Testing an artificial intelligence algorithm for detecting newborn hip dysplasia on ultrasound scans

Not Applicable

Conditions: Developmental dysplasia of the hip in newborns, diagnosed by ultrasound scan
Neonatal Diseases

Registration Number: ISRCTN49436239

Lead Sponsor: niversity of Oxford

Brief Summary: Not available

Detailed Description: Not available

Recruitment & Eligibility

Status: Ongoing

Sex: All

Target Recruitment: 10

Inclusion Criteria

Consultants/attendings (specialising in Paediatric Orthopaedic Surgery) and registrars/residents. Specialist physiotherapists who take part in hip screening as part of their clinical practice.

Exclusion Criteria

Any healthcare professional who does not review newborn hip ultrasound scans (either autonomously or under direct supervision) in their clinical practice

Study & Design

Study Type: Observational

Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Reader and AI algorithm performance will be evaluated as sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and Area Under Receiver Operating Characteristic Curve (AUC). Where the hip is abnormal on the ultrasound and readers correctly identify this classification as abnormal, it will be counted as a true positive, an incorrect diagnosis of normal by the reader will be a false negative. Where the hip is normal on the ultrasound, its correct classification by the reader will be a true negative and an incorrect classification will be a false positive.<br><br>The performance measures listed above will be compared for each reader with and without AI assistance. The performance of the AI algorithm alone will also be evaluated as a comparative measure.

Secondary Outcome Measures

Name	Time	Method
Reader speed will be evaluated as the mean review time per scan, with and without AI assistance. Reader confidence will be evaluated via a self-reported score (scale of 1 to 5, 1= not confident to 5 = fully confident), with and without AI assistance.