MUSCLE-ML: Multimodal Integration of Muscle Strength, Structure by Machine Learning for Precision Rehabilitation After ACL Injury
NCT ID: NCT07284771
Last Updated: 2025-12-16
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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NOT_YET_RECRUITING
182 participants
OBSERVATIONAL
2026-04-01
2028-08-31
Brief Summary
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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.
Detailed Description
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Conditions
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Study Design
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COHORT
PROSPECTIVE
Study Groups
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Deficit group
No Intervention: Observational Cohort
no intervention
Interventions
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No Intervention: Observational Cohort
no intervention
Eligibility Criteria
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Inclusion Criteria
* Commit the post-operation physiotherapy in Prince of Wales Hospital
Exclusion Criteria
* Patient non-compliance to the rehabilitation program
ALL
Yes
Sponsors
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Chinese University of Hong Kong
OTHER
Responsible Party
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Patrick Shu-Hang YUNG
Professor and Chairman, Department of Orthopaedics & Traumatology, Faculty of Medicine, The Chinese University of Hong Kong
Principal Investigators
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Shu Hang YUNG
Role: PRINCIPAL_INVESTIGATOR
Chinese University of Hong Kong
Central Contacts
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Other Identifiers
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23241901
Identifier Type: OTHER_GRANT
Identifier Source: secondary_id
2025.374
Identifier Type: -
Identifier Source: org_study_id