ArticleSensors (Basel, Switzerland)2021
A Machine-Learning Approach to Measure the Anterior Cruciate Ligament Injury Risk in Female Basketball Players.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The application of machine learning in the prediction of sports injuries: systematic review and meta-analysis.Scientific reports · 2026Pooled it
- Artificial intelligence in ACL injury prediction and prevention: a systematic review.Journal of orthopaedic surgery and research · 2026Pooled it
- Machine learning model identifies tibial anatomical variables as potential risk factors for anterior cruciate ligament injury.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2026Article
- Utilization of machine learning to identify lower extremity biomechanical predictors of rupture in a validated cadaveric model of ACL injury.Scientific reports · 2026Article
- A Preliminary Data-Driven Approach for Classifying Knee Instability During Subject-Specific Exercise-Based Game with Squat Motions.Sensors (Basel, Switzerland) · 2025Article
- From Past to Future: Emergent Concepts of Anterior Cruciate Ligament Surgery and Rehabilitation.Journal of clinical medicine · 2025Review
- Artificial Intelligence in Sports Biomechanics: A Scoping Review on Wearable Technology, Motion Analysis, and Injury Prevention.Bioengineering (Basel, Switzerland) · 2025Review
- Artificial intelligence in orthopedics: fundamentals, current applications, and future perspectives.Military Medical Research · 2025Review
- Article
- Comparison of Machine Learning Algorithms Fed with Mobility-Related and Baropodometric Measurements to Identify Temporomandibular Disorders.Sensors (Basel, Switzerland) · 2024Article
- The Role of Artificial Intelligence in Anterior Cruciate Ligament Injuries: Current Concepts and Future Perspectives.Healthcare (Basel, Switzerland) · 2024Review
- Non-contact lower limb injuries in Rugby Union: A two-year pattern recognition analysis of injury risk factors.PloS one · 2024Article
- WARNING: A Wearable Inertial-Based Sensor Integrated with a Support Vector Machine Algorithm for the Identification of Faults during Race Walking.Sensors (Basel, Switzerland) · 2023Article
- Measuring the Immediate Effects of High-Intensity Functional Training on Motor, Cognitive and Physiological Parameters in Well-Trained Adults.Sensors (Basel, Switzerland) · 2023Article
- A scoping review of portable sensing for out-of-lab anterior cruciate ligament injury prevention and rehabilitation.NPJ digital medicine · 2023Article
- Predicting ACL Injury Using Machine Learning on Data From an Extensive Screening Test Battery of 880 Female Elite Athletes.The American journal of sports medicine · 2022Article
- Study on Strength and Quality Training of Youth Basketball Players.Computational and mathematical methods in medicine · 2022Article
- Artificial Intelligence Technology in Basketball Training Action Recognition.Frontiers in neurorobotics · 2022Article
- Machine Learning-Based Performance Comparison to Diagnose Anterior Cruciate Ligament Tears.Journal of healthcare engineering · 2022Article
- The influence of limb role, direction of movement and limb dominance on movement strategies during block jump-landings in volleyball.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Anterior cruciate ligament (ACL) injury represents one of the main disorders affecting players, especially in contact sports. Even though several approaches based on artificial intelligence have been developed to allow the quantification of ACL injury risk, their applicability in training sessions compared with the clinical scale is still an open question. We proposed a machine-learning approach to accomplish this purpose. Thirty-nine female basketball players were enrolled in the study. Leg stability, leg mobility and capability to absorb the load after jump were evaluated through inertial sensors and optoelectronic bars. The risk level of athletes was computed by the Landing Error Score System (LESS). A comparative analysis among nine classifiers was performed by assessing the accuracy, F1-score and goodness. Five out nine examined classifiers reached optimum performance, with the linear support vector machine achieving an accuracy and F1-score of 96 and 95%, respectively. The feature importance was computed, allowing us to promote the ellipse area, parameters related to the load absorption and the leg mobility as the most useful features for the prediction of anterior cruciate ligament injury risk. In addition, the ellipse area showed a strong correlation with the LESS score. The results open the possibility to use such a methodology for predicting ACL injury.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.