# MRI and Deep Learning for Early Prediction of Neurodevelopmental Deficits in Very Preterm Infants

> **NIH NIH R01** · CINCINNATI CHILDRENS HOSP MED CTR · 2020 · $534,605

## Abstract

Project Summary/Abstract
About 100,000 very preterm infants (VPI; ≤32 weeks gestational age) are born every year in the United States.
Up to 35% develop noteworthy neurodevelopmental deficits, thereby increasing their risk for poor educational,
health, and social outcomes. Unfortunately, neurodevelopmental deficits cannot currently be reliably diagnosed
until 3 to 5 years of age. The imminent challenge lies in early identification of infants that are more likely to
develop later deficits. Advances in magnetic resonance imaging (MRI) and deep learning (DL) provide means
to address this challenge. Application of DL to infant brain MRI data can open up new windows into early
prediction of neurodevelopmental outcomes in at-risk infants and facilitate the move towards precision
medicine. Our objective is to apply DL to MRI acquired at term equivalent age for early prediction of
neurodevelopment deficits (cognitive, language, and motor) at age 2 in VPI. Our group has identified three key
components necessary for accurate prognostic models of later neurodevelopment. DL analysis of 1)
anatomical features derived from structural MRI (sMRI) allowing detection of brain structural abnormalities and
tissue pathologies; 2) brain connectivity features derived from resting-state functional MRI (rs-fMRI) and
diffusion MRI (dMRI) giving insights into atypical brain connectivity patterns; and 3) integration of anatomical
and connectivity features, thus enhancing abnormal neurodevelopment prediction. In this project, we will
dedicate our efforts in accomplishing the following specific aims. In Aim 1 and Aim 2, we will develop deepAna
and deepConn models analyzing anatomical and connectivity features independently to predict adverse
neurodevelopmental outcomes. By decoding each model, we will identify, validate and disseminate a series of
the most discriminative anatomical and connectivity features to the research community. In Aim 3, we will
develop an ensemble deepAnaConn model analyzing both anatomical and connectivity features, together with
clinical risk factors, for early prediction of neurodevelopmental deficits. This model will help clinicians to predict
later outcomes for those at-risk prematurely born infants before initial neonatal intensive care unit discharge.
We will validate the models using both internal and independent external data and will open the ‘black-box’ of
DL to aid interpretation of imaging and clinical findings. The techniques we develop are expected to improve
the modelling fidelity in medical diagnosis/ prognosis in the same way as DL has revolutionized other fields.
The DL models we develop will not only benefit early detection of neurodevelopmental deficits in VPI, but also
likely benefit individuals with other neurodevelopmental and neurological diseases. This study will significantly
impact public health because it will allow clinicians to target clinical and experimental intervention therapies to
the most at-risk infants during peri...

## Key facts

- **NIH application ID:** 10028428
- **Project number:** 1R01EB029944-01
- **Recipient organization:** CINCINNATI CHILDRENS HOSP MED CTR
- **Principal Investigator:** Lili He
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $534,605
- **Award type:** 1
- **Project period:** 2020-09-30 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10028428

## Citation

> US National Institutes of Health, RePORTER application 10028428, MRI and Deep Learning for Early Prediction of Neurodevelopmental Deficits in Very Preterm Infants (1R01EB029944-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10028428. Licensed CC0.

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