# Leveraging Neural Imaging for Automated Neonatal Infection Diagnosis

> **NIH NIH F30** · PENNSYLVANIA STATE UNIV HERSHEY MED CTR · 2022 · $44,064

## Abstract

PROJECT SUMMARY/ABSTRACT
Post-infectious hydrocephalus (PIH) is a leading cause of neonate mortality in the developing world, but there
are limited resources in place for appropriately diagnosing and monitoring the infections that lead to
hydrocephalus. There is often a lack of personnel and laboratory resources available for the gathering and
processing of lumbar puncture and blood cultures, which are the gold-standard for diagnosing the infectious
agents at play in sepsis and PIH. In order to overcome this obstacle, CSF and blood samples were taken from
a cohort of septic neonates in Mbale, Uganda, as well as a cohort of neonates and infants who had already
progressed to PIH. Cranial ultrasounds (CrUS) were taken from the cohort of septic neonates, and head CT
scans were gathered from the PIH cohort. This proposal hypothesizes that the pathogens determined from RNA
and DNA sequencing of the blood and CSF samples can be used to train supervised machine learning algorithms
to recognize imaging phenotypes characteristic of the underlying pathogen. Therefore, PIH can be prevented
by providing pathogen-specific diagnosis and targeted treatment recommendations at the bedside for septic
neonates using CrUS. Furthermore, surgical treatment success for PIH can be optimized using CT for the
purpose of identifying the underlying pathogen and providing management plan recommendations.
This project provides an ideal training environment for a fellow interested in pediatric neurosurgery with a
research emphasis on engineering and machine learning applied to image analysis. The interdisciplinary and
global nature of the project encourages development of a collaborative and innovative research approach. The
home institution of Penn State provides multiple clinical opportunities for growth in pediatric neurosurgery, the
MD/PhD program is supportive of truly translational research efforts, and the sponsor and co-sponsor are more
than adequately prepared to provide all aspects of training mentorship necessary to accomplish the aims of this
project and develop a well-rounded physician-scientist.

## Key facts

- **NIH application ID:** 10458011
- **Project number:** 5F30HD102120-03
- **Recipient organization:** PENNSYLVANIA STATE UNIV HERSHEY MED CTR
- **Principal Investigator:** Mallory Rose Peterson
- **Activity code:** F30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $44,064
- **Award type:** 5
- **Project period:** 2020-09-01 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10458011

## Citation

> US National Institutes of Health, RePORTER application 10458011, Leveraging Neural Imaging for Automated Neonatal Infection Diagnosis (5F30HD102120-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10458011. Licensed CC0.

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