A Machine-Learning Approach to Measure the Anterior Cruciate Ligament Injury Risk in Female Basketball Players

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.

Machine Learning-Based Performance Comparison to Diagnose Anterior Cruciate Ligament Tears

“…The early detection of knee OA and OP disease progression is complex and challenging in the case of classification problems [ 26 , 27 ]. The machine learning models can quantify anterior cruciate injury risk better for sports player injuries, synovial fluid of human OA knees, and joint angles prediction [ 28 – 32 ].…”

The influence of limb role, direction of movement and limb dominance on movement strategies during block jump-landings in volleyball

“…ACC is represented as the proportion of correctly classified instances among the total number of test instances. These are commonly used measures with standardized definitions, which can be found in any introduction manual to Machine Learning or introductory papers, such as Taborri et al (2021) 28 .…”

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“…SG systems in sports applications are diverse, performing data engineering ( Johnson et al, 2021 ) and data labeling ( Cronin et al, 2019 ), evaluating the role of the shoe structure on running biomechanics ( Onodera et al, 2017 ), monitoring fatigue to prevent injury ( Russell et al, 2021 ), ( Zhang J. et al, 2014 ), counting steps ( Kang et al, 2018 ), assessing physical autonomy and functional ability ( Khan et al, 2015 ), articulating real-time control of an electrical muscle stimulation (EMS) device for sports training ( Hassan et al, 2017 ), predicting and preventing injury ( Taborri et al, 2021 ), achieving multi-player tracking, identification, and re-identification ( Zhang R. et al, 2020 ), classifying and counting different sports activities ( Sundholm et al, 2014 ), and recognizing and analyzing sports behavior ( Guo and Wang, 2021 ).…”