# A machine learning based fetal monitoring system to predict and prevent fetal hypoxia.

> **NIH NIH R43** · DELFINA CARE INC. · 2023 · $261,310

## Abstract

Project Summary/Abstract:
Although EFM is widely deployed in the United States for most deliveries, it has failed to reduce rates for
hypoxic injuries such as neonatal encephalopathy, despite an increased rate of cesarean sections. This lack of
improvement has been attributed to inconsistent applications of vague guidelines during manual analysis of
EFM tracings. Existing automated tools available in the market to augment physician capabilities take the form
of low-precision simplistic rule-based alerts, which cause alarm fatigue and also fail to deliver improvements.
This project proposes the creation and validation of a machine learning model for prediction of intrapartum fetal
hypoxia with high sensitivity and specificity to address this need. Using a multi-site dataset of 50,000 tracings
coupled with electronic health records, a combination of clinical knowledge and a variety of machine learning
techniques will be used to create a model with leading performance. To clear the high bar set by FDA for
patient safety with a de novo device, this proposal aims to validate this model by demonstrating high sensitivity
and specificity on a held-out portion of this large multi-site data set, along with a user study to demonstrate
improved performance by clinicians with software assistance. After this project demonstrates the safety and
efficacy of this model for patient care, a future Phase II will beta test a software solution integrating this model
in labor and delivery wards. The research plan outlined in this proposal will give obstetricians a valuable
evidence-based tool to help them interpret EFM tracings.

## Key facts

- **NIH application ID:** 10760437
- **Project number:** 1R43HD113472-01
- **Recipient organization:** DELFINA CARE INC.
- **Principal Investigator:** Bonnie Lesley Zell
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $261,310
- **Award type:** 1
- **Project period:** 2023-09-05 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10760437

## Citation

> US National Institutes of Health, RePORTER application 10760437, A machine learning based fetal monitoring system to predict and prevent fetal hypoxia. (1R43HD113472-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10760437. Licensed CC0.

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