ArticleSports medicine (Auckland, N.Z.)2022
Return to Play Prediction Accuracy of the MLG-R Classification System for Hamstring Injuries in Football Players: A Machine Learning Approach.
Article in Sports medicine (Auckland, N.Z.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 29 citations in OpenAlex.
- Imaging characterization of sports-related hamstring injuries.Skeletal radiology · 2026Review
- Clustering analysis of hamstring injuries-A pilot study to determine phenogroups based on injury characteristics and recovery duration.Journal of experimental orthopaedics · 2026Article
- Artificial intelligence and machine learning in sports medicine: mapping clinical tasks and assessing clinical maturity - a scoping review.BMC medical informatics and decision making · 2026Article
- Athletes' performance and injury management in sports training using association rules and data mining techniques.Scientific reports · 2026Article
- Development and validation of a machine learning model for non-contact injury prediction based on lower limb strength asymmetry in professional football.Scientific reports · 2026Article
- Unsupervised learning-based optimization of college students' fundamental capacity evaluation method.Frontiers in physiology · 2026Article
- Time to return to sports in football players recovering from lower limb soft tissue injuries: a systematic review with meta-analysis.BMJ open sport & exercise medicine · 2026Article
- Classification systems for assessing acute muscle injuries: a retrospective comparison of inter-reader agreements.Skeletal radiology · 2026Article
- Histological Definition of Skeletal Muscle Injury: A Guide to Nomenclature Along the Connective Tissue Sheath/Structure.Sports medicine (Auckland, N.Z.) · 2025Review
- The Structure, Function, and Adaptation of Lower-Limb Aponeuroses: Implications for Myo-Aponeurotic Injury.Sports medicine - open · 2024Review
- Performance and healthcare analysis in elite sports teams using artificial intelligence: a scoping review.Frontiers in sports and active living · 2024Article
- Managing Lower Limb Muscle Reinjuries in Athletes: From Risk Factors to Return-to-Play Strategies.Journal of functional morphology and kinesiology · 2023Review
- MRI findings prior to return to play as predictors of reinjury in professional athletes: a novel decision-making tool.Insights into imaging · 2022Article
- From injury to comeback: A systematic review of machine learning models predicting return to sport in athletes.Digital healthArticle
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Authors and funding
13 authors at 7 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectiveMuscle injuries are one of the main daily problems in sports medicine, football in particular. However, we do not have a reliable means to predict the outcome, i.e. return to play from severe injury. The aim of the present study was to evaluate the capability of the MLG-R classification system to grade hamstring muscle injuries by severity, offer a prognosis for the return to play, and identify injuries with a higher risk of re-injury. Furthermore, we aimed to assess the consistency of our proposed system by investigating its intra-observer and inter-observer reliability.
methodsAll male professional football players from FC Barcelona, senior A and B and the two U-19 teams, with injuries that occurred between February 2010 and February 2020 were reviewed. Only players with a clinical presentation of a hamstring muscle injury, with complete clinic information and magnetic resonance images, were included. Three different statistical and machine learning approaches (linear regression, random forest, and eXtreme Gradient Boosting) were used to assess the importance of each factor of the MLG-R classification system in determining the return to play, as well as to offer a prediction of the expected return to play. We used the Cohen's kappa and the intra-class correlation coefficient to assess the intra-observer and inter-observer reliability.
resultsBetween 2010 and 2020, 76 hamstring injuries corresponding to 42 different players were identified, of which 50 (65.8%) were grade 3
conclusionsThe main determinant for a long return to play after a hamstring injury is the injury affecting the connective tissue structures of the hamstring. We developed a reliable hamstring muscle injury classification system based on magnetic resonance imaging that showed excellent results in terms of reliability, prognosis capability and objectivity. It is easy to use in clinical daily practice, and can be further adapted to future knowledge. The adoption of this system by the medical community would allow a uniform diagnosis leading to better injury management.
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