[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100614836":3},{"organization":4,"armGroups":7,"interventions":13,"overallOfficials":18,"centralContacts":22,"locations":10,"responsibleParty":28,"collaborators":10,"id":31,"slug":32,"hasResults":33,"nctId":34,"briefTitle":35,"officialTitle":35,"acronym":10,"eligibilityCriteria":36,"healthyVolunteers":37,"sex":38,"minAge":10,"maxAge":10,"enrollmentInfo":39,"targetDuration":10,"studyType":42,"phases":10,"briefSummary":43,"conditions":44,"keywords":10,"overallStatus":46,"whyStopped":10,"lastUpdateSubmitDate":47,"lastUpdatePostDateStruct":48,"startDateStruct":51,"completionDateStruct":53,"leadSponsor":55,"locationsCount":10},{"fullName":5,"class":6},"Chinese University of Hong Kong","OTHER",[8],{"label":9,"type":10,"description":10,"interventionNames":11},"Deficit group",null,[12],"Other: No Intervention: Observational Cohort",[14],{"type":6,"name":15,"description":16,"armGroupLabels":17,"otherNames":10},"No Intervention: Observational Cohort","no intervention",[9],[19],{"name":20,"affiliation":5,"role":21},"Shu Hang YUNG","PRINCIPAL_INVESTIGATOR",[23],{"name":24,"role":25,"phone":26,"phoneExt":10,"email":27},"muriel XIAO","CONTACT","(852)35053311","lingqingxiao@cuhk.edu.hk",{"type":21,"investigatorFullName":29,"investigatorTitle":30,"investigatorAffiliation":5,"oldNameTitle":10,"oldOrganization":10},"Patrick Shu-Hang YUNG","Professor and Chairman, Department of Orthopaedics & Traumatology, Faculty of Medicine, The Chinese University of Hong Kong","100614836","muscle-ml-multimodal-integration-of-muscle-strength-structure-by-machine-learning-for-precision-rehabilitation-after-acl-injury-100614836",false,"NCT07284771","MUSCLE-ML: Multimodal Integration of Muscle Strength, Structure by Machine Learning for Precision Rehabilitation After ACL Injury","Inclusion Criteria:\n\n* Unilateral ACL injury and plan for ACLR\n* Commit the post-operation physiotherapy in Prince of Wales Hospital\n\nExclusion Criteria:\n\n* Preoperative radiographic signs of arthritis\n* Patient non-compliance to the rehabilitation program",true,"ALL",{"count":40,"type":41},182,"ESTIMATED","OBSERVATIONAL","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:\n\nDo deficit clusters lead to poorer functional recovery compared to non-deficit clusters? Does an ML-derived composite score that integrates quadriceps\u002Fhamstring strength and size outperform isolated metrics in predicting RTP success?\n\nResearchers will compare deficit clusters against non-deficit clusters to determine if deficit clusters lead to poorer functional recovery.\n\nParticipants will:\n\nReturn for 5 follow-up timepoints in total for PRO and functional assessments including pre-operation, 1-, 3-, 6- and 12-months post-operation.",[45],"Machine Learning","NOT_YET_RECRUITING","2025-12-03",{"date":49,"type":50},"2025-12-16","ACTUAL",{"date":52,"type":41},"2026-04-01",{"date":54,"type":41},"2028-08-31",{"name":5,"class":6}]