for future efforts in injury prevention by combining ML-driven insights with accessible, user-friendly tools. 

Check out the ACL-IQ app: acl-iq.netlify.app
Watch the tutorial: YouTube Video