AI-POCUS to Improve Maternal and Neonatal Health Outcomes in Rural Ethiopia: A Three-Arm Cluster Randomized Controlled Trial
试验速览
- 阶段
- 4 期
- 状态
- 尚未招募
- 入组人数
- 1,059
- 试验地点
- 2
- 主要终点
- Maternal Mortality Ratio
研究概览
简要总结
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.
详细描述
Maternal and neonatal morbidity and mortality remain unacceptably high in sub-Saharan Africa and continue to impede progress toward global health targets. In Ethiopia, recent estimates show maternal mortality at 195 per 100,000 live births and neonatal mortality at 27 per 1,000 live births. Perinatal mortality is also elevated, ranging between 66‰ and 124‰ in hospital-based settings and 37‰ to 52‰ in community-level health facilities. These figures surpass the Sustainable Development Goal (SDG) thresholds for 2030, underscoring the urgent need for innovative, scalable solutions.
Ultrasound imaging is a cornerstone of modern antenatal care. The WHO recommends at least one ultrasound before 24 weeks' gestation to assess gestational age, detect multiple pregnancies, identify fetal anomalies, and diagnose high-risk conditions such as preeclampsia, placenta previa, or growth restriction. However, nearly two-thirds of pregnant women worldwide still lack access to this basic diagnostic tool. In low-resource environments, the barriers include limited infrastructure, high equipment costs, technical complexity, and the scarcity of trained professionals capable of performing and interpreting scans. As a result, potentially preventable maternal and neonatal deaths remain common.
Artificial Intelligence-driven Point-of-Care Ultrasound (AI-POCUS) introduces a transformative opportunity to address these gaps. POCUS devices embedded with AI algorithms can guide non-specialist health workers in image acquisition and interpretation, reducing reliance on highly trained personnel and lowering barriers to integration within primary care. Such innovations may strengthen early detection of pregnancy complications, enable timely referral to higher-level care, and ultimately improve maternal and neonatal survival.
This study is embedded within the Bringing Research to Impact for Global Health at Tsinghua (BRIGHT) initiative. It will use a three-arm cluster randomized controlled trial (C-RCT) design to evaluate and compare: (1) BabyChecker, a portable AI-enabled ultrasound developed in the Netherlands, (2) A China-developed AI-POCUS device, and (3) Standard antenatal care (ANC) without ultrasound, reflecting current practice in many rural Ethiopian communities.
The study population will include pregnant women receiving antenatal care in rural Ethiopia, as well as primary health care providers delivering these services. Data will be collected at both the patient and facility level to capture maternal and neonatal health outcomes, health service utilization, and system-level performance indicators.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Screening
- 盲法
- Single (Outcomes Assessor)
盲法说明
Other parties masked in this trial include the data analysts, who will remain blinded to group assignments during statistical analyses to minimize bias in outcome assessment.
入排标准
- 年龄范围
- 15 Years 至 49 Years（Child, Adult）
- 性别
- Female
- 接受健康志愿者
- 是
入选标准
- •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.
排除标准
- •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.
结局指标
主要结局
Maternal Mortality Ratio
时间窗: Baseline through 42 days postpartum
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
时间窗: Delivery through 7 days postpartum
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
时间窗: Birth through 7 days postpartum
Neonatal deaths within the first 7 days of life per 1,000 live births.
Preterm Birth Rate
时间窗: At delivery
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).
Maternal and Neonatal Referral Rate
时间窗: Antenatal period through 42 days postpartum
Proportion of mothers or newborns referred to higher-level hospitals due to severe complications.
Congenital Anomaly Rate
时间窗: Antenatal period through delivery
Proportion of infants with major structural anomalies detected by prenatal ultrasound or confirmed postnatally (e.g., neural tube defects, limb malformations, cleft lip/palate).
次要结局
- Completion of ≥4/8 Antenatal Care (ANC) Visits(Pregnancy through delivery)
- High-Risk Pregnancy Detection Rate(Pregnancy through delivery)
- High-Risk Pregnancy Follow-Up Completion Rate(Pregnancy through delivery)
- Referral Completion Rate After Screening(Pregnancy through delivery)
研究者
Yuxuan LI
Doctoral Candidate
Tsinghua University