Placental Analytics — Department of Health and Human Services SBIR Phase I: NICHD

Description

DESCRIPTION provided by applicant Placental measures of roundness cord insertion centrality volume shape and thickness are biomarkers of maternal fetal growth Deviations from expected placental morphology even as early as weeks are related to placental dysfunction and pregnancy complications such as preeclampsia pregnancy hypertension gestational diabetes mellitus and fetal chromosomal defects The common occurrence of these deviations reveals the need for mathematical models to quantify deviations in placental morphology and assess risk of adverse pregnancy outcomes early in pregnancy To meet this need we have assembled a leading team of experts in placental morphology mathematics computer science and obstetrics and gynecology to develop and validate mathematical models that predict placental dysfunction and pregnancy complications from placental morphology measurements obtained early in pregnancy The algorithm will be developed from a large database comprised of D ultrasound placental images from pregnancies with known outcomes providing objective criterion in which andquot healthyandquot and potentially andquot at riskandquot patterns of placental growth can be established A prototype for clinical software will be advanced to house the proposed models allow for inputting of individual patient data and generate estimates for increased risk for adverse outcomes thereby indicating patients for further investigation The proposed models will classify individualized pregnancy risks providing a foundation for a personalized plan to manage each pregnancy PUBLIC HEALTH RELEVANCE Abnormal placental growth is related to numerous adverse pregnancy outcomes such as preeclampsia fetal growth restriction and gestational diabetes mellitus The proposed project will apply multi site state of the art three dimensional ultrasound to mathematical models of the placenta delineating normal and `at riskandapos placental growth patterns The models offer a feasible scalable and non invasive method to ascertain pregnancy risks early in gestation