# AI-driven low-cost ultrasound for automated quantification of hypertension, preeclampsia, and IUGR

> **NIH NIH R01** · EMORY UNIVERSITY · 2022 · $655,280

## Abstract

PROJECT SUMMARY/ABSTRACT
Life-saving advances in medical care in recent decades have reduced global mortality rates but have
underperformed in addressing maternal mortality, stillbirth, and neonatal mortality. A key reason for these
disparities in both low- and high-income settings is the lack of systematic screening with appropriate and
affordable) technology for high priority conditions such as maternal hypertension and preeclampsia and fetal
growth restriction. The development of new low-cost diagnostic tools to improve access to detection of these
conditions by front-line workers would change outcomes for the most underserved populations, which is our
long-term goal. In an NICHD-funded study, we collected point of care Doppler ultrasound recordings and
developed a preliminary machine learning approach for detecting intrauterine growth restriction (IUGR) and
maternal hypertension. The overall objective of this proposal is to prospectively validate these findings in two
large underserved pregnancy cohorts in rural Guatemala and urban Georgia. Our general hypothesis is that our
low-cost artificial intelligence will perform as well in detecting maternal hypertension, preeclampsia, and IUGR
as standard-of-care high-cost diagnostic approaches. In Aim 1, we will validate our ultrasound-based IUGR
detection algorithm against the standard of care (2-dimensional fetal imaging). In Aim 2, we will validate
maternal hypertension and preeclampsia algorithms against gold-standard blood pressure devices and clinical
risk prediction tools. In Aim 3, we will implement real-time versions of the algorithms validated in Aims 1 and
2 and implement them on an edge-computing system for field testing. Successful completion of this proposal will
result in a novel and cost-effective approach to screening for maternal hypertension, preeclampsia, and IUGR
using point-of-care Doppler connected to a low-cost, AI-enabled edge-computing system, suitable for wide use
in low-resource settings. This proposal is innovative because it uses an artificial intelligence approach and
widely-available point-of-care Doppler devices to provide new approaches to timely detection of high-impact
maternal-fetal conditions. Our results will provide a strong basis for wide-scale deployment of new maternal and
fetal screening technology which is expected to have a significant impact on maternal and fetal morbidity by
improving access to timely screening. This research aligns with the NICHD's mission to advance knowledge of
pregnancy, fetal development, and birth by promoting strategies that prevent maternal, infant, and childhood
mortality and morbidity through lost-cost high-impact screening technology.

## Key facts

- **NIH application ID:** 10567313
- **Project number:** 1R01HD110480-01
- **Recipient organization:** EMORY UNIVERSITY
- **Principal Investigator:** Gari David Clifford
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $655,280
- **Award type:** 1
- **Project period:** 2022-09-20 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10567313

## Citation

> US National Institutes of Health, RePORTER application 10567313, AI-driven low-cost ultrasound for automated quantification of hypertension, preeclampsia, and IUGR (1R01HD110480-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10567313. Licensed CC0.

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