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AI-Assisted Fracture Detection in Emergency Radiography

NCT06754137 · Status: COMPLETED · Phase: NA · Type: INTERVENTIONAL · Enrollment: 1667

Last updated 2026-08-17

No results posted yet for this study

Summary

This study evaluates whether artificial intelligence (AI) can support doctors who interpret X-rays for suspected fractures in emergency care.

In the participating hospitals, X-rays are usually interpreted first by the frontline treating physician, while the formal radiology report is generally available later and not before the patient leaves the emergency department. AI may therefore provide an immediate additional assessment while clinical decisions are being made.

Patients were randomly assigned to one of two groups. In the AI-assisted group, physicians interpreted the X-rays with support from an AI system. In the control group, physicians interpreted the same type of X-rays without AI support. All final diagnoses and treatment decisions remained with the treating physician.

The main question is whether AI assistance affects the time from triage to completion of emergency department treatment. The study also evaluates whether AI influences physician diagnostic confidence, the use of additional imaging, missed fractures, and diagnostic accuracy.

The study includes patients aged 2 years or older presenting after trauma with a suspected fracture requiring X-ray imaging. No additional imaging or treatment was required solely because of study participation.

Conditions

  • Bone Fractures

Interventions

DIAGNOSTIC_TEST

BoneView AI-Assisted Radiograph Interpretation

BoneView version 2.3.8 (Gleamer, Paris, France) analyzes DICOM radiographs and provides real-time visual annotations and classifications for supported musculoskeletal abnormalities. Physicians in the intervention group could view fracture-related findings as well as other supported outputs, including dislocations, joint effusions, and focal bone lesions. At University Hospital Salzburg and Regional Hospital Hallein, BoneView was delivered through the Aidoc aiOS platform (version 3.24.0). At University Hospital Nuremberg, BoneView was integrated directly into the local imaging workflow. BoneView was used as a decision-support tool and did not replace physician interpretation or the subsequent formal radiology report.

Sponsors & Collaborators

  • Klinikum Nürnberg

    collaborator OTHER

  • Salzburger Landeskliniken

    lead OTHER

Study Design

Allocation
RANDOMIZED
Purpose
DIAGNOSTIC
Masking
NONE
Model
PARALLEL

Eligibility

Min Age
2 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-10-01
Primary Completion
2026-04-30
Completion
2026-04-30

Countries

  • Austria
  • Germany

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06754137 on ClinicalTrials.gov