Clinical Validation of RBfracture for Diagnosing Trauma-related Musculoskeletal Injuries

NCT07421791 | Not Yet Recruiting

• 1 other identifier: 20254568
• Study Type: observational
• Target: 415
• Locations: 0 countries
• Sites: N/A

Brief Summary

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.

Conditions

Fracture; Joint Dislocation; Joint Effusion; Knee Lipohemarthrosis

Outcome Measures

Primary Outcomes (1)

• 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.

  one month

Secondary Outcomes (3)

• Change in diagnostic accuracy between device-assisted and device-unassisted readers at the exam level.

  one month

• Change in diagnostic accuracy between device-assisted and device-unassisted readers at the injury level.

  one month

• Generalizability of device performance across demographic and technical factors

  one month

Study Arms (2)

device-assisted

In the device-assisted modality, study readers will interpret patient X-ray exams with RBfracture assistance.

Diagnostic Test: RBfracture

device-unassisted

In the device-unassisted modality, study readers will interpret patient X-ray exams without RBfracture assistance.

Interventions

RBfracture DIAGNOSTIC_TEST

RBfracture is a decision support software designed to assist the intended user in diagnosing fracture, joint dislocation, joint effusion, and lipohemarthrosis.

device-assisted

Eligibility Criteria

• Age: 22 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

X-ray exams of adult patients suspected of having trauma-related musculoskeletal injuries.

You may qualify if:

• 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)

You may not qualify if:

• 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.
• Exams containing additional radiographs that are incoherent with the XR exam type (e.g., wrist radiograph in a hip and pelvis exam type).
• Radiograph views that are unsupported.
• 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).

Sponsors & Collaborators

• Radiobotics: lead

Biospecimen

Retention: SAMPLES WITHOUT DNA

Samples are retrospectively-sampled, deidentified X-rays

MeSH Terms

Conditions

Fractures, Bone; Joint Dislocations; Hydrarthrosis

Condition Hierarchy (Ancestors)

Wounds and Injuries; Joint Diseases; Musculoskeletal Diseases

Central Study Contacts

Janitha M Mudannayake, PhD

CONTACT

+46708412511; jm@radiobotics.com

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: RETROSPECTIVE
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: February 12, 2026
• First Posted: February 19, 2026
• Study Start: February 1, 2026
• Primary Completion: April 1, 2026
• Study Completion: April 1, 2026
• Last Updated: February 27, 2026

Record last verified: 2026-02