Clinical Validation of RBfracture for Diagnosing Trauma-related Musculoskeletal Injuries

Official title

Clinical Validation of RBfracture to Demonstrate Improved Diagnostic Performance of the Intended Users

Overview

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.

Detailed description

RBfracture, developed by Radiobotics (Copenhagen, Denmark), is a decision-support software solution for computerized detection and diagnosis of acute fractures, joint dislocations, joint effusion, and lipohemarthrosis. To demonstrate improved diagnostic performance of the intended users when assisted by RBfracture, a fully-crossed multi-reader multi-case (MRMC) study will be conducted. 415 study exams will be retrospectively obtained through stratified sampling with enrichment to represent all anatomical regions supported by the software. Sixteen independent clinicians representing various clinical roles and years of experience will participate as study readers.

The reference standard will be established by American Board of Radiology (ABR)-certified radiologists with a minimum of 3 years of experience post certification. All exams will be reviewed and annotated by two musculoskeletal (MSK) radiologists, independently. Annotations will be done in a medical-grade reading environment, using diagnostic quality monitors. If required, any disagreement will be adjudicated by a third specialized radiologist.

In both assisted and unassisted sessions, readers will localize every unique fracture, joint dislocation, joint effusion, and lipohemarthrosis. For each finding, readers will provide a bounding box and a confidence score (1-5). Additionally, at the exam level, readers will indicate the presence or absence of each injury type along with a confidence score (0-100).

At the case level, ROC curves will be generated for each reader under both device-assisted and unassisted reading scenarios. The AUC of the ROC curve per reader in each reading scenario, and the difference in the reader-averaged AUC between device-assisted and unassisted reading scenarios will be calculated. The difference in reader-averaged AUC between the two reading scenarios will be reported as the primary outcome to demonstrate improved diagnostic performance of the intended users. Exam-level sensitivity, specificity, and average false positives, and injury-level sensitivity and AUC-AFROC will be presented as secondary outcomes.

Interventions

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

Primary outcome measures

• Change in diagnostic accuracy between device-assisted and device-unassisted readers at the exam level. [Time frame: one month]

Secondary outcome measures (3)

• Change in diagnostic accuracy between device-assisted and device-unassisted readers at the exam level. [Time frame: one month]
• Change in diagnostic accuracy between device-assisted and device-unassisted readers at the injury level. [Time frame: one month]
• Generalizability of device performance across demographic and technical factors [Time frame: one month]

Eligibility criteria

Inclusion criteria

• 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)

Exclusion criteria

• 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).

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: Yes

Study design

Observational model

Case-only

Study locations

Center list to be confirmed — check the primary protocol.

Identifiers

NCT: NCT07421791 · 20254568

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗