# Neural Network Approach to Estimate Fetal Weight in the Late Third Trimester of Pregnancy

> **NIH NIH K01** · UNIVERSITY OF ROCHESTER · 2024 · $158,396

## Abstract

Project Summary and Abstract
Fetal weight estimation, or the assessment of antenatal fetal weight for the purposes of growth tracking and
labor planning, is a critical component of safe prenatal care. Estimations currently rely on ultrasound-derived
measurements of specific fetal planes to indirectly assess growth and wellbeing. The standard fetal biometric
measurements for the estimation of fetal weight (biparietal diameter, head circumference, abdominal
circumference and femur length) are poorly correlated to actual fetal weight, defined as the measurement of
newborn weight in grams at birth. For newborns who are above 4,000 grams at birth, current error estimates of
fetal weight in the late-third trimester of pregnancy are only accurate approximately 40% of the time. By no
longer relying on fetal biometric measurements, data science approaches have the potential to estimate fetal
weight with lower bias and errors compared to standard regression methods. To date, no studies have used
ultrasound images, not just the fetal measurements, as input into a neural network approach to estimate fetal
weight. The overarching goal of this proposal is to develop the skills and training necessary to lead the
advancement of data science for use in clinical assessment during pregnancy. Using existing ultrasound
imaging and birth certificate data (n=17,478 patients) from the University of Rochester (UR) Medicine Hospitals
and the Finger Lakes Regional Perinatal/Obstetrics Data System (PDS), and n= 310 patients in the R01 study,
Understanding Pregnancy Signals and Infant Development (UPSIDE: R01HD083369), the specific aims are: 1)
To determine the maternal (i.e., body mass index) and fetal factors (i.e., growth measurements) that increase
the discordance between the estimation of fetal weight by the Hadlock formula and actual birth weight of
neonates using birth certificate data from the PDS, 2) To evaluate the accuracy of a CNN algorithm on
ultrasound images in the third trimester to estimate fetal weight compared to the Hadlock formula, and 3) To
test the effectiveness CNN algorithm on new ultrasound images from the UPSIDE study. This proposal will
leverage the expertise of Dr. Caitlin Dreisbach’s mentorship team, computational resources, and the
exceptional research environment at the UR School of Nursing, Goergen Institute for Data Science, and the
Rochester Institute of Technology. Results from this study have the potential to change practice and improve
clinical assessments during the late third trimester of pregnancy. The research study and mentored training
included in this award allows Dr. Dreisbach to establish her long-term career goal of becoming an independent
investigator with expertise in the translation of data science to obstetric clinical care.

## Key facts

- **NIH application ID:** 10886147
- **Project number:** 5K01NR020504-03
- **Recipient organization:** UNIVERSITY OF ROCHESTER
- **Principal Investigator:** Caitlin Dreisbach
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $158,396
- **Award type:** 5
- **Project period:** 2022-08-17 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10886147

## Citation

> US National Institutes of Health, RePORTER application 10886147, Neural Network Approach to Estimate Fetal Weight in the Late Third Trimester of Pregnancy (5K01NR020504-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10886147. Licensed CC0.

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