# Deep Learning Approaches for Personalized Modeling and Forecasting of Glaucomatous Changes

> **NIH NIH R01** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2021 · $124,658

## Abstract

Project Summary
Glaucoma is a leading cause of vision morbidity and blindness worldwide. Early disease detection and
sensitive monitoring of progression are crucial to allow timely treatment for preservation of vision. The
introduction of ocular imaging technologies significantly improves these capabilities, but in clinical practice
there are still substantial challenges at managing the optimal care for individual cases due to difficulties of
accurately assessing the potential progression and its speed and magnitude. These difficulties are due to a
variety of causes that change over the course of the disease, including large inter-subject variability, inherent
measurement variability, image quality, varying dynamic ranges of measurements, minimal measurable level of
tissues, etc. In this proposal, we propose novel agnostic data-driven deep learning approaches to detect
glaucoma and accurately forecast its progression that are optimized to each individual case. We will use state-
of-the-art automated computerized machine learning methods, namely the deep learning approach, to identify
structural features embedded within OCT images that are associated with glaucoma and its progression
without any a priori assumptions. This will provide novel insight into structural information, and has shown very
encouraging preliminary results. Instead of relying on the conventional knowledge-based approaches (e.g.
quantifying tissues known to be significantly associated with glaucoma such as retinal nerve fiber layer), the
proposed cutting-edge agnostic deep learning approaches determine the features responsible for future
structural and functional changes out of thousands of features autonomously by learning from the provided
large longitudinal dataset. This program will advance the use of structural and functional information obtained
in the clinics with a substantial impact on the clinical management of subjects with glaucoma. Furthermore, the
developed methods have potentials to be applied to various clinical applications beyond glaucoma and
ophthalmology.

## Key facts

- **NIH application ID:** 10089451
- **Project number:** 5R01EY030929-02
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** HIROSHI ISHIKAWA
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $124,658
- **Award type:** 5
- **Project period:** 2020-02-01 → 2021-08-23

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10089451

## Citation

> US National Institutes of Health, RePORTER application 10089451, Deep Learning Approaches for Personalized Modeling and Forecasting of Glaucomatous Changes (5R01EY030929-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10089451. Licensed CC0.

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