# A placenta-based strategy for improved clinical prediction of fetal growth trajectory using automated image analysis of placental morphology and vascularity

> **NIH NIH R01** · VANDERBILT UNIVERSITY · 2022 · $604,173

## Abstract

PROJECT SUMMARY/ABSTRACT
Fetal growth restriction is associated with a profound increase in perinatal and even long-term health risk.
Antenatal care is key to optimizing outcomes and preventing stillbirth, yet up to half of growth-restricted infants
are not identified during pregnancy. The placenta serves a central in maintaining a healthy pregnancy and
supporting fetal growth; yet, direct assessment of placental development is glaringly absent from clinical care as
there are no practical tools that enable providers to monitor placental development. In recent years, 3D
ultrasound (3DUS) has allowed investigators to identify important associations between placental morphology
and clinical outcomes using a variety of offline medical image analysis techniques. However, these techniques
typically require extensive manual input. Moreover, we have recently developed an innovative tool based on a
dynamic model of fetal-placental growth that considers placental growth in the evaluation of fetal growth and can
help identify pregnancies at increased risk of growth restriction. However, this tool requires placental volume
assessment, which, as mentioned above, remains impractical for clinical use.
In this proposal, we will expand and enhance our automated segmentation tools to enable bedside volumetric
assessment of the placenta throughout pregnancy. In addition, we will develop novel tools and parameters for
assessing placental shape, gross morphology, and vascularity in an effort to identify additional features of
placental development that can augment our understanding of placental development and create additional
markers of placental health.
Taken together, the current proposal leverages an ongoing collaboration between computer scientists and
physician-scientists to utilize modern fully automated image analysis methodology to create clinically impactful
placental assessment tools that can be integrated into the clinical workflow. The proposed research will allow
bedside assessment of placental morphology and vascularity, which can be leveraged into precision medicine
approaches and allow for more accurate and reliable surveillance of fetal growth and well-being. Specifically, we
will build: 1) Refine and validate a fetal-placental growth model using automated early placental volume and
placental histopathology, 2) Extend to include later gestational ages and expand the toolkit to include novel
measures of placental shape and vascularity, and 3) create an augmented version of the dynamic model that
incorporates the added functionality of our segmentation pipeline, as well as serum biomarkers, to result in a
clinically useful tool for monitoring fetal growth.
We anticipate that this proposal will significantly change clinical care and create a new, placenta-based paradigm
for understanding and managing fetal growth disorders.

## Key facts

- **NIH application ID:** 10512601
- **Project number:** 1R01HD109739-01
- **Recipient organization:** VANDERBILT UNIVERSITY
- **Principal Investigator:** Ipek Oguz
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $604,173
- **Award type:** 1
- **Project period:** 2022-09-05 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10512601

## Citation

> US National Institutes of Health, RePORTER application 10512601, A placenta-based strategy for improved clinical prediction of fetal growth trajectory using automated image analysis of placental morphology and vascularity (1R01HD109739-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10512601. Licensed CC0.

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