About this trial
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.
Sixteen 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.
The 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.
Eligibility criteria
This trial accepts healthy volunteersQualifiers
XR exams of a patient ≥22 years of age following a recent acute musculoskeletal trauma.
Modality is digitally acquired radiographs (Computed Radiography or Digital Radiography)
Disqualifiers
XR exam types that are outside of the intended use (e.g., chest, abdomen, facial bones, cervical spine).
Exams with missing patient age.
Exams from follow-up patient examinations, e.g., post-surgical controls or evaluation of fracture healing.
Any exams containing radiographs previously used in software development.
Trial population
X-ray exams of adult patients suspected of having trauma-related musculoskeletal injuries.
Trial design
Case-only
Retrospective
Treatments tested in this trial
RBfracture
Diagnostic testRBfracture is a decision support software designed to assist the intended user in diagnosing fracture, joint dislocation, joint effusion, and lipohemarthrosis.
Treatment groups
Trial outcomes
Primary outcomes
Change in diagnostic accuracy between device-assisted and device-unassisted readers at the exam level.
The difference in the reader-averaged AUC between device-assisted and device-unassisted readers is significant at a one-sided p value of 0.025.
Secondary outcomes
Change in diagnostic accuracy between device-assisted and device-unassisted readers at the exam level.
The difference in reader-averaged sensitivity and specificity between device-assisted and device-unassisted readers.
Change in diagnostic accuracy between device-assisted and device-unassisted readers at the injury level.
The difference in reader-averaged AUC-AFROC, sensitivity, and false positives per exam between device-assisted and device-unassisted readers.
Generalizability of device performance across demographic and technical factors
At the exam level, the difference in AUC, sensitivity, and specificity between device-assisted and device-unassisted readers, with data stratified by reader qualification, patient sex, patient ethnicity (if available), and radiographic machine manufacturer.
Locations
Sponsors and contacts
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