# Developing Population-Generalizable Deep Learning Models for Automated Glaucoma Screening

> **NIH NIH R01** · SCHEPENS EYE RESEARCH INSTITUTE · 2024 · $754,779

## Abstract

Project Summary 
Motivation: Glaucoma is a leading cause of irreversible blindness affecting 3 million patients in the US. About 
50% of people with glaucoma are unaware of their condition, particularly due to a lack of access to routine 
ophthalmic care. Glaucoma detection with deep learning using retinal imaging is promising to provide cost-effective mass glaucoma screening, which can be deployed in primary care and pharmacies without needing 
individuals to visit more expensive eye clinics for screening. It is unclear if deep learning models for glaucoma 
detection are population-generalizable, and few studies have been conducted to develop novel methods to 
improve model population generalizability. We hypothesize that performance consistency across populations in 
glaucoma detection can be improved by developing novel population-generalizable deep learning techniques. 
We propose new generalizability-scaled performance metrics of the area under the receiver operating 
characteristic curve, sensitivity, and specificity for population generalizability assessment, which penalize raw 
performance metrics with performance variance across populations. 
Overall Approach: Our models use optical coherence tomography scans and fundus photos to predict 
glaucoma diagnosis defined by clinical guidelines. Our population-generalizable models will be compared with 
standard models with and without oversampling and transfer learning measured by generalizability-scaled 
performance metrics. Aims: (1) Assessing performance generalizability of existing deep learning models 
and developing a population scaling approach. We will quantify the performance population generalizability 
for the three widely used deep learning models including ResNet, EfficientNet, and Vision Transformer. We will 
develop a novel population scaling approach to improve model generalizability by reweighting the training error 
function based on the cross-population variance of training errors. (2) Developing population-conditioned 
generative models for data augmentation to improve performance generalizability. We will develop 
population-conditioned generative models to synthesize training data. The generated data will be used to 
improve data availability across populations for training glaucoma detection models. (3) Developing 
population normalization models to improve glaucoma detection generalizability. We will develop a 
population normalization technique to balance feature importance across populations. We will integrate the 
three population-generalizable approaches to maximize performance generalizability. 
Main Deliverables and Impacts: Our population-generalizable deep learning models for automated glaucoma 
screening will greatly benefit patients by providing more effective glaucoma screening. We will release a large 
de-identified dataset of 10,000 patients to the public to study the population generalizability of medical artificial 
intelligence.

## Key facts

- **NIH application ID:** 10872671
- **Project number:** 1R01EY036222-01
- **Recipient organization:** SCHEPENS EYE RESEARCH INSTITUTE
- **Principal Investigator:** Mengyu Wang
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $754,779
- **Award type:** 1
- **Project period:** 2024-09-30 → 2029-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10872671

## Citation

> US National Institutes of Health, RePORTER application 10872671, Developing Population-Generalizable Deep Learning Models for Automated Glaucoma Screening (1R01EY036222-01). Retrieved via MORVS 2026-10-25 from https://api.morvs.ai/grant/nih/10872671. Licensed CC0.

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