NCT07284771

MUSCLE-ML: Multimodal Integration of Muscle Strength, Structure by Machine Learning for Precision Rehabilitation After ACL Injury

• Status: Not Yet Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 182 (estimated)
• Sponsor: Chinese University of Hong Kong · Academic / Other
• Sex: All
• Age: —
• Healthy volunteers: Accepted

Summary

The goal of this clinical trial is to use machine learning (ML) to predict functional recovery by integrating muscle-related factors and other relevant parameters for identification of non-responders to conventional rehabilitation. The main questions it aims to answer are: Do deficit clusters lead to poorer functional recovery compared to non-deficit clusters? Does an ML-derived composite score that integrates quadriceps/hamstring strength and size outperform isolated metrics in predicting RTP success? Researchers will compare deficit clusters against non-deficit clusters to determine if deficit clusters lead to poorer functional recovery. Participants will: Return for 5 follow-up timepoints in total for PRO and functional assessments including pre-operation, 1-, 3-, 6- and 12-months post-operation.

Conditions

• Machine Learning

Interventions

Type	Name	Description
OTHER	No Intervention: Observational Cohort	no intervention

Timeline

• Start date: 2026-04-01
• Primary completion: 2028-03-31
• Completion: 2028-08-31
• First posted: 2025-12-16
• Last updated: 2025-12-16