# SCH: New Advanced Machine Learning Framework for Mining Heterogeneous Ocular Data to Accelerate

> **NIH NIH R01** · UNIV OF MARYLAND, COLLEGE PARK · 2024 · $300,000

## Abstract

Vision loss is among the top 10 causes of disability in the U.S in adults over the age of 18 and one of the most
common disabling conditions in children. The major ocular diseases are caused by the retinal chronic
progressive neurodegeneration and unfortunately are irreversible and incurable, thus the early diagnosis of
ocular diseases is crucial for clinician to provide retinoprotection. Recent advances in ophthalmological
imaging and high throughput genotyping and sequencing techniques provide exciting new opportunities to
ultimately improve our understanding of ocular diseases, their genetic architecture, and their influences on
endophenotype and function. However, existing studies of genetics and retinal images are only conducted
separately, wasting the opportunity to explore the interplay between genetics and retinal images. Therefore,
there is a critical need for new machine learning and scientific advances to reveal genetic basis of retinal
imaging endophenotypes and to synergize genetics and imaging for understanding disease progression. We
propose to conduct the novel retinal imaging genetics research to integratively study both retinal images and
genetic data for automated ocular disease diagnosis and prognosis, genetic association study of
endophenotype, and disease progression prediction. Our group has performed pioneering research on retinal
genetics, prediction, and image analysis, therefore we are in a unique position to achieve these goals.
Specifically, we will investigate the following aims: 1) build efficient data integration models to integrate
retinal imaging genetics data from multiple sources; 2) develop knowledge guided learning models for
identifying nonlinear associations among high-dimensional retinal imaging genetics data; 3) detect the
longitudinal interrelations in retinal data utilizing temporal deep learning model; 4) new robust fair metric
learning model to unify the disease prediction and fair metric selection; 5) apply and validate the proposed
machine learning methods to large-scale retinal imaging genetics data from multiple independent cohorts. The
successful completion of this proposal will produce cutting-edge machine learning tools to facilitate
automated disease diagnosis and accurate long-term prediction of disease development and progression
trajectory, which will enhance the early prevention and current clinical management of the disease and will
provide insights for novel precision treatment development.

## Key facts

- **NIH application ID:** 10818584
- **Project number:** 7R01EB034116-03
- **Recipient organization:** UNIV OF MARYLAND, COLLEGE PARK
- **Principal Investigator:** Wei Chen
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $300,000
- **Award type:** 7
- **Project period:** 2022-07-15 → 2027-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10818584

## Citation

> US National Institutes of Health, RePORTER application 10818584, SCH: New Advanced Machine Learning Framework for Mining Heterogeneous Ocular Data to Accelerate (7R01EB034116-03). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10818584. Licensed CC0.

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