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Classification algorithms determine the similarity of an observation to defined classes, e.g., injured or healthy athletes, and can highlight treatment targets or assess progress of a treatment. The primary aim was to cross-validate a previously developed classification algorithm using a different sample, while a secondary aim was to examine its ability to predict future ACL injuries. The examined outcome measure was ‘healthy-limb’ class membership probability, which was compared between a cohort of athletes without previous or future (No Injury) previous (PACL) and future ACL injury (FACL). The No Injury group had significantly higher probabilities than the PACL (p &lt; 0.001; medium effect) and FACL group (p ≤ 0.045; small effect). The ability to predict group membership was poor for the PACL (area under curve [AUC]; 0.61&lt;AUC&lt;0.62) and FACL group (0.57&lt;AUC&lt;0.59). The ACL injury incidence proportion was highest in athletes with probabilities below 0.20 (9.4%; +2.7% to baseline), while athletes with probabilities above 0.80 had an incidence proportion of 4.1% (−2.6%). While findings that a low probability might represent an increase in injury risk on a group level, it is not sensitive enough for injury screening to predict a future injury on the individual level.

sports biomechanics

Cross-validation of a machine learning algorithm that determines anterior cruciate ligament rehabilitation status and evaluation of its ability to predict future injury