Increased Preactivation and Cocontraction in ACL-Reconstructed Athletes: Insights from AI-Based EMG Analysis.

INTRODUCTION

Anterior cruciate ligament (ACL) tears disrupt the neural structures within the ligament, impairing the neuromuscular control of knee-stabilizing muscles. Consequently, muscle activity patterns are a crucial area of research in return-to-sport evaluation after ACL reconstruction (ACL-R). Artificial intelligence-based methods may help identify and extract meaningful parameters from these patterns. This study aims at accurately and reliably quantifying knee muscle preactivation and timing-based cocontraction indexes (CCIs) in athletes with and without ACL-R during sports-specific landing tasks using an artificial intelligence approach.

METHODS

Eleven athletes with ACL-R and 18 control athletes (CA) performed two landing tasks: single-leg hop and single-leg cross drop landing. EMG signals were recorded bilaterally from four knee-stabilizing muscles: biceps femoris (BF), semitendinosus (ST), vastus lateralis, and vastus medialis. Muscle preactivation onsets before landing and timing-based CCIs were computed through a pretrained deep-learning-based muscle activity detector (LSTM-MAD). To ensure comparability with state-of-the-art approaches, amplitude-based EMG CCIs were also computed.

RESULTS

LSTM-MAD estimated muscle preactivation onset with an error under 23 ms compared with manual segmentations by three experts. During single-leg hops, ACL-R athletes exhibited significantly greater BF (ACL-R: -193 ± 12 ms; CA: -152 ± 5 ms; P = 0.002) and ST (ACL-R: -189 ± 11 ms; CA: -140 ± 4 ms; P < 0.001) preactivations and longer cocontraction durations (ACL-R: 69% ± 2%; CA: 54% ± 2%; P < 0.001) compared with CA. During single-leg cross drop landings, ACL-R athletes showed greater BF (ACL-R: -234 ± 25 ms; CA: -150 ± 11 ms; P = 0.02) preactivations and longer BF and ST cocontractions (ACL-R: 55% ± 5%; CA: 35% ± 2%; P = 0.003) compared with CA. At return to sport, ACL-R athletes demonstrated greater BF and ST preactivation and cocontraction during landing tasks.

CONCLUSIONS

Integrating artificial intelligence-based methods to assess neuromuscular control could improve the effectiveness of rehabilitation protocols, facilitating safer return-to-sport decisions.