# Self-aligning, motion-stabilized ocular imaging for eye care in urgent and emergent care settings

> **NIH NIH R01** · DUKE UNIVERSITY · 2024 · $411,582

## Abstract

ABSTRACT
This research proposal from a multi-disciplinary team including urgent and emergent care physicians,
ophthalmologists, and biomedical engineers seeks to improve the ability of non-specialty providers to provide
ocular exams at the point of care. Urgent and emergent care settings are frequently the access point of
patients seeking eye care. Standard of care instruments to examine the eye in these non-specialty settings are
notoriously difficult to use, and patients frequently are referred out for later specialty care at added expense in
time and delayed care. To improve the likelihood of diagnostically useful eye examinations in these non-
specialty settings, we will introduce a remote, semi-autonomous eye imaging system capable of retinal optical
coherence tomography (OCT), retinal scanning laser ophthalmoscope (SLO), and anterior segment slit
illuminated imaging that can be used without need for on-site staffing for operation.
These developments have both direct immediate clinical and research applicability by providing the potential to
readily examine patient eyes where the patients already are. In addition, there are future implications as a
platform for remote diagnostic capabilities in other settings where specialty eye care may be limited.

## Key facts

- **NIH application ID:** 10929446
- **Project number:** 5R01EY035534-02
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Anthony Nanlin Kuo
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $411,582
- **Award type:** 5
- **Project period:** 2023-09-30 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10929446

## Citation

> US National Institutes of Health, RePORTER application 10929446, Self-aligning, motion-stabilized ocular imaging for eye care in urgent and emergent care settings (5R01EY035534-02). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/10929446. Licensed CC0.

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