Machine Learning
Conditions
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.
Interventions
OTHERNo Intervention: Observational Cohort
no intervention
Sponsors
Chinese University of Hong Kong
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
Yes
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
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| International Knee Documentation Committee score | 6- and 12-months post-operation |
Contacts
Primary Contactmuriel XIAO
Outcome results
None listed