FRACT-AI: A study comparing the finding of broken bones on X-Rays by artificial intelligence to the findings by clinicians of varying grades and professional backgrounds
Not Applicable
- Conditions
- ocation of fractures on plain X-rays by artificial intelligence and human clinicians.Not Applicable
- Registration Number
- ISRCTN19562541
- Lead Sponsor
- IHR Clinical Research Network
- Brief Summary
2024 Protocol article in https://pubmed.ncbi.nlm.nih.gov/39237277/ (added 06/09/2024)
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- Ongoing
- Sex
- All
- Target Recruitment
- 16
Inclusion Criteria
1. Healthcare professional from the following professions/specialities:
1.1. Emergency medicine physicians
1.2. Surgeons in trauma and orthopaedics
1.3. Radiologists
1.4. Radiographers
1.5. Physiotherapists
1.6. Emergency nurse practitioners
Exclusion Criteria
1. Not from the above listed professions (emergency medicine physicians, surgeons in trauma and orthopaedics, radiologists, radiographers, physiotherapists, emergency nurse practitioners.)
2. Radiologists already musculoskeletal specialists,
Study & Design
- Study Type
- Observational
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Clinician readers will be asked to identify the presence or absence of fracture by placing a marker on the image at the location of the fracture (if present) and to rank their confidence for fracture identification. Confidence rating will take the form of a Likert scale from 1 to 10, 1 being least confident, 10 being very confident).
- Secondary Outcome Measures
Name Time Method There are no secondary outcome measures