Non-invasive prenatal monitoring to support assessment of maternal blood pressure and fetal cardiac activity using smartphone based 1D Doppler ultrasound and AI driven signal analysis.
Perinatal morbidity and mortality remain major public health challenges worldwide, particularly in low‑resource countries and underserved rural communities in the United States, where poverty is strongly linked to increased infant mortality. Although prenatal care is proven to reduce these outcomes through early detection of maternal and fetal health risks, many regions lack reliable access to care, or the diagnostic tools needed to identify complications. This creates a substantial and growing unmet need for affordable, accessible technologies that can extend effective prenatal screening beyond traditional clinical settings. Solutions that enable earlier, more accurate identification of risk have the potential to significantly improve infant survival while addressing a large, underserved global market.
Emory inventors have developed software algorithms that process data from a low cost, one dimensional Doppler ultrasound device integrated with a smartphone. The system enables caregivers to capture, record, and analyze fetal cardiac signals during routine prenatal monitoring. Using artificial intelligence techniques applied to Doppler signals, the technology supports estimation of maternal blood pressure and identification of hypertension, including conditions associated with preeclampsia. The approach provides a non-invasive, cuff free alternative to traditional blood pressure measurement and is designed for use in a wide range of clinical and resource limited care settings.