# Multimodal Artificial Intelligence to Predict Glaucomatous Progression and Surgical Intervention

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2024 · $103,363

## Abstract

Project Summary
The overall objective of this proposal, “Multimodal Artificial Intelligence to Predict Glaucomatous
Progression and Surgical Intervention”, is to use multimodal artificial intelligence (AI) and deep
learning strategies to predict which glaucoma patients will need glaucoma surgery and which
are likely to have progressive visual field loss in the future. This study is designed to leverage
longstanding well characterized clinical and research cohorts of glaucoma patients and its
validated decision support AI infrastructure to predict which glaucoma patients will progress and
which will need surgery. The proposal includes the following two Specific Aims. Aim 1 will use
baseline electronic health records (EHR), optic nerve head (ONH) optical coherence
tomography (OCT) imaging, visual field (VF) data, intraocular pressure (IOP) and central
corneal thickness (CCT) in a multimodal DL model to predict the likelihood of surgical
intervention for glaucoma. Aim 2 will use baseline EHR, ONH OCT imaging, VF data, IOP and
CCT in a multimodal DL model to predict the likelihood of fast glaucomatous visual field
progression. To address these aims, existing data from glaucoma patients 1) enrolled in the
National Eye Institute funded Diagnostic Innovations in Glaucoma Study (DIGS 1995-present)
and African Descent and Glaucoma Evaluation Study (ADAGES 2009-2021), and 2) managed
at the UCSD Viterbi Family Department of Ophthalmology will be used in the AI model
development and testing. We will also leverage UCSD’s existing cloud-based AI pipeline to
build a glaucoma-specific platform to train, test and in the future, update the deep learning
models developed. In the future, this infrastructure can be used to support randomized clinical
trial testing of AI guided glaucoma management and enable real-time decision support for
clinicians.

## Key facts

- **NIH application ID:** 11067213
- **Project number:** 3R01EY034146-03S1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Sally Liu Baxter
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $103,363
- **Award type:** 3
- **Project period:** 2022-09-01 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11067213

## Citation

> US National Institutes of Health, RePORTER application 11067213, Multimodal Artificial Intelligence to Predict Glaucomatous Progression and Surgical Intervention (3R01EY034146-03S1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/11067213. Licensed CC0.

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