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Assessing AI-Supported Fracture Detection in Emergency Care Units
Sponsor: Salzburger Landeskliniken
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
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.
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