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

The Chinese University of Hong Kong

Status: Begins enrollment in 1 month

Conditions: Machine Learning

Treatments: Other: No Intervention: Observational Cohort

Study type: Observational

Funder types: Other

Identifiers

NCT07284771

23241901 (Other Grant/Funding Number)

2025.374

Details and patient eligibility

About

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.

Enrollment: 182 estimated patients

Sex: All

Volunteers: Accepts Healthy Volunteers

Inclusion criteria

• Unilateral ACL injury and plan for ACLR
• Commit the post-operation physiotherapy in Prince of Wales Hospital

Exclusion criteria

• Preoperative radiographic signs of arthritis
• Patient non-compliance to the rehabilitation program

Trial design

182 participants in 1 patient group

Deficit group

Treatment:

Other: No Intervention: Observational Cohort