Maternal and neonatal health remains one of the most pressing global health challenges, particularly in low- and middle-income countries (LMICs). Ethiopia continues to face a high burden, with maternal mortality estimated at 195 per 100,000 live births, neonatal mortality at 27 per 1,000 live births, and perinatal mortality rates ranging from 37‰ to 124‰ depending on the level of care. These outcomes remain substantially higher than the targets set under the United Nations Sustainable Development Goals (SDGs) for 2030.
The World Health Organization (WHO) recommends that all pregnant women receive at least one ultrasound scan before 24 weeks of gestation, yet nearly two-thirds of women worldwide-especially in LMICs-lack access to this service. Barriers include high costs of ultrasound machines, limited technical expertise, and shortages of skilled sonographers in rural primary care.
Artificial Intelligence-driven Point-of-Care Ultrasound (AI-POCUS) represents a promising innovation to expand prenatal imaging in resource-constrained settings by equipping frontline health workers with AI-supported diagnostic capabilities. This study, conducted under the Tsinghua University BRIGHT (Bringing Research to Impact for Global Health at Tsinghua) program, will evaluate the clinical effectiveness, feasibility, cost, and scalability of AI-POCUS in rural Ethiopia. A three-arm cluster randomized controlled trial will compare two AI-enabled ultrasound technologies-BabyChecker (Netherlands) and a China-developed AI-POCUS device-against standard antenatal care without ultrasound. Findings will generate robust clinical and policy-relevant evidence to guide the sustainable implementation of AI-enabled maternal health interventions in sub-Saharan Africa.
Aged 15-49 years;
Gestational age less than 24 weeks at the first ANC visit;
No history of severe pregnancy complications (e.g., placenta previa, preeclampsia, etc.);
Signed informed consent and agreed to participate in the study.
Pregnant women with cognitive impairments or unable to communicate effectively;
Failure to complete antenatal care within the specified timeframe;
Incomplete or unavailable records of antenatal care and delivery.
Parallel
AI-POCUS (BabyChecker, Delft Imaging)
DeviceA portable AI-driven ultrasound system developed by Delft Imaging (Netherlands). The device integrates fetal position, amniotic fluid volume, and biparietal diameter measurements, with built-in diagnostic suggestions and risk alerts. Primary healthcare workers, after brief training, use it for antenatal screening, complication detection, and referral decision-making.
AI-POCUS (Edan, China)
DeviceAn AI-POCUS device developed by Edan (China), capable of analyzing blind ultrasound sweeps to extract fetal diagnostic parameters and assist in early risk identification. It supports clinical decision-making for antenatal care.
Maternal Mortality Ratio
Maternal deaths per 100,000 live births, defined as deaths occurring during pregnancy or within 42 days postpartum due to pregnancy-related causes.
Stillbirth Rate / Perinatal Mortality Rate
Stillbirths (≥28 weeks gestation) per 1,000 total births, and perinatal mortality including stillbirths and neonatal deaths within the first 7 days of life.
Early Neonatal Mortality Rate
Neonatal deaths within the first 7 days of life per 1,000 live births.
Preterm Birth Rate
Proportion of births before 37 completed weeks of gestation, subdivided into extremely preterm (\<32 weeks), very preterm (32-33 weeks), and late preterm (34-36 weeks).
Completion of ≥4/8 Antenatal Care (ANC) Visits
Proportion of pregnant women who completed at least 4 of 8 ANC visits, respectively, during pregnancy, according to WHO recommendations.
High-Risk Pregnancy Detection Rate
Proportion of pregnant women identified as high-risk (e.g., placenta previa, malpresentation) by AI-POCUS or conventional ultrasound, divided by total enrolled women.
High-Risk Pregnancy Follow-Up Completion Rate
Among women identified as high-risk, proportion who completed at least 1 or 2 follow-up ANC visits.
Referral Completion Rate After Screening
Proportion of women meeting high-risk pregnancy criteria who successfully completed referral to a higher-level hospital.
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Tsinghua University
Lead sponsor
Debre Berhan University
Collaborator
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