Ignite Creation Date:
2024-05-06 @ 7:47 PM
Last Modification Date:
2024-10-26 @ 3:13 PM
Study NCT ID:
NCT06130397
Status:
RECRUITING
Last Update Posted:
2024-06-21
First Post:
2023-11-08
Brief Title:
AI Assisted Detection of Fractures on X-Rays FRACT-AI
Sponsor:
Oxford University Hospitals NHS Trust
Organization:
Oxford University Hospitals NHS Trust
Study Overview
Official Title:
FRACT-AI Evaluating the Impact of Artificial Intelligence-Enhanced Image Analysis on the Diagnostic Accuracy of Frontline Clinicians in the Detection of Fractures on Plain X-Ray
Status:
RECRUITING
Status Verified Date:
2024-06
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
FRACT-AI
Brief Summary:
This study has been added as a sub study to the Simulation Training for Emergency Department Imaging 2 study ClinicalTrialsgov ID NCT05427838 This work aims to evaluate the impact of an Artificial Intelligence AI-enhanced algorithm called Boneview on the diagnostic accuracy of clinicians in the detection of fractures on plain XR X-Ray The study will create a dataset of 500 plain X-Rays involving standard images of all bones other than the skull and cervical spine with 50 normal cases and 50 containing fractures A reference ground truth for each image to confirm the presence or absence of a fracture will be established by a senior radiologist panel This dataset will then be inferenced by the Gleamer Boneview algorithm to identify fractures Performance of the algorithm will be compared against the reference standard The study will then undertake a Multiple-Reader Multiple-Case study in which clinicians interpret all images without AI and then subsequently with access to the output of the AI algorithm 18 clinicians will be recruited as readers with 3 from each of six distinct clinical groups Emergency Medicine Trauma and Orthopedic Surgery Emergency Nurse Practitioners Physiotherapy Radiology and Radiographers with three levels of seniority in each group Changes in reporting accuracy sensitivity specificity confidence and speed of readers in two sessions will be compared The results will be analyzed in a pooled analysis for all readers as well as for the following subgroups Clinical role Level of seniority Pathological finding Difficulty of image The study will demonstrate the impact of an AI interpretation as compared with interpretation by clinicians and as compared with clinicians using the AI as an adjunct to their interpretation The study will represent a range of professional backgrounds and levels of experience among the clinical element The study will use plain film x-rays that will represent a range of anatomical views and pathological presentations however x-rays will present equal numbers of pathological and non-pathological x-rays giving equal weight to assessment of specificity and sensitivity Ethics approval has already been granted and the study will be disseminated through publication in peer-reviewed journals and presentation at relevant conferences
Detailed Description:
None
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
False
Is an FDA AA801 Violation?:
None