Artificial intelligence in ACL injury prediction and prevention: a systematic review

Abstract

BACKGROUND: Anterior cruciate ligament (ACL) injuries are prevalent in sports, with significant physical, economic, and long-term health impacts. Artificial intelligence (AI) offers promising solutions for predicting and preventing ACL injuries through advanced data analysis. This systematic review evaluates AI applications in ACL injury prediction and prevention, focusing on techniques, performance metrics, sports contexts, and intervention effectiveness. METHODS: Following PRISMA 2020 guidelines, we searched PubMed, Scopus, Google Scholar, and Web of Science from inception to November 1, 2025 for peer-reviewed studies in English using AI for ACL injury prediction or prevention. Studies were excluded if they focused on other injuries or were non-original research. Two reviewers independently screened articles, extracted data (e.g., AI techniques, sample size, outcomes), and assessed methodological quality using the PROBAST + AI. Narrative synthesis was conducted due to methodological heterogeneity. RESULTS: : 0.9947-0.9992). Input data ranged from biomechanical parameters to video-based knee angles. PROBAST + AI demonstrated low ROB, indicating robust methodological quality for development. CONCLUSION: AI demonstrates significant potential in predicting ACL injury risk and informing prevention strategies through biomechanical and kinematic analyses. However, small sample sizes, heterogeneous methodologies, and practical barriers (e.g., equipment costs) limit clinical adoption. Future research should focus on larger, diverse cohorts and standardized protocols to enhance generalizability and implementation. REGISTRY NUMBER: CRD420251230914.

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