Machine LearningNCT07284771
MUSCLE-ML: Multimodal Integration of Muscle Strength, Structure by Machine Learning for Precision Rehabilitation After ACL Injury
An observational study of Machine Learning, run by Chinese University of Hong Kong.
Trial 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. Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Machine Learning
- How many people
- 182 participants
- Where
- No sites listed yet
- Study dates
- 1 Apr 2026to 31 Aug 2028
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Study population: All the ACLR patients will be recruited at the Orthopaedics Outpatient Clinic at Prince of Wales Hospital.
Who may be able to join
- Unilateral ACL injury and plan for ACLR
- Commit the post-operation physiotherapy in Prince of Wales Hospital
Who cannot join
- Preoperative radiographic signs of arthritis
- Patient non-compliance to the rehabilitation program
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
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?
Read more
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
- Being tested
- No Intervention: Observational Cohort
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
No Intervention: Observational CohortOther
no intervention
| Study group | Role | What they receive |
|---|---|---|
| Deficit group | Not described Receives: Other: No Intervention: Observational Cohort |
What is being measured
The primary outcome is the main question the study is designed to answer.
International Knee Documentation Committee score
Measured: 6- and 12-months post-operation
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted16 Dec 2025
- Study start1 Apr 2026 (estimated)
- Primary completion31 Mar 2028 (estimated)
- Study completion31 Aug 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Machine LearningStudies of the same condition, open and under way.
- More trials run by Chinese University of Hong KongThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 182 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Chinese University of Hong Kong
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Patrick Shu-Hang YUNG, Chinese University of Hong Kong
- Investigators
- Shu Hang YUNG, Principal investigator, Chinese University of Hong Kong
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07284771
- Sponsor's protocol ID
- 2025.374
- Other IDs
- 23241901
- Status last verified
- Dec 2025
- First submitted
- 3 Dec 2025
- Last updated
- 16 Dec 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 16 Dec 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- MUSCLE-ML: Multimodal Integration of Muscle Strength, Structure by Machine Learning for Precision Rehabilitation After ACL Injury
- ClinicalTrials.gov ID
- NCT07284771
- Acronym / sponsor ID
- 2025.374
- Conditions
- Machine Learning
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 182 participants (estimated)
- Lead sponsor
- Chinese University of Hong KongUniversity, hospital or other
- Study start
- 1 Apr 2026 (estimated)
- Primary completion
- 31 Mar 2028 (estimated)
- Study completion
- 31 Aug 2028 (estimated)
- First posted
- 16 Dec 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Deficit group | – | Other: No Intervention: Observational Cohort |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- All the ACLR patients will be recruited at the Orthopaedics Outpatient Clinic at Prince of Wales Hospital.
Key inclusion criteria (2 listed)
- Unilateral ACL injury and plan for ACLR
- Commit the post-operation physiotherapy in Prince of Wales Hospital
Key exclusion criteria (2 listed)
- Preoperative radiographic signs of arthritis
- Patient non-compliance to the rehabilitation program
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| No Intervention: Observational Cohortno intervention | Other | Deficit group |
Primary outcome
International Knee Documentation Committee score
Time frame: 6- and 12-months post-operation
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 16 Dec 2025.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Shu Hang YUNG principal investigator
Chinese University of Hong Kong
Questions about this trial
What is this clinical trial studying?
An observational study of Machine Learning, run by Chinese University of Hong Kong.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 16 Dec 2025. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact muriel XIAO ((852)35053311, lingqingxiao@cuhk.edu.hk), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
International Knee Documentation Committee score (time frame: 6- and 12-months post-operation). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 16 Dec 2025.