ArticleThe American journal of sports medicine2020
Linear Discriminant Analysis Successfully Predicts Knee Injury Outcome From Biomechanical Variables.
Article in The American journal of sports medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 19 citations in OpenAlex.
- Trial
- 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
- Post-Acute Concussion-Related Differences in Neuromotor Control Measured Using a Low-Cost Movement Assessment System: A Feasibility Study.International journal of sports physical therapy · 2025Article
- Predicting factors for extremity fracture among border-fall patients using machine learning computing.Heliyon · 2024Article
- Construction and validation of machine learning algorithm for predicting depression among home-quarantined individuals during the large-scale COVID-19 outbreak: based on Adaboost model.BMC psychology · 2024Article
- Development and validation of a prediction tool for intraoperative blood transfusion in brain tumor resection surgery: a retrospective analysis.Scientific reports · 2023Article
- New parameters measured via preoperative tonsil photos to evaluate the post-tonsillectomy pain: an analysis assisted by machine learning.Gland surgery · 2023Article
- Neuromotor control associates with muscle weakness observed with McArdle sign of multiple sclerosis.Annals of clinical and translational neurology · 2022Article
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundThe most commonly damaged structures of the knee are the anterior cruciate ligament (ACL), medial collateral ligament (MCL), and menisci. Given that these injuries present as either isolated or concomitant, it follows that these events are driven by specific mechanics versus coincidence. This study was designed to investigate the multiplanar mechanisms and determine the important biomechanical and demographic factors that contribute to classification of the injury outcome. HYPOTHESIS: Linear discriminant analysis (LDA) would accurately classify each injury type generated by the mechanical impact simulator based on biomechanical input variables (ie, ligament strain and knee kinetics). STUDY
designControlled laboratory study.
methodsIn vivo kinetics and kinematics of 42 healthy, athletic participants were measured to determine stratification of injury risk (ie, low, medium, and high) in 3 degrees of knee forces/moments (knee abduction moment, anterior tibial shear, and internal tibial rotation). These stratified kinetic values were input into a cadaveric impact simulator to assess ligamentous strain and knee kinetics during a simulated landing task. Uniaxial and multiaxial load cells and implanted strain sensors were used to collect mechanical data for analysis. LDA was used to determine the ability to classify injury outcome by demographic and biomechanical input variables.
resultsFrom LDA, a 5-factor model (Entropy
conclusionUse of LDA accurately predicted the outcome of knee injury from kinetic data from cadaveric simulations with the use of a mechanical impact simulator at 25° of knee flexion. Thus, with clinically relevant kinetics, it is possible to determine clinical risk of injury and also the likely presentation of singular or concomitant knee injury. CLINICAL RELEVANCE: LDA demonstrates that injury outcomes are largely characterized by specific mechanics that can distinguish ACL, MCL, and medial meniscal injury. Furthermore, as the mechanics of injury are better understood, improved interventional prehabilitation can be designed to reduce these injuries.
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