PregnancyPhase 4AIM-ETHNCT07171086
AI-POCUS for Maternal and Neonatal Health in Ethiopia
A Phase 4 study testing AI-POCUS (BabyChecker, Delft Imaging) and AI-POCUS (Edan, China) in people with Pregnancy and Pregnancy Complications, run by Tsinghua University at 1 site in Ethiopia.
Trial 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… Read more about this study
- Who is it for?
- Ages 15 to 49 years, women onlyHealthy volunteers can take part
- Conditions the study is for
- Pregnancy, Pregnancy Complications, Preterm Birth
- What is being tested?
- AI-POCUS (BabyChecker, Delft Imaging) and AI-POCUS (Edan, China)
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 1,059 participants
- Where
- 1 siteEthiopia
- Study dates
- 30 Sep 2025to 31 Aug 2026
- Phase
- Phase 4
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 15 to 49 years
- Women only
- Healthy volunteers accepted
- Child, Adult
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Ethiopia1 site
Hakim Gizaw Hospital
Debre Berhan, Amhara, 1000
Tesfanesh Demisse, +251910901201, tesfitimnt@gmail.com
About this study
Official title: AI-POCUS to Improve Maternal and Neonatal Health Outcomes in Rural Ethiopia: A Three-Arm Cluster Randomized Controlled Trial
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.
Read the 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:
- Clinical effectiveness: improved detection of high-risk pregnancies, reduced maternal and neonatal complications, and mortality.
- Implementation feasibility and acceptability: user experience among health workers and pregnant women, integration into routine workflows, and perceived trust in AI-assisted care.
- Economic evaluation: cost and cost-effectiveness of AI-POCUS compared to standard ANC, including resource utilization, referral patterns, and potential savings from earlier detection.
- 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
- Being tested
- AI-POCUS (BabyChecker, Delft Imaging), AI-POCUS (Edan, China)
- Keywords
- Artificial Intelligence, Point-of-Care Ultrasound (POCUS), Maternal and Neonatal Health, Cluster Randomized Controlled Trial, Sub-Saharan Africa, Rural Centers
- Medical subject headings
- Pregnancy Complications, Premature Birth, Fetal Growth Retardation, Stillbirth, Fetal Death
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-POCUS (BabyChecker, Delft Imaging)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)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.
| Study group | Role | What they receive |
|---|---|---|
| Standard Care Control | No intervention | 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. |
| BabyChecker (Delft Imaging, Netherlands) | Experimental | 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. Receives: Device: AI-POCUS (BabyChecker, Delft Imaging) |
| AI-POCUS (Edan, China) | Experimental | 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. Receives: Device: AI-POCUS (Edan, China) |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Baseline through 42 days postpartum
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.
Measured: Delivery through 7 days postpartum
Early Neonatal Mortality Rate
Neonatal deaths within the first 7 days of life per 1,000 live births.
Measured: Birth through 7 days postpartum
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).
Measured: At delivery
Maternal and Neonatal Referral Rate
Proportion of mothers or newborns referred to higher-level hospitals due to severe complications.
Measured: Antenatal period through 42 days postpartum
Congenital Anomaly Rate
Proportion of infants with major structural anomalies detected by prenatal ultrasound or confirmed postnatally (e.g., neural tube defects, limb malformations, cleft lip/palate).
Measured: Antenatal period through delivery
Secondary outcomes (4)
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.
Measured: Pregnancy through delivery
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.
Measured: Pregnancy through delivery
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.
Measured: Pregnancy through delivery
Referral Completion Rate After Screening
Proportion of women meeting high-risk pregnancy criteria who successfully completed referral to a higher-level hospital.
Measured: Pregnancy through delivery
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Sep 2025
- Study start30 Sep 2025 (estimated)
- Primary completion31 Mar 2026 (estimated)
- Study completion31 Aug 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for PregnancyStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase 4
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- This study will employ a cluster randomized controlled trial design, with primary health care centers serving as the cluster (intervention) units and individual pregnant women as the primary observational units. A total of nine health centers will be selected and matched based on geographic location, maternal mortality rates, and the service capacity of health care personnel. The matched health centers will then be randomly assigned in a 1:1:1 ratio to one of three study arms, with each arm including three health centers. A total of 1,059 pregnant women will be recruited, with 353 participants per study arm. Interventions will be implemented at the cluster level, while outcomes - including maternal and neonatal health indicators - will be assessed at the individual participant level. This design allows for the evaluation of the effectiveness and feasibility of AI-POCUS interventions while accounting for intra-cluster correlation and contextual variability across health centers.
- Masking
- Single blind
- Who is masked
- Outcomes assessor
- Enrolment
- 1,059 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Tsinghua University
- Sponsor type
- University, hospital or other
- Collaborators
- Debre Berhan University
- Responsible party
- Principal investigator, Yuxuan LI, Tsinghua University
- Investigators
- Kun TANG, Associate Professor, Study chair, Tsinghua University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07171086
- Sponsor's protocol ID
- PROJECT #: BRIGHT (202430045)
- Other IDs
- BRIGHT (202430045)
- Acronym
- AIM-ETH
- Status last verified
- Sep 2025
- First submitted
- 5 Sep 2025
- Last updated
- 12 Sep 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided: Undecided
Study at a glance
From the registry record, last updated by the sponsor on 12 Sep 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- AI-POCUS to Improve Maternal and Neonatal Health Outcomes in Rural Ethiopia: A Three-Arm Cluster Randomized Controlled Trial
- ClinicalTrials.gov ID
- NCT07171086
- Acronym / sponsor ID
- AIM-ETH / PROJECT #: BRIGHT (202430045)
- Conditions
- Pregnancy, Pregnancy Complications, Preterm Birth, Fetal Growth Restriction, Stillbirth and Fetal Death, Pregnancy Abnormal
- Keywords
- Artificial Intelligence, Point-of-Care Ultrasound (POCUS), Maternal and Neonatal Health, Cluster Randomized Controlled Trial, Sub-Saharan Africa, Rural Centers
- Phase
- Phase 4
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 1,059 participants (estimated)
- Lead sponsor
- Tsinghua UniversityUniversity, hospital or other
- Collaborators
- Debre Berhan University
- Study start
- 30 Sep 2025 (estimated)
- Primary completion
- 31 Mar 2026 (estimated)
- Study completion
- 31 Aug 2026 (estimated)
- First posted
- 12 Sep 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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:
- Clinical effectiveness: improved detection of high-risk pregnancies, reduced maternal and neonatal complications, and mortality.
- Implementation feasibility and acceptability: user experience among health workers and pregnant women, integration into routine workflows, and perceived trust in AI-assisted care.
- Economic evaluation: cost and cost-effectiveness of AI-POCUS compared to standard ANC, including resource utilization, referral patterns, and potential savings from earlier detection.
- 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.
Study design
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (outcomes assessor)
- Number of arms
- 3
- Model description
- This study will employ a cluster randomized controlled trial design, with primary health care centers serving as the cluster (intervention) units and individual pregnant women as the primary observational units. A total of nine health centers will be selected and matched based on geographic location, maternal mortality rates, and the service capacity of health care personnel. The matched health centers will then be randomly assigned in a 1:1:1 ratio to one of three study arms, with each arm including three health centers. A total of 1,059 pregnant women will be recruited, with 353 participants per study arm. Interventions will be implemented at the cluster level, while outcomes - including maternal and neonatal health indicators - will be assessed at the individual participant level. This design allows for the evaluation of the effectiveness and feasibility of AI-POCUS interventions while accounting for intra-cluster correlation and contextual variability across health centers.
| Arm | Type | Receives |
|---|---|---|
| Standard Care Control | No intervention | –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. |
| BabyChecker (Delft Imaging, Netherlands) | Experimental | Device: AI-POCUS (BabyChecker, Delft Imaging)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. |
| AI-POCUS (Edan, China) | Experimental | Device: 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. |
Who may qualify
- Ages
- 15 to 49 years
- Sex
- Female only
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult
Key inclusion criteria (4 listed)
- 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.
Key exclusion criteria (3 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| 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. | Device | BabyChecker (Delft Imaging, Netherlands) |
| 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. | Device | AI-POCUS (Edan, China) |
Primary outcome
Maternal Mortality Ratio
Time frame: 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
Time frame: 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
Time frame: Birth through 7 days postpartum
Neonatal deaths within the first 7 days of life per 1,000 live births.
Preterm Birth Rate
Time frame: 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
Time frame: Antenatal period through 42 days postpartum
Proportion of mothers or newborns referred to higher-level hospitals due to severe complications.
Congenital Anomaly Rate
Time frame: 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).
4 secondary outcomes
Completion of ≥4/8 Antenatal Care (ANC) Visits
Time frame: Pregnancy through delivery
High-Risk Pregnancy Detection Rate
Time frame: Pregnancy through delivery
High-Risk Pregnancy Follow-Up Completion Rate
Time frame: Pregnancy through delivery
Referral Completion Rate After Screening
Time frame: Pregnancy through delivery
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 12 Sep 2025.
- Individual participant dataSharing planned: Undecided. Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Kun TANG, Associate Professor study chair
Tsinghua University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A Phase 4 study testing AI-POCUS (BabyChecker, Delft Imaging) and AI-POCUS (Edan, China) in people with Pregnancy and Pregnancy Complications, run by Tsinghua University at 1 site in Ethiopia.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 12 Sep 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Ethiopia. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
What does this phase mean?
Phase 4 studies take place after a treatment is approved, to learn more about its long-term benefits and risks in everyday use.
Not every study follows the same path, and some studies (observational studies, device studies) have no phase.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Yuxuan LI, Doctoral Candidate (+86-18813076657, li-yx23@mails.tsinghua.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Maternal Mortality Ratio (time frame: Baseline through 42 days postpartum). The study lists 6 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 12 Sep 2025.