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RECRUITING
NCT06754137
NA

Assessing AI-Supported Fracture Detection in Emergency Care Units

Sponsor: Salzburger Landeskliniken

View on ClinicalTrials.gov

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.

Official title: Evaluating the Cost-Efficiency and Workflow Impact of AI-Supported Fracture Detection in an Orthopedic Emergency Care Unit

Key Details

Gender

All

Age Range

Any - Any

Study Type

INTERVENTIONAL

Enrollment

4800

Start Date

2025-03-31

Completion Date

2026-04-30

Last Updated

2026-01-22

Healthy Volunteers

No

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.

Locations (3)

Landesklinik Hallein, Salzburger Landeskliniken

Hallein, Austria

University Hospital Salzburg, Salzburger Landeskliniken

Salzburg, Austria

University Hosptial Nuremberg, Klinikum Nürnberg

Nuremberg, Germany