[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100625373":3},{"organization":4,"armGroups":7,"interventions":17,"overallOfficials":10,"centralContacts":23,"locations":10,"responsibleParty":29,"collaborators":10,"id":31,"slug":32,"hasResults":33,"nctId":34,"briefTitle":35,"officialTitle":36,"acronym":10,"eligibilityCriteria":37,"healthyVolunteers":38,"sex":39,"minAge":40,"maxAge":10,"enrollmentInfo":41,"targetDuration":10,"studyType":44,"phases":10,"briefSummary":45,"conditions":46,"keywords":10,"overallStatus":51,"whyStopped":10,"lastUpdateSubmitDate":52,"lastUpdatePostDateStruct":53,"startDateStruct":56,"completionDateStruct":58,"leadSponsor":60,"locationsCount":10},{"fullName":5,"class":6},"Radiobotics","INDUSTRY",[8,14],{"label":9,"type":10,"description":11,"interventionNames":12},"device-assisted",null,"In the device-assisted modality, study readers will interpret patient X-ray exams with RBfracture assistance.",[13],"Diagnostic Test: RBfracture",{"label":15,"type":10,"description":16,"interventionNames":10},"device-unassisted","In the device-unassisted modality, study readers will interpret patient X-ray exams without RBfracture assistance.",[18],{"type":19,"name":20,"description":21,"armGroupLabels":22,"otherNames":10},"DIAGNOSTIC_TEST","RBfracture","RBfracture is a decision support software designed to assist the intended user in diagnosing fracture, joint dislocation, joint effusion, and lipohemarthrosis.",[9],[24],{"name":25,"role":26,"phone":27,"phoneExt":10,"email":28},"Janitha M Mudannayake, PhD","CONTACT","+46708412511","jm@radiobotics.com",{"type":30,"investigatorFullName":10,"investigatorTitle":10,"investigatorAffiliation":10,"oldNameTitle":10,"oldOrganization":10},"SPONSOR","100625373","clinical-validation-of-rbfracture-for-diagnosing-trauma-related-musculoskeletal-injuries-100625373",false,"NCT07421791","Clinical Validation of RBfracture for Diagnosing Trauma-related Musculoskeletal Injuries","Clinical Validation of RBfracture to Demonstrate Improved Diagnostic Performance of the Intended Users","Inclusion Criteria:\n\n* XR exams of a patient ≥22 years of age following a recent acute musculoskeletal trauma.\n* Modality is digitally acquired radiographs (Computed Radiography or Digital Radiography)\n\nExclusion Criteria:\n\n* XR exam types that are outside of the intended use (e.g., chest, abdomen, facial bones, cervical spine).\n* Exams with missing patient age.\n* Exams from follow-up patient examinations, e.g., post-surgical controls or evaluation of fracture healing.\n* Any exams containing radiographs previously used in software development.\n* Exams containing additional radiographs that are incoherent with the XR exam type (e.g., wrist radiograph in a hip and pelvis exam type).\n* Radiograph views that are unsupported.\n* Poor radiographic image quality, rendering radiograph clinically unsuitable (e.g., inappropriate selection of technical exposure factors, patient motion, presence of artefacts, and improper collimation of the radiographic beam).",true,"ALL","22 Years",{"count":42,"type":43},415,"ESTIMATED","OBSERVATIONAL","The goal of this study is to determine if the computer software, RBfracture, developed by Radiobotics, helps primary care, emergency, and radiology clinicians more easily identify bone injuries caused by a traumatic impact (such as a fall or car collision). RBfracture uses artificial intelligence (AI) to analyze X-ray images of patients to identify fractures and joint dislocations visible on the X-ray images. RBfracture also identifies fluid buildup in the elbow and knee joints resulting from a fracture or dislocation.\n\nSixteen clinicians will review X-ray images from 415 adult patients, who may have sustained a bone injury, to diagnose any injuries visible on their X-ray images. First, the clinicians will review half of the images with and half of the images without the help of the RBfracture software. After a 4-week break, the clinicians will once again review the same images. This time, the software's help will be switched, so it is unavailable for the images the clinicians previously reviewed with it, and available for the images they reviewed without it.\n\nThe number of correct and incorrect diagnoses made by the clinicians when they were helped by the software will be compared to the number of correct and incorrect diagnoses made by the clinicians when they did not receive any help from the software. This comparison will reveal if using the software helps clinicians to diagnose more injuries and miss less injuries.",[47,48,49,50],"Fracture","Joint Dislocation","Joint Effusion","Knee Lipohemarthrosis","NOT_YET_RECRUITING","2026-02-24",{"date":54,"type":55},"2026-02-27","ACTUAL",{"date":57,"type":43},"2026-02",{"date":59,"type":43},"2026-04",{"name":5,"class":6}]