AI-POCUS for Maternal and Neonatal Health in Ethiopia

NCT ID: NCT07171086

Last Updated: 2025-09-12

Basic Information

• Recruitment Status: NOT_YET_RECRUITING
• Clinical Phase: PHASE4
• Total Enrollment: 1059 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2025-09-30
• Study Completion Date: 2026-08-31

Brief Summary

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.

Detailed Description

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.

Evaluation will follow a multi-dimensional framework, addressing:

1. Clinical effectiveness: improved detection of high-risk pregnancies, reduced maternal and neonatal complications, and mortality.

2. Implementation feasibility and acceptability: user experience among health workers and pregnant women, integration into routine workflows, and perceived trust in AI-assisted care.

3. Economic evaluation: cost and cost-effectiveness of AI-POCUS compared to standard ANC, including resource utilization, referral patterns, and potential savings from earlier detection.

4. Scalability and policy relevance: analysis of barriers and enablers for broader adoption in Ethiopia and similar LMIC contexts, with direct input from policymakers and health system stakeholders.

The study aims to provide rigorous clinical evidence and practical implementation guidance on how AI-POCUS technologies can be sustainably scaled in resource-constrained settings. Findings are expected to inform national health policies, guide investment decisions, and offer a replicable model for expanding maternal health technologies across sub-Saharan Africa and other LMICs.

Conditions

Pregnancy; Pregnancy Complications; Preterm Birth; Fetal Growth Restriction; Stillbirth and Fetal Death; Pregnancy Abnormal

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: SCREENING
• Blinding Strategy: SINGLE

Study Groups

Standard Care Control

Participants in this arm will receive routine antenatal care (ANC) according to Ethiopian national guidelines, without the use of AI-POCUS devices. All examinations, screenings, and referrals will be conducted through standard clinical practice. This group serves as the baseline comparator for evaluating the added impact of AI-POCUS technology.

Group Type: NO_INTERVENTION

No interventions assigned to this group

BabyChecker (Delft Imaging, Netherlands)

Health centers in this arm will be equipped with the BabyChecker system developed by Delft Imaging (Netherlands). The portable device integrates fetal position, amniotic fluid volume, and biparietal diameter measurements, and provides diagnostic suggestions and risk alerts. After brief training, primary healthcare workers will independently perform antenatal examinations, screen for obstetric complications, and make referral decisions.

Group Type: EXPERIMENTAL

AI-POCUS (BabyChecker, Delft Imaging)

Intervention Type: DEVICE

A 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)

This arm will use the AI-POCUS device developed by Edan (China), designed to analyze blind ultrasound sweeps and automatically extract fetal diagnostic parameters. The system supports the early detection of maternal and fetal risks and assists in clinical decision-making.

Group Type: EXPERIMENTAL

AI-POCUS (Edan, China)

Intervention Type: DEVICE

An 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.

Interventions

AI-POCUS (BabyChecker, Delft Imaging)

A 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.

Intervention Type: DEVICE

AI-POCUS (Edan, China)

An 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.

Intervention Type: DEVICE

Eligibility Criteria

Inclusion Criteria

1. Aged 15-49 years;

2. Gestational age less than 24 weeks at the first ANC visit;

3. No history of severe pregnancy complications (e.g., placenta previa, preeclampsia, etc.);

4. Signed informed consent and agreed to participate in the study.

Exclusion Criteria

1. Pregnant women with cognitive impairments or unable to communicate effectively;

2. Failure to complete antenatal care within the specified timeframe;

3. Incomplete or unavailable records of antenatal care and delivery.

• Minimum Eligible Age: 15 Years
• Maximum Eligible Age: 49 Years
• Eligible Sex: FEMALE
• Accepts Healthy Volunteers: Yes

Sponsors

Debre Berhan University

OTHER

Sponsor Role: collaborator

Tsinghua University

OTHER

Sponsor Role: lead

Responsible Party

Yuxuan LI

Doctoral Candidate

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Kun TANG, Associate Professor

Role: STUDY_CHAIR

Tsinghua University

Locations

Hakim Gizaw Hospital

Debre Berhan, Amhara, Ethiopia

Countries

Ethiopia

Central Contacts

Yuxuan LI, Doctoral Candidate

Role: CONTACT

li-yx23@mails.tsinghua.edu.cn

+86-18813076657

Facility Contacts

Tesfanesh Demisse

Role: primary

tesfitimnt@gmail.com

+251910901201

Other Identifiers

BRIGHT (202430045)

Identifier Type: OTHER_GRANT

Identifier Source: secondary_id

PROJECT #: BRIGHT (202430045)

Identifier Type: -

Identifier Source: org_study_id