AI Assisted Detection of Fractures on X-Rays (FRACT-AI)

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.

Conditions

• Fracture
• Bone Fracture
• Dislocation
• Fracture Multiple
• Fractures, Closed
• Fractures, Open

Interventions

OTHER

Cases reading

The reading will be done remotely via the Report and Image Quality Control site (www.RAIQC.com), an online platform allowing medical imaging viewing and reporting. Participants can work from any location, but the work must be done from a computer with internet access. For avoidance of doubt, the work cannot be performed from a phone or tablet. The project is divided into two phases and participants are required to complete both phases. The estimated total involvement in the project is up to 20-24 hours. Phase 1: Time allowed: 2 weeks \- Participants must review 500 X-rays and express a clinical opinion through a structured reporting template (multiple choice, no open text required). Rest/washout period - Time allowed: 4 weeks, to mitigate the effects of recall bias. Phase 2 - Time allowed: 2 weeks \- Review 500 X-rays together with an AI report for each case and express their clinical opinion through the same structured reporting template used in Phase 1.

OTHER

Ground truthing

Two consultant musculoskeletal radiologists will independently review the images to establish the 'ground truth' findings on the XRs, where a consensus is reached this will then be used as the reference standard. In the case of disagreement, a third senior musculoskeletal radiologist's opinion (\>20 years experience) will undertake arbitration. A difficulty score will be assigned to each abnormality by the ground truthers using a 4-point Likert scale (1 being easy/obvious to 4 being hard/poorly visualised).

Sponsors & Collaborators

• Gleamer

  collaborator INDUSTRY

• Oxford University Hospitals NHS Trust

  lead OTHER

Eligibility

Sex

ALL

Healthy Volunteers

Yes

Timeline & Regulatory

Start

2024-02-08

Primary Completion

2024-10-31

Completion

2025-06-01

Countries

• United Kingdom

Study Locations