[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"effusion-joint\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:effusion-joint":26},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,1,0,[8],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":4,"eligibilityCriteria":15,"healthyVolunteers":11,"sex":16,"minAge":4,"maxAge":4,"enrollmentInfo":17,"targetDuration":4,"studyType":20,"phases":21,"briefSummary":23,"conditions":24,"keywords":29,"overallStatus":38,"whyStopped":4,"lastUpdateSubmitDate":39,"lastUpdatePostDateStruct":40,"startDateStruct":43,"completionDateStruct":45,"leadSponsor":47,"locationsCount":50},"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":18,"type":19},4800,"ESTIMATED","INTERVENTIONAL",[22],"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.",[25,26,27,28],"Fractures, Bone","Effusion Joint","Bone Lesion","Dislocation",[30,31,32,33,34,35,36,37,28,27],"Artificial Intelligence","Fracture Detection","Emergency Care","Cost-Efficiency","AI-Assisted Diagnosis","Diagnostic Accuracy","Orthopedic Diagnostics","Effusion","RECRUITING","2026-01-20",{"date":41,"type":42},"2026-01-22","ACTUAL",{"date":44,"type":42},"2025-03-31",{"date":46,"type":19},"2026-04-30",{"name":48,"class":49},"Salzburger Landeskliniken","OTHER",3]