Machine Learning in Sports Medicine. Risk Assessment of Patellar Tendinopathy in Elite Athletes
DOI:
https://doi.org/10.63403/re.v32i2.409Keywords:
chronic patellar tendon injury, artificial intelligence, machine learning, score, elite athletesAbstract
Introduction: patellar tendinopathy is prevalent among elite athletes, particularly in sports involving frequent jumping like basketball. Early identification of at-risk players remains a clinical challenge, where machine learning could offer innovative solutions.
Objectives: to develop a machine learning model to predict the risk of patellar tendinopathy in basketball players using clinical, biomechanical, anthropometric, and training load variables.
Materials and methods: a cross-sectional observational study was conducted on eighty-one professional and semiprofesional basketball players. Clinical (VISA-P, VAS), ultrasound (AP5 and AP10), anthropometric, and functional (SLS) data were collected. The Random Forest algorithm was used with 10-fold cross-validation in Python.
Results: the model achieved a 93.94% accuracy in the test set. The most influential variable was VISA-P (55.3%), followed by AP10 and VAS. The confusion matrix showed high sensitivity and specificity with minimal false negatives. A simplified decision tree enhanced clinical applicability.
Conclusion: the Random Forest model achieved accuracy above 93% and enabled early identification of patellar tendinopathy risk in basketball players, highlighting the VISA-P score and AP10 as the main predictors. Its accuracy and interpretability make it a valuable tool for personalized prevention in elite sports settings.
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Copyright (c) 2025 Facundo M. Segura, Florencio P. Segura, María P. Lucero Zudaire, Agustina Trevisson, Florencio V. Segura
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