# Individualization of Fetal Growth Assessment using Maternal Genetics and Explainable AI > **NIH NIH K08** · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · 2024 · $171,720 ## Abstract ABSTRACT This is a K08 award application for Dr. Nathan Blue, a Maternal-Fetal Medicine physician and young inves- tigator pursuing translational and clinical research to improve risk stratification approaches to fetal growth re- striction (FGR). A K08 award will provide him with the means to acquire critical skills in three key career devel- opment areas: 1) programming skills to carry out analyses and visualizations (Unix, R, Python), 2) novel bio- medical informatics approaches to quantify risk of adverse outcomes, and 3) interdisciplinary research leader- ship and management. By acquiring these skills, Dr. Blue will fulfill his career goal of becoming an independent investigator who can improve prenatal recognition of fetuses at risk of adverse outcomes. To pursue this goal, Dr. Blue has assembled the mentoring team of Dr. Robert Silver (primary mentor), a Maternal-Fetal Medicine physician and international authority on obstetric complications, Dr. Mark Yandell (co-mentor), a human genet- ics scientist, expert in computational biology, and developer of medical risk assessment software tools, and Dr. Martin Tristani-Firouzi (co-mentor), a Pediatric Cardiology physician and widely recognized leader in applica- tion of new informatics tools to complex clinical problems such as congenital heart disease. Fetal growth restriction (FGR) is a leading cause of preventable stillbirths, postnatal complications, and re- sults in a lifelong increased risk of cardiovascular disease. Based on his own published data, Dr. Blue’s central hypothesis is that current fetal assessment tools function poorly because they assume all fetuses should be the same size and fetal growth ultrasounds are interpreted in isolation of other factors that could be useful to inform risk. He will test this hypothesis by analyzing maternal genetic variants and using a novel explainable artificial intelligence (AI) method to develop individualized prediction models for expected fetal growth and risk of perinatal morbidity. This will uncover insights into normal fetal growth as well as produce a new neonatal morbidity risk calculator. By pursuing the following aims, Dr. Blue will test his hypothesis and lay the ground- work for refining his new tools prior to application to fetal growth in a prospective cohort (to be proposed in an R01 application during the K08 award period). Specific Aim 1 will test the hypothesis that maternal genetic information can be used to individualize birth weight prediction in uncomplicated pregnancies. Specific Aim 2 will test the hypothesis that genetics, specific clinical variables, and social determinants of health interact syn- ergistically to increase the risk of poor outcomes in FGR, which can be captured by new explainable AI. The proposed research is significant because despite FGR’s enormous global burden, current approaches to fetal growth assessment continue to perform poorly, forcing clinicians and families to make plans without appropriatel... ## Key facts - **NIH application ID:** 10784285 - **Project number:** 1K08HD113835-01 - **Recipient organization:** UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH - **Principal Investigator:** Nathan Richard Blue - **Activity code:** K08 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $171,720 - **Award type:** 1 - **Project period:** 2024-07-01 → 2027-10-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10784285 ## Citation > US National Institutes of Health, RePORTER application 10784285, Individualization of Fetal Growth Assessment using Maternal Genetics and Explainable AI (1K08HD113835-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10784285. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*