# Advancing Public Eye Health through AI-Driven Prediction of Diabetic Retinopathy

> **NIH NIH R43** · BIOXYTECH RETINA, INC. · 2024 · $406,500

## Abstract

ABSTRACT
We propose to develop a reliable, clinically validated diagnostic imaging and monitoring technology to address the significant burden of diabetic retinopathy (DR), one of the leading causes of vision loss worldwide. The proposed technology has the potential to reduce the cost of DR care, support early intervention, and preserve vision for millions of people living with diabetes. DR affects over 230 million individuals globally, and in the United States alone, approximately 38 million people have diabetes, with an estimated 30% developing DR over time.
This progressive complication of both type I and II diabetes results in structural damage to the retinal vasculature. Large-scale epidemiological studies have shown variability in the prevalence and severity of DR across different populations, highlighting a widespread need for improved screening and diagnostic tools in diverse clinical settings.
While DR has no definitive cure, a range of treatments are available that are most effective when applied in the earliest stages of disease. Recent research has shown that changes in retinal oxygen saturation can serve as a reliable biomarker for detecting DR before structural damage occurs. However, no current clinical technology can non-invasively detect these changes with sufficient sensitivity and resolution.
To meet this need, Bioxytech Retina has developed a novel, non-invasive imaging technology that uses spatially modulated light to generate a high-resolution map of retinal oxygenation in a single snapshot. This approach allows for effective imaging of the retina’s multi-layered structure, overcoming the limitations of conventional oximetry techniques. Preliminary clinical studies have shown the technology can detect DR between 4 to 13 months earlier than standard methods.
This Phase I project will: 1) further develop the technical and software components of the system; 2) conduct a clinical evaluation study at two clinical sites; and 3) integrate a machine learning platform to enhance diagnostic accuracy, automate image analysis, and improve disease classification. These innovations aim to enable earlier, more accurate detection of DR and facilitate more tailored patient monitoring and management across a broad range of clinical environments.

## Key facts

- **NIH application ID:** 10923617
- **Project number:** 1R43MD019594-01
- **Recipient organization:** BIOXYTECH RETINA, INC.
- **Principal Investigator:** Ali Basiri
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $406,500
- **Award type:** 1
- **Project period:** 2024-08-14 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10923617

## Citation

> US National Institutes of Health, RePORTER application 10923617, Advancing Public Eye Health through AI-Driven Prediction of Diabetic Retinopathy (1R43MD019594-01). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/10923617. Licensed CC0.

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