# Deep Learning-based retinal optical coherence tomography markers for optic neuropathies

> **NIH NIH F30** · UNIVERSITY OF CALIFORNIA-IRVINE · 2024 · $44,850

## Abstract

PROJECT SUMMARY/ABSTRACT
Optic neuropathies, a wide-ranging group of eye diseases, primarily target the optic nerve and frequently result
in irreversible vision loss. Globally affecting millions, these conditions stem from a variety of causes such as
glaucoma, ischemic, inflammatory, compressive, toxic, and hereditary factors, each with distinct
pathophysiological traits. Characterized by the degeneration of retinal ganglion cells, these diseases lead to
changes in the optic nerve head and visual field defects. The diverse onset and myriad underlying causes of
these neuropathies complicate early diagnosis, underscoring the necessity for advanced diagnostic tools.
This project proposes leveraging deep learning-based morphometric analysis in optical coherence tomography
(OCT) to develop innovative biomarkers for optic neuropathies. OCT, a non-invasive imaging technique, has
revolutionized the diagnosis and management of these conditions, providing detailed retinal and optic nerve
head imagery. However, its efficacy is limited by device-dependent variability, signal quality dependency, and
insufficient sensitivity of current thickness markers in chronic disease monitoring.
This project proposes to overcome these limitations by employing advanced computational techniques such as
deep learning, neural fields, and geometric modeling. These methods excel in extracting complex patterns from
medical images and enhancing the accuracy of morphometric analyses. Geometric deep learning adapts to the
neuroretina's geometric structures, offering novel insights into the optic nerve head and macula. Neural field
image registration enhances OCT image co-registration accuracy, crucial for longitudinal disease monitoring.
The project aims to revolutionize the diagnosis and management of optic neuropathies through three
interconnected objectives. The first aim is to employ deep generative models for mapping structure-function
relationships in optic neuropathies, focusing on predicting visual field outcomes and tracking disease progression
using advanced deep learning techniques. The second aim is to pioneer the next generation of retinal
morphometric OCT biomarkers using deep learning, enhancing the precision in identifying retinal changes and
improving the longitudinal analysis of optic nerve head and macular structures. Finally, the third aim is dedicated
to leveraging deep learning models for the classification of various types of optic neuropathies directly from
retinal OCT scans, aiming to significantly increase the accuracy and efficiency in distinguishing these conditions.
This research proposes a multidimensional strategy to significantly improve the diagnostic and management
capabilities in the field of neuro-ophthalmology through innovative applications of deep learning to OCT imaging.
By integrating advanced imaging techniques with deep learning models, it aims to unveil novel biomarkers and
predictive models, offering insights into the progression and tre...

## Key facts

- **NIH application ID:** 10998041
- **Project number:** 1F30EY036725-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA-IRVINE
- **Principal Investigator:** Pooya Khosravi
- **Activity code:** F30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $44,850
- **Award type:** 1
- **Project period:** 2024-07-11 → 2029-07-10

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10998041

## Citation

> US National Institutes of Health, RePORTER application 10998041, Deep Learning-based retinal optical coherence tomography markers for optic neuropathies (1F30EY036725-01). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10998041. Licensed CC0.

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