Clinical trial NCT06130397: AI Assisted Detection of Fractures on X-Rays (FRACT-AI)
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
Study type: OBSERVATIONAL
Overall status: ACTIVE_NOT_RECRUITING
Conditions: Fracture
Bone Fracture
Dislocation
Fracture Multiple
Fractures, Closed
Fractures, Open
Keywords: radiology
emergency medicine
artificial intelligence
XRays
Fractures
Lead sponsor: Oxford University Hospitals NHS Trust
Sponsor class: OTHER
Collaborators: name: Gleamer
First posted: 2023-11-14
Enrollment: 21
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Emergency medicine doctors, trauma and orthopaedic surgeons, emergency nurse practitioners, physiotherapists, general radiologists and radiographers reviewing X-rays as part of their routine clinical practice, currently working in the National Health Service (NHS).

Readers will be recruited from across 5 NHS organisations which comprise the Thames Valley Emergency Medicine Research Network (www.TaVERNresearch.org):

* Oxford University Hospitals NHS Foundation Trust
* Royal Berkshire NHS Foundation Trust
* Buckinghamshire Healthcare NHS Trust
* Frimley Health NHS Foundation Trust
* Milton Keynes University Hospital NHS Foundation Trust

Brief summary: This study has been added as a sub study to the Simulation Training for Emergency Department Imaging 2 study (ClinicalTrials.gov 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.


Eligibility criteria: Inclusion Criteria:

* Emergency medicine doctors, trauma and orthopaedic surgeons, emergency nurse practitioners, physiotherapists, general radiologists and radiographers reviewing X-rays as part of their routine clinical practice.
* Currently working in the National Health Service (NHS).

Exclusion Criteria:

* Non-radiology physicians with previous formal postgraduate XR reporting training.
* Non-radiology physicians with previous career in radiology

Primary outcomes:
measure: Performance of AI algorithm: sensitivity
timeFrame: During 4 weeks of reading time
measure: Performance of AI algorithm: specificity
timeFrame: During 4 weeks of reading time
measure: Performance of AI algorithm: Area under the ROC Curve (AU ROC)
timeFrame: During 4 weeks of reading time
measure: Performance of readers with and without AI assistance: Sensitivity
timeFrame: During 4 weeks of reading time
measure: Performance of readers with and without AI assistance: Specificity
timeFrame: During 4 weeks of reading time
measure: Performance of readers with and without AI assistance: Area under the ROC Curve (AU ROC)
timeFrame: During 4 weeks of reading time
measure: Reader speed with vs without AI assistance.
timeFrame: During 4 weeks of reading time
Secondary outcomes:
Other outcomes:
Locations:
city: Oxford
country: United Kingdom
facility: Oxford University Hospitals NHS Foundation Trust
state: Oxfordshire
Study officials:
References:
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