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Assessing AI-Supported Fracture Detection in Emergency Care Units

NCT06754137 · Status: RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 4800

Last updated 2026-01-22

No results posted yet for this study

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

Interventions

DIAGNOSTIC_TEST

AI-Assisted Fracture Detection System

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.

DIAGNOSTIC_TEST

Standard Physician-Interpreted Fracture Detection

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

Sponsors & Collaborators

  • Klinikum Nürnberg

    collaborator OTHER

  • Salzburger Landeskliniken

    lead OTHER

Study Design

Allocation
RANDOMIZED
Purpose
DIAGNOSTIC
Masking
NONE
Model
PARALLEL

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-03-31
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