Low-cost AI ultrasound to detect high blood pressure, preeclampsia, and poor fetal growth
Official title: AI-driven low-cost ultrasound for automated quantification of hypertension, preeclampsia, and IUGR
Funded at Emory University · NIH-11161459 · Data updated 2026-11-12
This effort uses an inexpensive ultrasound with AI to find high blood pressure, preeclampsia, and fetal growth problems in pregnant people, especially in underserved communities.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Emory University NIH-funded |
| Lab location | 1 site (Atlanta, United States) |
| Project start | September 20, 2022 |
| Project end | August 31, 2027 |
| Funding year | 2025 |
| Award (this year) | $610,642 |
| NIH project number | 5R01HD110480-04 |
| Project ID | NIH-11161459 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal participants are pregnant people receiving prenatal care at participating clinics in rural Guatemala or urban Georgia, especially those at risk for hypertension, preeclampsia, or suspected fetal growth restriction.
✕Less likely to help
People who are not pregnant, those outside the study regions or clinics, or those already receiving full diagnostic imaging and hypertension care are unlikely to directly benefit from participating.
What this research studies
If I join, clinicians will use a low-cost ultrasound device that records fetal and maternal Doppler signals while an AI program analyzes them for signs of hypertension, preeclampsia, and intrauterine growth restriction (IUGR). The team will compare the AI results to standard 2-dimensional fetal imaging and gold-standard blood pressure measurements. The work will be carried out prospectively in two large underserved pregnancy groups—one in rural Guatemala and one in urban Georgia—to see how the tool performs across different settings. The AI was built from earlier point-of-care Doppler recordings and this project aims to validate those initial findings in real-world prenatal clinics.
Why it matters
Potential benefit: If successful, the tool could enable earlier and more affordable detection of hypertensive disorders and fetal growth problems, improving care access for underserved pregnant people.
How similar studies have gone: Preliminary work using point-of-care Doppler recordings with machine learning showed promising results, but large prospective validation across diverse populations is still novel.
Where this research is happening
-
Emory University
Atlanta, United States Active
Researchers
- Principal investigator: Clifford, Gari David — Emory University
- Study coordinator: Clifford, Gari David
Related research
Other NIH-funded projects on the same topics.
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AI-powered low-cost ultrasound to detect high-risk pregnancy conditions
A new, affordable ultrasound AI system helps frontline workers identify pregnancy complications like high blood pressure, preeclampsia, and restricted fetal growth.
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Using AI to create affordable ultrasound technology for detecting pregnancy complications
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Dynamic risk prediction and subtyping of hypertensive disorders in pregnancy
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Understanding how genes and environment interact to cause preeclampsia and later high blood pressure in mothers
This project looks at why some pregnancies develop preeclampsia and how it relates to long-term blood pressure and heart health in mothers.
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Children's long-term health after treating chronic hypertension in pregnancy
This grant studies how giving antihypertensive medicine during pregnancy for women with chronic high blood pressure affects their children’s health as they grow up.
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Reducing chronic high blood pressure after preeclampsia to protect heart and vessels
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.