Low-cost AI ultrasound to detect high blood pressure, preeclampsia, and poor fetal growth
AI-driven low-cost ultrasound for automated quantification of hypertension, preeclampsia, and IUGR
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 ID | NIH-11161459 on NIH RePORTER |
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.
Who could benefit from this research
Good fit: 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.
Not a fit: 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.
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 performed: 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
Atlanta, United States
- Emory University — Atlanta, United States (Active)
Researchers
- Principal investigator: Clifford, Gari David — Emory University
- Study coordinator: Clifford, Gari David
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 below.