Assessing AI-Supported Fracture Detection in Emergency Care Units

NCT06754137 | Recruiting

• 1 other identifier: KI-FRACTURE_001_2024-11-27
• Study Type: interventional
• Target: 4,800
• Locations: 2 countries
• Sites: 3

Brief Summary

Brief Summary The purpose of this study is to determine if artificial intelligence (AI) can assist doctors in detecting broken bones, effusions, dislocations and bone lesions more quickly and accurately in an emergency room setting. The study will also evaluate whether AI can save time and reduce costs in healthcare. The main questions to be addressed are:

• Does AI improve the accuracy of detecting broken bones/dislocations/effusions/bone lesions?
• Can AI expedite the process of diagnosing broken bones/dislocations/effusions/bone lesions?
• Does AI reduce healthcare costs by enhancing efficiency? To investigate these questions, two groups of patients will be compared. One group will follow the traditional diagnostic approach, while the other group will utilize AI to assist in diagnosing X-rays. Participants in the study will: Undergo standard X-ray imaging of injured arms or legs, as part of routine care. Have X-rays reviewed by doctors with or without AI support, depending on the assigned group. The study will include patients of all ages presenting to the emergency room with an isolated injury or joint complaints. No additional tests or treatments beyond standard care will be involved.

Conditions

Fractures, Bone; Effusion Joint; Bone Lesion; Dislocation

Keywords

Artificial Intelligence; Fracture Detection; Emergency Care; Cost-Efficiency; AI-Assisted Diagnosis; Diagnostic Accuracy; Orthopedic Diagnostics; Effusion; Dislocation; Bone Lesion

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 (3)

• Time to Diagnosis

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

• Physician Diagnostic Confidence

  Measured immediately after the diagnosis

• Cost-Efficiency of Diagnostic Workflow

  Calculated at the end of the study for all enrolled participants, approximately 6 months from study initiation.

Study Arms (2)

Diagnostics without AI

ACTIVE COMPARATOR

Standard diagnostic approach where physicians interpret X-ray images without AI assistance.

Diagnostic Test: Standard Physician-Interpreted Fracture Detection

Diagnostics with AI

EXPERIMENTAL

Diagnostic approach where physicians are supported by an AI system (Aidoc or Gleamer BoneView) for fracture detection on X-ray images.

Diagnostic Test: AI-Assisted Fracture Detection System

Interventions

AI-Assisted Fracture Detection System DIAGNOSTIC_TEST

The intervention involves the use of an AI-assisted fracture detection system (Aidoc or Gleamer BoneView), which is integrated into the hospital's Picture Archiving and Communication System (PACS). These AI tools analyze X-ray images in real time, highlighting potential fracture sites for physician review. The AI output serves as an additional aid, while the final diagnosis remains the responsibility of the physician.

Diagnostics with AI

Standard Physician-Interpreted Fracture Detection DIAGNOSTIC_TEST

Physicians interpret X-ray images using their standard diagnostic practices without any assistance from AI. This represents the traditional approach to diagnosing fractures.

Diagnostics without AI

Eligibility Criteria

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

You may qualify if:

• Presenting to the emergency department with an isolated injury or joint complaint
• Patients able and willing to provide informed consent.

You may not qualify if:

• Patients with injuries or complaints involving multiple body regions
• Patients with prior imaging of the affected extremity or region within the past 6 months
• Contraindications to X-ray imaging (e.g., pregnancy or severe instability)
• Patients with other ongoing studies that may interfere with this study
• Patients unable to provide consent due to cognitive impairment or language barriers without an available representative.

Sponsors & Collaborators

• Salzburger Landeskliniken: lead
• Klinikum Nürnberg: collaborator

Study Sites (3)

Landesklinik Hallein, Salzburger Landeskliniken

Hallein, 5400, Austria

NOT YET RECRUITING

University Hospital Salzburg, Salzburger Landeskliniken

Salzburg, 5020, Austria

RECRUITING

University Hosptial Nuremberg, Klinikum Nürnberg

Nuremberg, 90471, Germany

RECRUITING

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; Hydrarthrosis; Joint Dislocations

Condition Hierarchy (Ancestors)

Wounds and Injuries; Joint Diseases; Musculoskeletal Diseases

Central Study Contacts

Martin Breitwieser, MD, MBA, BSc

CONTACT

+43 5 7255; m.breitwieser@salk.at

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

Study Record Dates

• First Submitted: December 9, 2024
• First Posted: December 31, 2024
• Study Start: March 31, 2025
• Primary Completion: April 30, 2026
• Study Completion: April 30, 2026
• Last Updated: January 22, 2026

Record last verified: 2026-01

Data Sharing

• IPD Sharing: Will share

Locations

DE (1); AU (2)