ArticleScientific reports2022
Leveraging explainable machine learning to identify gait biomechanical parameters associated with anterior cruciate ligament injury.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Explainable artificial intelligence for gait analysis: advances, pitfalls, and challenges - a systematic review.Frontiers in bioengineering and biotechnology · 2025Pooled it
- Identifying Gait-Related Functional Outcomes in Post-Knee Surgery Patients Using Machine Learning: A Systematic Review.International journal of environmental research and public health · 2022Pooled it
- The Effects of a Single Session of a Rhythmic Movement Program on Selected Biopsychological Parameters in PD Patients: A Methodological Approach.Medicina (Kaunas, Lithuania) · 2023Trial
- Explainable Machine Learning Using Sensor-Derived Biomechanical Features to Classify Elevated VALR-Related Loading Across Midsole Hardness Conditions in School-Aged Boys.Sensors (Basel, Switzerland) · 2026Article
- 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
- Advances in Gait Alterations and Rehabilitation After Anterior Cruciate Ligament Reconstruction: Biomechanics and Emerging Technologies.Orthopaedic surgery · 2026Review
- Quantitative comparison of explainable AI methods for interpreting deep learning-based classification of 3D gait kinematics.Scientific reports · 2026Article
- Classification of anterior cruciate ligament injury profiles through running analysis: a machine learning approach.Scientific reports · 2026Article
- Segmentation and diagnosis of anterior cruciate ligament tear using deep learning and radiomics based on knee CT.BMC medical imaging · 2026Article
- A comprehensive review of explainable artificial intelligence in healthcare methods, evaluation, and clinical integration.iScience · 2026Review
- Utilization of machine learning to identify lower extremity biomechanical predictors of rupture in a validated cadaveric model of ACL injury.Scientific reports · 2026Article
- Human-centered evaluation of statistical parametric mapping and explainable machine learning for outlier detection in plantar pressure data.Scientific reports · 2026Article
- Article
- Key biomechanical features of jump-landing under cognitive dual-task conditions: an XGBoost-SHAP-based explainable machine learning analysis.Frontiers in bioengineering and biotechnology · 2026Article
- Feasibility of explainable machine learning for analyzing drop landing strategies: effects of landing height and fatigue.Frontiers in physiology · 2026Article
- Biomechanical Assessment of Syndesmotic and Deltoid Ligament Strain in Pronation-External Rotation Type Ankle Injuries by Musculoskeletal Computer Simulation.Foot & ankle orthopaedics · 2026Article
- The Clinical Significance of the Manchester Colour Wheel in a Sample of People Treated for Insured Injuries.Journal of clinical medicine · 2025Article
- Explainable machine learning to predict prolonged post-operative opioid use in rotator cuff patients.BMC musculoskeletal disorders · 2025Article
- Scoping Review of Machine Learning Techniques in Marker-Based Clinical Gait Analysis.Bioengineering (Basel, Switzerland) · 2025Review
- A machine learning approach to identify stride characteristics predictive of musculoskeletal injury, enforced rest and retirement in Thoroughbred racehorses.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
9 authors.
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Abstract
Anterior cruciate ligament (ACL) deficient and reconstructed knees display altered biomechanics during gait. Identifying significant gait changes is important for understanding normal and ACL function and is typically performed by statistical approaches. This paper focuses on the development of an explainable machine learning (ML) empowered methodology to: (i) identify important gait kinematic, kinetic parameters and quantify their contribution in the diagnosis of ACL injury and (ii) investigate the differences in sagittal plane kinematics and kinetics of the gait cycle between ACL deficient, ACL reconstructed and healthy individuals. For this aim, an extensive experimental setup was designed in which three-dimensional ground reaction forces and sagittal plane kinematic as well as kinetic parameters were collected from 151 subjects. The effectiveness of the proposed methodology was evaluated using a comparative analysis with eight well-known classifiers. Support Vector Machines were proved to be the best performing model (accuracy of 94.95%) on a group of 21 selected biomechanical parameters. Neural Networks accomplished the second best performance (92.89%). A state-of-the-art explainability analysis based on SHapley Additive exPlanations (SHAP) and conventional statistical analysis were then employed to quantify the contribution of the input biomechanical parameters in the diagnosis of ACL injury. Features, that would have been neglected by the traditional statistical analysis, were identified as contributing parameters having significant impact on the ML model's output for ACL injury during gait.
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