Risk screening for pre-eclampsia relies on accurate gestational age assessment, but routine access to ultrasound-based gestational dating remains challenging in many low- and middle-income countries. As part of the formative work for the ‘Preventing pre-eclampsia: Evaluating AspiRin Low-dose regimens following risk Screening’ (PEARLS) platform, we aim to validate and implement an artificial intelligence (AI)-based algorithm for estimation of gestational age, using blind sweeps done with a handheld ultrasound device. This study protocol outlines the accuracy cohort for AI-based gestational age estimation in participating facilities in Ghana, Kenya and South Africa.

This multicountry prospective cohort study will recruit 969 pregnant women at 13 health facilities across Kenya, Ghana and South Africa. The eligible population is pregnant women presenting for antenatal visits from 11⁺⁰ to 13⁺⁶ weeks’ gestation. Eligible women will have a gestational age assessment by a trained sonographer using fetal biometry (reference standard), followed by gestational age estimation conducted by a trained midwife using the AI-based Intelligent Ultrasound ScanNav FetalCheck system (experimental). Both conventional and AI-based gestational age scans will be conducted with the General Electric VScan Air platform. Women will return for a second visit between 14⁺⁰ and 27⁺⁶ weeks’ gestation (week of visit is randomly selected) for an assessment with both conventional and AI-based ultrasound. The primary objective is to determine the accuracy and precision of gestational age estimation using an AI ultrasound system in first and second trimesters, as compared with gestational age estimation using crown-rump length measurement by conventional ultrasound in first trimester (11⁺⁰ to 13⁺⁶ weeks’).

This study has received or sought ethics approval from the following entities: Australia: University of Melbourne, Office of Research Ethics and Integrity (Reference Number: 2024–28489-49438-3) and the Alfred Hospital Ethics Committee (Reference: Project 727/23); Ghana: Ghana Health Service Ethics Review Committee (GHS-ERC Number 002/01/24); Kenya: Kenyatta National Hospital, University of Nairobi ERC (Ref: KNH-ERC/01/MISC/20); South Africa: University of Cape Town, Faculty of Health Science, Human Research Ethics Committee (HREC Ref: 138/2024). Key findings will be disseminated to research teams to inform future scale-up of AI-based pregnancy dating and pre-eclampsia risk screening. Findings from this pilot work will be published in peer-reviewed open-access journals, conferences and meetings to maximise reach of our findings.