# Multimodal Artificial Intelligence to Predict Glaucomatous Progression and Surgical Intervention

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2022 · $410,446

## 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:** 10504041
- **Project number:** 1R01EY034146-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Sally Liu Baxter
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $410,446
- **Award type:** 1
- **Project period:** 2022-09-01 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10504041

## Citation

> US National Institutes of Health, RePORTER application 10504041, Multimodal Artificial Intelligence to Predict Glaucomatous Progression and Surgical Intervention (1R01EY034146-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10504041. Licensed CC0.

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