[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100640746":3},{"organization":4,"armGroups":7,"interventions":19,"overallOfficials":25,"centralContacts":29,"locations":35,"responsibleParty":52,"collaborators":54,"id":63,"slug":12,"hasResults":64,"nctId":65,"briefTitle":66,"officialTitle":66,"acronym":67,"eligibilityCriteria":68,"healthyVolunteers":69,"sex":70,"minAge":12,"maxAge":12,"enrollmentInfo":71,"targetDuration":12,"studyType":74,"phases":75,"briefSummary":77,"conditions":78,"keywords":80,"overallStatus":85,"whyStopped":12,"lastUpdateSubmitDate":86,"lastUpdatePostDateStruct":87,"startDateStruct":90,"completionDateStruct":92,"leadSponsor":94,"locationsCount":95},{"fullName":5,"class":6},"Children's Hospital Medical Center, Cincinnati","OTHER",[8,13],{"label":9,"type":10,"description":11,"interventionNames":12},"Standard non-AI echocardiography","NO_INTERVENTION","In the Standard non-AI Echocardiography arm, participants will receive the current standard of care under the ADUNU program, which includes a single parasternal long-axis view with black-and-white and color Doppler imaging. Providers have been trained to recognize mitral regurgitation greater than 1.5 or 2 cm, any aortic insufficiency, qualitatively reduced left ventricular systolic function, and pericardial effusion. Detection of any of these findings constitutes a screen positive, prompting referral for a confirmatory echocardiogram.",null,{"label":14,"type":15,"description":16,"interventionNames":17},"RADAR-AI-assisted echocardiography","EXPERIMENTAL","In the RADAR Echocardiography arm, participants will undergo AI-assisted screening according to the well-established RADAR protocol including the same image acquisition protocol but interpreted by the tablet-based software based on two independent AI algorithms 1) RHD positive or negative and 2) mitral regurgitation jet length. Positive findings from either algorithm constitutes a screen positive. Providers may also refer for other concerns.",[18],"Diagnostic Test: AI assisted echocardiography",[20],{"type":21,"name":22,"description":23,"armGroupLabels":24,"otherNames":12},"DIAGNOSTIC_TEST","AI assisted echocardiography","Continue standard of care with AI-assisted echocardiography",[14],[26],{"name":27,"affiliation":5,"role":28},"Andrea Beaton","PRINCIPAL_INVESTIGATOR",[30],{"name":31,"role":32,"phone":33,"phoneExt":12,"email":34},"Isabella Brigham","CONTACT","513-517-1307","isabella.aspromonte@cchmc.org",[36],{"facility":37,"status":12,"city":38,"state":12,"zip":12,"country":39,"cosmosGeoPoint":40,"geoPoint":45,"contacts":46},"Uganda Heart Institute","Kampala","Uganda",{"type":41,"coordinates":42},"Point",[43,44],32.58219,0.31628,{"lat":44,"lon":43},[47,51],{"name":48,"role":32,"phone":49,"phoneExt":12,"email":50},"Doreen Nakagaayi","+256 780770785","dnakagaayi@gmail.com",{"name":48,"role":28,"phone":12,"phoneExt":12,"email":12},{"type":53,"investigatorFullName":12,"investigatorTitle":12,"investigatorAffiliation":12,"oldNameTitle":12,"oldOrganization":12},"SPONSOR",[55,56,58,60],{"name":37,"class":6},{"name":57,"class":6},"Ochsner Health System",{"name":59,"class":6},"Vanderbilt University Medical Center",{"name":61,"class":62},"Children's National Health Center","UNKNOWN","100640746",false,"NCT07599956","Artificial Intelligence to Scale Early Rheumatic Heart Disease Detection","SHIELD 1","Inclusion Criteria:\n\n* Employed at a participating ADUNU facility\n* Holds a designated role in the ADUNU program as a nurse screener\n\nExclusion Criteria:\n\n* None. The pragmatic trial design includes all eligible staff at participating facilities.",true,"ALL",{"count":72,"type":73},62,"ESTIMATED","INTERVENTIONAL",[76],"NA","The main goal of this project is to see if RADAR (Rapid AI-assisted Detection and Analysis of Rheumatic heart disease), which is a machine and deep-learning AI model, can help make rheumatic heart disease (RHD) screening easier to expand. Specifically, the project will test whether RADAR can screen as accurately-or more accurately-than current methods, and whether it can be used effectively in different low-resource settings. The aim is to show that RADAR could be adopted and used widely around the world.",[79],"Rheumatic Heart Disease",[81,82,83,84],"Rheumatic heart disease","Artificial Intelligence","Deep Learning","Machine Learning","NOT_YET_RECRUITING","2026-05-22",{"date":88,"type":89},"2026-05-27","ACTUAL",{"date":91,"type":73},"2026-06",{"date":93,"type":73},"2028-06",{"name":5,"class":6},1]