MUSCLE-ML: Multimodal Integration of Muscle Strength, Str... | NCT07284771

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

NCT07284771

Sponsor

Chinese University of Hong Kong

Status

Not yet recruiting

Last Update Posted

Dec 16, 2025Actual

Enrollment

182Estimated

Phase

Not provided

Conditions

Machine Learning

Interventions

No Intervention: Observational Cohort

Countries

Not provided

Protocol Section

Identification Module Status Module Sponsor/Collaborators Module Oversight Module Description Module Conditions Module Design Module Arms/Interventions Module Outcomes Module Eligibility Module Contacts/Locations Module References Module IPD Sharing Statement Module

Results Section

Participant Flow Module Baseline Characteristics Module Outcome Measures Module Adverse Events Module More Info Module

Annotation Section

Annotation Module

Document Section

Large Document Module

Derived Section

Miscellaneous Info Module Condition Browse Module Intervention Browse Module

Protocol Section

Identification Module

NCT ID

Results Section

No data available

No data is available for this block.

Annotation Section

No data available

No data is available for this block.

Document Section

No data available

No data is available for this block.

Derived Section

Miscellaneous Info Module

Version Holder

ID	Type	Description	Link
23241901	Other Grant/Funding Number	Health and Medical Research Fund

Status Module

Record Verification Date

Dec 2025

Overall Recruitment Status or Expanded Access Status

Not yet recruiting

Last Known Status

Not provided

Delayed Posting

Not provided

Why Stopped

Not provided

Expanded Access Info

Start Date

Apr 1, 2026Estimated

Primary Completion Date

Mar 31, 2028Estimated

Completion Date

Aug 31, 2028Estimated

First Submitted Date

Dec 3, 2025

First Submission Date that Met QC Criteria

Dec 3, 2025

First Posted Date

Dec 16, 2025Actual

Results Waived

Not provided

Results First Submitted Date

Not provided

Results First Submitted that Met QC Criteria

Not provided

Results First Posted Date

Not provided

Certification/Extension (aka Delayed Results) First Submitted Date

Not provided

Certification/Extension First Submitted that Passed QC Review

Not provided

Certification/Extension First Posted Date

Not provided

Last Update Submitted Date

Dec 3, 2025

Last Update Posted Date

Dec 16, 2025Actual

Sponsor/Collaborators Module

Responsible Party, by Official Title

Patrick Shu-Hang YUNG, Professor and Chairman, Department of Orthopaedics & Traumatology, Faculty of Medicine, The Chinese University of Hong Kong, Chinese University of Hong KongPrincipal Investigator

Lead Sponsor

Chinese University of Hong KongOTHER

Collaborators

Not provided

Oversight Module

Has Data Monitoring Committee (DMC)

Is FDA Regulated Drug

Is FDA Regulated Device

Is Unapproved Device

Not provided

Pediatric Postmarket Surveillance of a Device Product

Not provided

Product Exported from US

Not provided

FDAAA801 Violation

Not provided

Description Module

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.

Detailed Description

Not provided

Design Module

Study Type

Observational

Number of References to an Expanded Access Study

Not provided

Expanded Access Types

Not provided

Patient Registry

Target Follow-Up Duration

Not provided

Phases

Not provided

Interventional Study Design

Allocation

Not provided

Intervention Model

Biospecimen

No data available

No data is available for this block.

Enrollment

182Estimated

Arms/Interventions Module

Arm Groups

Label	Type	Description	Intervention Names
Deficit group			Other: No Intervention: Observational Cohort

Interventions

Name	Type	Description	Arm Group Labels	Other Names
No Intervention: Observational Cohort	Other	no intervention	Deficit group

Outcomes Module

Primary Outcomes

Measure	Description	Time Frame
International Knee Documentation Committee score		6- and 12-months post-operation

Secondary Outcomes

Not provided

Other Outcomes

Not provided

Eligibility Module

Eligibility Criteria

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

Accepts Healthy Volunteers

Yes

Sex

All

Sex/Gender Based

Not provided

Sex/Gender Description

Not provided

Minimum Age

Not provided

Maximum Age

Not provided

Standard Ages

ChildAdultOlder Adult

Study Population

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

Sampling Method

Probability Sample

Contacts/Locations Module

Central Contacts

Name	Role	Phone	Extension	Email
muriel XIAO	Contact	(852)35053311		lingqingxiao@cuhk.edu.hk

Overall Officials

Name	Affiliation	Role
Shu Hang YUNG	Chinese University of Hong Kong	Principal Investigator

Locations

Not provided