MedTrace Logo

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

NCT07284771 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 182

Last updated 2025-12-16

No results posted yet for this study

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

OTHER

No Intervention: Observational Cohort

no intervention

Sponsors & Collaborators

  • Chinese University of Hong Kong

    lead OTHER

Principal Investigators

  • Shu Hang YUNG · Chinese University of Hong Kong

Eligibility

Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2026-04-01
Primary Completion
2028-03-31
Completion
2028-08-31

More Related Trials

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07284771 on ClinicalTrials.gov