[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100630330":3},{"organization":4,"armGroups":7,"interventions":19,"overallOfficials":29,"centralContacts":33,"locations":39,"responsibleParty":56,"collaborators":59,"id":72,"slug":73,"hasResults":74,"nctId":75,"briefTitle":76,"officialTitle":77,"acronym":24,"eligibilityCriteria":78,"healthyVolunteers":74,"sex":79,"minAge":80,"maxAge":24,"enrollmentInfo":81,"targetDuration":24,"studyType":84,"phases":85,"briefSummary":87,"conditions":88,"keywords":90,"overallStatus":42,"whyStopped":24,"lastUpdateSubmitDate":96,"lastUpdatePostDateStruct":97,"startDateStruct":100,"completionDateStruct":102,"leadSponsor":104,"locationsCount":105},{"fullName":5,"class":6},"Chinese University of Hong Kong","OTHER",[8,14],{"label":9,"type":10,"description":11,"interventionNames":12},"AI-Assisted Group","ACTIVE_COMPARATOR","AR severity will be assessed using an AI tool that evaluates grading and key echocardiographic parameters (e.g., EROA, VC, PISA, jet width, and RegVol), along with the time required for assessments.",[13],"Diagnostic Test: AI-Assisted Group",{"label":15,"type":6,"description":16,"interventionNames":17},"Manual Measurement Group","AR severity will be assessed manually by trained sonographers following standard protocols. Cardiologists (ASE level III or equivalent), blinded to patient history and group assignment, will review both AI-generated and manual outputs to make final diagnoses and treatment decisions based solely on the initial assessments.",[18],"Other: Manual measurement group",[20,25],{"type":21,"name":9,"description":22,"armGroupLabels":23,"otherNames":24},"DIAGNOSTIC_TEST","Participants in this group will undergo aortic regurgitation assessment using an advanced artificial intelligence tool.",[9],null,{"type":6,"name":26,"description":27,"armGroupLabels":28,"otherNames":24},"Manual measurement group","Participants in this group will receive a traditional diagnostic assessment for aortic regurgitation, performed by trained sonographers following standard protocols.",[15],[30],{"name":31,"affiliation":5,"role":32},"Alex PW Lee, Professor","PRINCIPAL_INVESTIGATOR",[34],{"name":35,"role":36,"phone":37,"phoneExt":24,"email":38},"Xueting Wang","CONTACT","(852) 3505 3840","xueting@cuhk.edu.hk",[40],{"facility":41,"status":42,"city":43,"state":44,"zip":45,"country":43,"countryCode":46,"cosmosGeoPoint":47,"geoPoint":52,"contacts":53},"Division of Cardiology, Department of Medicine and Therapeutics Faculty of Medicine, The Chinese University of Hong Kong","RECRUITING","Hong Kong","New Territories","Sha Tin","HK",{"type":48,"coordinates":49},"Point",[50,51],114.17469,22.27832,{"lat":51,"lon":50},[54],{"name":55,"role":36,"phone":37,"phoneExt":24,"email":38},"Xueting PW Wang, Professor",{"type":32,"investigatorFullName":57,"investigatorTitle":58,"investigatorAffiliation":5,"oldNameTitle":24,"oldOrganization":24},"Dr Alex PW Lee","Professor",[60,62,65,67,70],{"name":61,"class":6},"Semmelweis University",{"name":63,"class":64},"The Prince Charles Hospital","OTHER_GOV",{"name":66,"class":6},"Toho University",{"name":68,"class":69},"Us2.ai","UNKNOWN",{"name":71,"class":6},"The University of New South Wales","100630330","artificial-intelligence-in-aortic-regurgitation-100630330",false,"NCT07486271","Artificial Intelligence in Aortic Regurgitation","Artificial Intelligence in Aortic Regurgitation: A Multicenter Randomised Controlled Trial","Inclusion Criteria:\n\n* Confirmed AR diagnosis via TTE and Doppler imaging per guidelines.\n* Age ≥ 18 years.\n* Adequate acoustic window for AR quantification.\n\nExclusion Criteria:\n\n* Prior cardiac transplant or implanted cardiac devices.\n* Poor image quality.\n* Pregnancy or lactation.","ALL","18 Years",{"count":82,"type":83},540,"ESTIMATED","INTERVENTIONAL",[86],"NA","This research project aims to develop and validate a tool that uses artificial intelligence (AI) to automatically detect and quantify aortic regurgitation (AR). The clinical efficacy of this tool will be established by comparing it to manual diagnostic methods in a multicenter randomized controlled trial. By leveraging deep learning (DL) techniques, the AI system will automate aortic regurgitation (AR) detection, measurement, and diagnosis, addressing challenges like variability in echocardiographic interpretations and the need for specialized expertise. It will integrate multiple echocardiographic parameters to provide accurate, standardized, and efficient AR diagnoses, reducing human error and improving consistency. This tool will enhance diagnostic precision and accessibility, improving clinical outcomes and extending advanced diagnostic capabilities to a broader range of healthcare environments, including resource-limited settings.",[89],"Aortic Regurgitation Disease",[91,92,93,94,95],"Aortic Regurgitation","Artificial Intelligence","Echocardiography","Automated Diagnosis","Clinical Efficacy","2026-03-17",{"date":98,"type":99},"2026-03-20","ACTUAL",{"date":101,"type":99},"2025-12-01",{"date":103,"type":83},"2028-03-31",{"name":5,"class":6},1]