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

NCT07284771 | Not Yet Recruiting

• 2 other identifiers: 2025.37423241901
• Study Type: observational
• Target: 182
• Locations: 0 countries
• Sites: N/A

Brief 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

Outcome Measures

Primary Outcomes (1)

• International Knee Documentation Committee score

  6- and 12-months post-operation

Study Arms (1)

Deficit group

Other: No Intervention: Observational Cohort

Interventions

No Intervention: Observational Cohort OTHER

no intervention

Deficit group

Eligibility Criteria

• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

All the ACLR patients will be recruited at the Orthopaedics Outpatient Clinic at Prince of Wales Hospital.

You may qualify if:

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

You may not qualify if:

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

Sponsors & Collaborators

• Chinese University of Hong Kong: lead

Study Officials

• Shu Hang YUNG

  Chinese University of Hong Kong

  PRINCIPAL INVESTIGATOR

Central Study Contacts

muriel XIAO

CONTACT

(852)35053311; lingqingxiao@cuhk.edu.hk

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor and Chairman, Department of Orthopaedics & Traumatology, Faculty of Medicine, The Chinese University of Hong Kong

Study Record Dates

• First Submitted: December 3, 2025
• First Posted: December 16, 2025
• Study Start: April 1, 2026
• Primary Completion (Estimated): March 31, 2028
• Study Completion (Estimated): August 31, 2028
• Last Updated: December 16, 2025

Record last verified: 2025-12