AI Estimation of Gestational Age
Research type
Research Study
Full title
Evaluation of AI algorithm for Gestational Age Estimation
IRAS ID
356332
Contact name
Kypros Nicolaides
Contact email
Sponsor organisation
King's College Hospital NHS Foundation Trust
Duration of Study in the UK
0 years, 4 months, 0 days
Research summary
Accurate estimation of gestational age (GA) is essential for optimal antenatal care, influencing screening, diagnosis, and clinical decision-making throughout pregnancy. The standard method relies on ultrasound measurements of fetal biometry, such as crown-rump length (CRL) in the first trimester or head circumference (HC) in later stages, performed by trained sonographers. However, these measurements require expertise and can be time-consuming.
This observational study aims to evaluate an artificial intelligence (AI)-based GA estimation algorithm developed by General Electric Healthcare (GEHC). The AI system processes real-time ultrasound video streams to estimate GA without requiring specific biometric measurements. This approach has been tested in low- and middle-income countries, demonstrating promising accuracy comparable to conventional methods. Further validation in routine clinical settings is necessary before considering broader implementation.
The study will be conducted at the Fetal Medicine Research Institute, King’s College Hospital, London, and will involve 800 participants across a range of gestational ages (8 to 34 weeks). Participants will undergo standard fetal biometry assessments, and the AI system will generate GA estimates. These estimates will remain blinded, will not be displayed to clinicians or patients, and will not influence clinical care.
The primary objective is to assess whether the AI-based GA estimation is non-inferior to standard biometric methods. Secondary objectives include evaluating the time required for AI-based GA estimation and determining the proportion of scans for which the AI system returns a GA estimate.
REC name
London - Surrey Research Ethics Committee
REC reference
25/LO/0426
Date of REC Opinion
6 Jun 2025
REC opinion
Favourable Opinion