Acute fractures in otherwise healthy children (i.e. no underlying skeletal dysplasia, metabolic bone disease) Musculoskeletal Diseases
Conditions
Interventions
Current interventions as of 30/08/2024:
A retrospective dataset of 500 scans will be compiled, to include fractures across 4 body parts in children (older than 2 years old, but less than 16 years old
both genders). The body parts include ankles, wrists, elbows and knees. There will therefore be 125 scans per 4 body parts, with each body part being approximately 35% abnormal (i.e. each body part =
Sponsors
Great Ormond Street Hospital for Children NHS Foundation Trust
Eligibility
Sex/Gender
All
Inclusion criteria
Inclusion criteria: Radiographic 'readers' will include radiology consultants and registrars (either general, musculoskeletal or paediatric subspecialty interests), emergency medicine consultants and registrars, orthopaedic surgical consultants and registrars and reporting radiographers who review paediatric limb radiographs as part of their clinical practice
Exclusion criteria
Exclusion criteria: Any doctor, nurse, radiographer who does not routinely review paediatric radiographs in their clinical practice or for their job.
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Reader and AI performance of the paediatric X-rays will be evaluated using measures of sensitivity, specificity, positive predictive value, negative predictive value and accuracy, where each correctly identified fracture on an Xray (where one exists) will be counted as a true positive, and each incorrectly identified fracture on an Xray (i.e. an overcall) will be counted as a false positive. Where fractures are present but not identified by the reader, this will constitute a false negative. Where no fracture exists, and none is identified by the reader, this will count as a true negative. The performance measures listed above will be compared for each reader before and after using AI assistance in interpretation of the X-rays. The performance of the AI tool alone will also be evaluated (without a human in the loop) for comparative measure. | — |
Secondary
| Measure | Time frame |
|---|---|
| 1. The reader confidence in their diagnostic ability to identify or confirm the absence of a fracture per Xray will be measured using a survey provided at the time of reviewing each Xray on the image viewer platform using a 5 point Likert scale (1 = not confident, 5 = very confident). Differences will be compared in these scores before and after the use of the AI tool. 2. The readers’ intended management plan (for the patient) based on the Xray will be provided in a drop down menu (7 options available) provided on the image viewer platform next to each Xray the reader has to interpret. The reader will need to select the single best option they would follow. The differences in theoretical management choices will be compared before and after the use of the AI tool. | — |
Countries
England, Northern Ireland, Scotland, United Kingdom, Wales
Contacts
Public ContactSusan Shelmerdine
Outcome results
None listed