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.
Highlights
Basketball is one of the most widespread team sports with more than 450 million amateur and professional players in the world [1]
The results of the Landing Error Score System (LESS) analysis led to the identification of 26 athletes belonging to the class no risk” (NR) and the remaining 13 to the class R
With the aim to assess the feasibility of using a machine-learning algorithm for the identification of basketball players associated with a higher risk of anterior cruciate ligament injury, we compared nine different classifiers fed with data related to leg stability, leg mobility and load absorption capability
Summary
Basketball is one of the most widespread team sports with more than 450 million amateur and professional players in the world [1]. Basketball is a vertical sport, which requires jumping and landing activities two or three times more often than other team games, such as soccer and volleyball [2] These aspects lead to a high incidence of injuries among basketball players; the knee joint has been demonstrated as the most commonly stressed and injured body area [3]. ACL injury, together with ankle sprains, has the main incidence in female basketball players; 16% of them may incur an ACL injury during their career It is worth highlighting, as the risk of ACL rupture in female players is up to eight times more than male players [5]. Several studies have been conducted to understand the main underlying risk causes, as the combination of anatomical, physiological and biomechanical factors can cause an increase in the injury occurrence [6,7,8]
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