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The desire to regain a comparable level of mobility or sporting performance, without apprehension and without the risk of re-injury, after Anterior Cruciate Ligament (ACL) reconstruction is legitimate, yet it remains rarely achieved. To date, no Return To Sport (RTS) protocol has been fully validated. The criteria currently employed lack standardization and, at times, objectivity, which limits their clinical applicability. Although the literature emphasizes the importance of evaluating performance, muscle strength, and psychological readiness, the predictive validity of these parameters remains uncertain. Moreover, the time to RTS, which varies widely (from six months to over a year), is not a reliable criterion in itself but could instead be considered a target variable within a predictive model. This perspective aligns with the RTS continuum, which differentiates between return to activity, RTS and return to performance. The present article reviews existing criteria and examines the potential contribution of machine learning models to improving RTS prediction.
