[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100280571":3},{"organization":4,"armGroups":7,"interventions":15,"overallOfficials":10,"centralContacts":26,"locations":31,"responsibleParty":48,"collaborators":10,"id":50,"slug":51,"hasResults":52,"nctId":53,"briefTitle":54,"officialTitle":55,"acronym":56,"eligibilityCriteria":57,"healthyVolunteers":58,"sex":59,"minAge":10,"maxAge":10,"enrollmentInfo":60,"targetDuration":10,"studyType":63,"phases":10,"briefSummary":64,"conditions":65,"keywords":68,"overallStatus":34,"whyStopped":10,"lastUpdateSubmitDate":70,"lastUpdatePostDateStruct":71,"startDateStruct":74,"completionDateStruct":76,"leadSponsor":78,"locationsCount":79},{"fullName":5,"class":6},"Duke University","OTHER",[8],{"label":9,"type":10,"description":11,"interventionNames":12},"Non-invasive vascular testing",null,"All patients undergoing non-invasive vascular testing will be eligible for this study. The official results will be used to develop the algorithm and to evaluate the accuracy of the algorithm",[13,14],"Device: Non-invasive vascular testing","Device: machine-learning algorithm",[16,23],{"type":17,"name":9,"description":18,"armGroupLabels":19,"otherNames":20},"DEVICE","Results of clinically indicated non-invasive vascular testing will be used to develop a machine learning algorithm",[9],[21,22],"Continuous wave Doppler","plethysmography",{"type":17,"name":24,"description":10,"armGroupLabels":25,"otherNames":10},"machine-learning algorithm",[9],[27],{"name":28,"role":29,"phone":10,"phoneExt":10,"email":30},"Leila Mureebe, MD","CONTACT","leila.mureebe@duke.edu",[32],{"facility":33,"status":34,"city":35,"state":36,"zip":37,"country":38,"countryCode":39,"cosmosGeoPoint":40,"geoPoint":45,"contacts":46},"Duke University Medical Center","RECRUITING","Durham","North Carolina","27710","United States","US",{"type":41,"coordinates":42},"Point",[43,44],-78.89862,35.99403,{"lat":44,"lon":43},[47],{"name":28,"role":29,"phone":10,"phoneExt":10,"email":30},{"type":49,"investigatorFullName":10,"investigatorTitle":10,"investigatorAffiliation":10,"oldNameTitle":10,"oldOrganization":10},"SPONSOR","100280571","machine-learning-for-handheld-vascular-studies-100280571",false,"NCT02932176","Machine Learning for Handheld Vascular Studies","Development and Validation of a Novel Machine-learning Algorithm to Assist in Handheld Vascular Diagnostics","DopplerZAM","Inclusion Criteria:\n\n* A clinically driven request for non-invasive vascular testing must be present\n\nExclusion Criteria:\n\n* None (other than patient declines to participate)",true,"ALL",{"count":61,"type":62},180,"ESTIMATED","OBSERVATIONAL","The use of handheld arterial 'stethoscopes' (continuous wave Doppler devices) are ubiquitous in clinical practice. However, most users have received no formal training in their use or the interpretation of the returned data. This leads to delays in diagnosis and errors in diagnosis.\n\nThe investigators intend to create a novel machine-learning algorithm to assist clinicians in the use of this data. This study will allow the investigators to collect sound files from the use of the devices and compare the algorithms output to established, existing vascular testing. There will be no invasive procedures, and use of these stethoscopes is part of routine clinical care.\n\nIf successful, this data and algorithm will be later deployed via smartphone app for point of case testing in a separate study",[66,67],"Atherosclerosis","Wounds and Injuries",[69],"Arteries","2026-03-04",{"date":72,"type":73},"2026-03-05","ACTUAL",{"date":75,"type":73},"2016-09-07",{"date":77,"type":62},"2026-12-31",{"name":5,"class":6},1]