# Robust AI to develop risk models in retinopathy of prematurity using deep learning

> **NIH NIH R21** · MASSACHUSETTS GENERAL HOSPITAL · 2020 · $274,883

## Abstract

ROP is a retinal neovascular disease affecting preterm infants, and is a leading cause of childhood blindness
worldwide. Known clinical risk factors include preterm birth, low birthweight and use of supplemental oxygen
but improved risk models are needed to identify infants that progress to treatment requiring disease and
blindness. Deep learning techniques have been used to successfully identify “plus” disease in multi-
institutional cohorts and to provide a continuous measure of disease severity. A major limitation of deep
learning, however, is the need for large amounts of well curated datasets. Other limitations include overfitting
and “brittleness” that can cause model performance to drop on external data. There are, however, numerous
barriers to building and hosting these large central repositories with multi-institutional data required for robust
deep learning including concerns about data sharing, regulations costs, patient privacy and intellectual
property. In this project, we aim to demonstrate the utility of distributed/federated deep learning approaches
where the data are located within institutions, but model parameters are shared with a central server.
A major challenge thwarting this research, however, is the requirement for large quantities of labeled image
data to train deep learning models. Efforts to create large public centralized collections of image data are
hindered by barriers to data sharing, costs of image de-identification, patient privacy concerns, and control
over how data are used. Current deep learning models that are being built using data from one or a few
institutions are limited by potential overfitting and poor generalizability. Instead of centralizing or sharing patient
images, we aim to distribute the training of deep learning models across institutions with computations
performed on their local image data.
Specifically, we seek to build robust risk models for predicting treatment requiring disease. Two large cohorts
will be used to validate the hypothesis that the performance of the risk models using distributed learning
approaches that of centrally hosted and is more robust than models built on single institutional datasets.
 Grants Admin
Updated 04.01.2019 JBou

## Key facts

- **NIH application ID:** 10048436
- **Project number:** 1R21EY031883-01
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Jayashree Kalpathy-Cramer
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $274,883
- **Award type:** 1
- **Project period:** 2020-09-30 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10048436

## Citation

> US National Institutes of Health, RePORTER application 10048436, Robust AI to develop risk models in retinopathy of prematurity using deep learning (1R21EY031883-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10048436. Licensed CC0.

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