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

NHS Trust

Status: Completed

Conditions: Fractures, Open; Dislocation; Fracture; Fractures, Closed; Bone Fracture; Fracture Multiple

Treatments: Other: Cases reading; Other: Ground truthing

Study type: Observational

Funder types: Other; Industry

Identifiers

NCT06130397

310995-C

Details and patient eligibility

About

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.

Enrollment: 21 patients

Sex: All

Volunteers: Accepts Healthy Volunteers

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

Trial design

21 participants in 2 patient groups

Readers/participants

Description:

Reader Selection: 18 readers will be selected from the following five clinical specialty groups (3 readers each): * Emergency Medicine * Trauma and Orthopaedic Surgery * Emergency Nurse Practitioners * Physiotherapy * General Radiology * Radiographers And from the following level of seniority/experience: * Consultant/Senior/Equivalent - \>10yrs experience * Middle Grade/Registrar/Equivalent - 5-10yrs experience * Junior Grade/Senior House Officer/Equivalent - \<5yrs experience Each specialty reader group will include 1 reader at each level of experience. 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

Treatment:

Other: Cases reading

Ground truthers

Description:

Two consultant musculoskeletal radiologists. A third senior musculoskeletal radiologist's opinion (\>20 years experience) will undertake arbitration.

Treatment:

Other: Ground truthing