[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100574042":3},{"organization":4,"armGroups":7,"interventions":20,"overallOfficials":26,"centralContacts":31,"locations":38,"responsibleParty":86,"collaborators":90,"id":93,"slug":94,"hasResults":95,"nctId":96,"briefTitle":97,"officialTitle":98,"acronym":26,"eligibilityCriteria":99,"healthyVolunteers":95,"sex":100,"minAge":26,"maxAge":26,"enrollmentInfo":101,"targetDuration":26,"studyType":104,"phases":105,"briefSummary":107,"conditions":108,"keywords":113,"overallStatus":59,"whyStopped":26,"lastUpdateSubmitDate":122,"lastUpdatePostDateStruct":123,"startDateStruct":126,"completionDateStruct":128,"leadSponsor":130,"locationsCount":131},{"fullName":5,"class":6},"Salzburger Landeskliniken","OTHER",[8,14],{"label":9,"type":10,"description":11,"interventionNames":12},"Diagnostics without AI","ACTIVE_COMPARATOR","Standard diagnostic approach where physicians interpret X-ray images without AI assistance.",[13],"Diagnostic Test: Standard Physician-Interpreted Fracture Detection",{"label":15,"type":16,"description":17,"interventionNames":18},"Diagnostics with AI","EXPERIMENTAL","Diagnostic approach where physicians are supported by an AI system (Aidoc or Gleamer BoneView) for fracture detection on X-ray images.",[19],"Diagnostic Test: AI-Assisted Fracture Detection System",[21,27],{"type":22,"name":23,"description":24,"armGroupLabels":25,"otherNames":26},"DIAGNOSTIC_TEST","AI-Assisted Fracture Detection System","The intervention involves the use of an AI-assisted fracture detection system (Aidoc or Gleamer BoneView), which is integrated into the hospital's Picture Archiving and Communication System (PACS). These AI tools analyze X-ray images in real time, highlighting potential fracture sites for physician review. The AI output serves as an additional aid, while the final diagnosis remains the responsibility of the physician.",[15],null,{"type":22,"name":28,"description":29,"armGroupLabels":30,"otherNames":26},"Standard Physician-Interpreted Fracture Detection","Physicians interpret X-ray images using their standard diagnostic practices without any assistance from AI. This represents the traditional approach to diagnosing fractures.",[9],[32],{"name":33,"role":34,"phone":35,"phoneExt":36,"email":37},"Martin Breitwieser, MD, MBA, BSc","CONTACT","+43 5 7255","54705","m.breitwieser@salk.at",[39,57,70],{"facility":40,"status":41,"city":42,"state":26,"zip":43,"country":44,"countryCode":45,"cosmosGeoPoint":46,"geoPoint":51,"contacts":52},"Landesklinik Hallein, Salzburger Landeskliniken","NOT_YET_RECRUITING","Hallein","5400","Austria","AT",{"type":47,"coordinates":48},"Point",[49,50],13.1,47.68333,{"lat":50,"lon":49},[53],{"name":54,"role":34,"phone":55,"phoneExt":26,"email":56},"Sebastian Filipp, MD","+43 57 25544 55354","s.filipp@salk.at",{"facility":58,"status":59,"city":60,"state":26,"zip":61,"country":44,"countryCode":45,"cosmosGeoPoint":62,"geoPoint":66,"contacts":67},"University Hospital Salzburg, Salzburger Landeskliniken","RECRUITING","Salzburg","5020",{"type":47,"coordinates":63},[64,65],13.04399,47.79941,{"lat":65,"lon":64},[68],{"name":33,"role":34,"phone":69,"phoneExt":26,"email":37},"+43 57 2550 54705",{"facility":71,"status":59,"city":72,"state":26,"zip":73,"country":74,"countryCode":75,"cosmosGeoPoint":76,"geoPoint":80,"contacts":81},"University Hosptial Nuremberg, Klinikum Nürnberg","Nuremberg","90471","Germany","DE",{"type":47,"coordinates":77},[78,79],11.07752,49.45421,{"lat":79,"lon":78},[82],{"name":83,"role":34,"phone":84,"phoneExt":26,"email":85},"Thomas Reuter, MD","+49 911 3982600","thomas.reuter@klinikum-nuernberg.de",{"type":87,"investigatorFullName":88,"investigatorTitle":89,"investigatorAffiliation":5,"oldNameTitle":26,"oldOrganization":26},"PRINCIPAL_INVESTIGATOR","Martin Breitwieser","Principal Investigator",[91],{"name":92,"class":6},"Klinikum Nürnberg","100574042","assessing-ai-supported-fracture-detection-in-emergency-care-units-100574042",false,"NCT06754137","Assessing AI-Supported Fracture Detection in Emergency Care Units","Evaluating the Cost-Efficiency and Workflow Impact of AI-Supported Fracture Detection in an Orthopedic Emergency Care Unit","Inclusion Criteria:\n\n* Presenting to the emergency department with an isolated injury or joint complaint\n* Patients able and willing to provide informed consent.\n\nExclusion Criteria:\n\n* Patients with injuries or complaints involving multiple body regions\n* Patients with prior imaging of the affected extremity or region within the past 6 months\n* Contraindications to X-ray imaging (e.g., pregnancy or severe instability)\n* Patients with other ongoing studies that may interfere with this study\n* Patients unable to provide consent due to cognitive impairment or language barriers without an available representative.","ALL",{"count":102,"type":103},4800,"ESTIMATED","INTERVENTIONAL",[106],"NA","Brief Summary The purpose of this study is to determine if artificial intelligence (AI) can assist doctors in detecting broken bones, effusions, dislocations and bone lesions more quickly and accurately in an emergency room setting. The study will also evaluate whether AI can save time and reduce costs in healthcare.\n\nThe main questions to be addressed are:\n\n* Does AI improve the accuracy of detecting broken bones\u002Fdislocations\u002Feffusions\u002Fbone lesions?\n* Can AI expedite the process of diagnosing broken bones\u002Fdislocations\u002Feffusions\u002Fbone lesions?\n* Does AI reduce healthcare costs by enhancing efficiency?\n\nTo investigate these questions, two groups of patients will be compared. One group will follow the traditional diagnostic approach, while the other group will utilize AI to assist in diagnosing X-rays.\n\nParticipants in the study will:\n\nUndergo standard X-ray imaging of injured arms or legs, as part of routine care.\n\nHave X-rays reviewed by doctors with or without AI support, depending on the assigned group.\n\nThe study will include patients of all ages presenting to the emergency room with an isolated injury or joint complaints. No additional tests or treatments beyond standard care will be involved.",[109,110,111,112],"Fractures, Bone","Effusion Joint","Bone Lesion","Dislocation",[114,115,116,117,118,119,120,121,112,111],"Artificial Intelligence","Fracture Detection","Emergency Care","Cost-Efficiency","AI-Assisted Diagnosis","Diagnostic Accuracy","Orthopedic Diagnostics","Effusion","2026-01-20",{"date":124,"type":125},"2026-01-22","ACTUAL",{"date":127,"type":125},"2025-03-31",{"date":129,"type":103},"2026-04-30",{"name":5,"class":6},3]