AI-Assisted Fracture Detection in Emergency Radiography

NCT06754137 | Completed

• 1 other identifier: KI-FRACTURE_001_2024-11-27
• Study Type: interventional
• Target: 1,667
• Locations: 2 countries
• Sites: 3

Brief 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

Keywords

Artificial Intelligence; Fracture Detection; Emergency Care; AI-Assisted Diagnosis; Diagnostic Accuracy; Orthopedic Diagnostics; Emergency Radiography; Clinical Decision Support; Diagnostic Confidence; Emergency Department

Outcome Measures

Primary Outcomes (1)

• Diagnostic Accuracy of Fracture/Dislocation/Effusion/Bone Lesion Detection

  The primary outcome measures the diagnostic accuracy of detecting broken bones/dislocations/effusions/bone lesions using sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). Diagnostic accuracy will be compared between the AI-assisted diagnostic approach and the standard physician-only approach. The gold standard for comparison will be determined by expert consensus based on independent review by a radiologist and an orthopedic specialist.

  At the time of initial diagnosis, within 2 hours of patient presentation to the orthopedic emergency unit

Secondary Outcomes (2)

• Time to Diagnosis

  During the patient's emergency department visit, typically within 4 hours of presentation.

• Physician Diagnostic Confidence

  Measured immediately after the diagnosis

Study Arms (2)

Standard Radiograph Interpretation Without AI

NO INTERVENTION

Plain radiographs are interpreted by the frontline treating physician according to routine clinical practice without access to AI output. All diagnostic, imaging, treatment, consultation, and discharge decisions remain the responsibility of the treating physician. Formal radiology reports are generally available later and are not routinely available before completion of the emergency department encounter.

AI-Assisted Radiograph Interpretation

EXPERIMENTAL

Plain radiographs are interpreted by the frontline treating physician with access to real-time output from BoneView version 2.3.8 (Gleamer, Paris, France). The AI output is used as diagnostic decision support only; all final diagnostic, imaging, treatment, consultation, and discharge decisions remain the responsibility of the treating physician.

Diagnostic Test: BoneView AI-Assisted Radiograph Interpretation

Interventions

BoneView AI-Assisted Radiograph Interpretation DIAGNOSTIC_TEST

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.

AI-Assisted Radiograph Interpretation

Eligibility Criteria

• Age: 2 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

• Age 2 years or older.
• Presentation to the emergency department following trauma requiring plain radiographic imaging.
• Injury involving a single anatomical region within the supported anatomical scope of the AI system.
• Written informed consent provided by the participant or legally authorized representative, with age-appropriate assent where applicable.

You may not qualify if:

• Age younger than 2 years.
• Injuries involving multiple anatomical regions.
• Head or cervical spine injuries.
• Previous radiographic imaging or medical assessment for the same injury before the index presentation.
• Contraindication to X-ray imaging, including pregnancy.
• Lack of informed consent.

Sponsors & Collaborators

• Salzburger Landeskliniken: lead
• Klinikum Nürnberg: collaborator

Study Sites (3)

Landesklinik Hallein, Salzburger Landeskliniken

Hallein, 5400, Austria

Location

University Hospital Salzburg, Salzburger Landeskliniken

Salzburg, 5020, Austria

Location

University Hosptial Nuremberg, Klinikum Nürnberg

Nuremberg, 90471, Germany

Location

Related Publications (1)

• Breitwieser M, Zirknitzer S, Poslusny K, Freude T, Scholsching J, Bodenschatz K, Wagner A, Hergan K, Schaffert M, Metzger R, Marko P. AI in Fracture Detection: A Cross-Disciplinary Analysis of Physician Acceptance Using the UTAUT Model. Diagnostics (Basel). 2025 Aug 21;15(16):2117. doi: 10.3390/diagnostics15162117.

  PMID: 40870969 DERIVED

MeSH Terms

Conditions

Fractures, Bone; Emergencies

Condition Hierarchy (Ancestors)

Wounds and Injuries; Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Model Details: Participants were individually randomized in a 1:1 ratio to AI-assisted radiograph interpretation or standard radiograph interpretation without AI assistance. A single global randomization sequence was generated by the trial statistician and used across all three participating centers.

Study Record Dates

• First Submitted: December 9, 2024
• First Posted: December 31, 2024
• Study Start: October 1, 2025
• Primary Completion: April 30, 2026
• Study Completion: April 30, 2026
• Last Updated: August 17, 2026

Record last verified: 2026-08

Data Sharing

• IPD Sharing: Will share

Locations

DE (1); AU (2)