# Artificial intelligence assisted panoramic Optical Coherence Tomography Angiography for Retinopathy of Prematurity

> **NIH NIH R01** · OREGON HEALTH & SCIENCE UNIVERSITY · 2021 · $377,300

## Abstract

PROJECT SUMMARY
The long-term goal of this project is to determine whether optical coherence tomography (OCT) and OCT
angiography (OCTA) might lead more accurate and objective diagnosis, earlier intervention, and improved
outcomes in retinopathy of prematurity (ROP). International consensus and National Institute of Health (NIH)
funded clinical trials over the last 30 years have defined the phenotypic classifications, natural history, prognosis,
and management of ROP. However, it is well established that due to the subjectivity of the ophthalmoscopic
examination, and systematic bias between examiners, there is significant variation in treatment of the most
severe forms of ROP in the real world. This leads to both under-treatment (and poor outcomes due to retinal
detachment) and over-treatment (exposing neonates to the ocular and systemic risks of treatment). Roughly
20,000 babies per year develop retinal detachments (RD) due to ROP and there is strong evidence that most of
these are preventable. In adult retinal vascular diseases, most notably diabetic retinopathy (DR), OCT and OCTA
can detect and quantify disease features such as diabetic macular edema (DME) and retinal neovascularization
(NV) before they are noted clinically, enabling earlier treatment and reducing the risk of blindness from RD.
However, evaluating the use of this technology in neonates requires high speed and portable technology, and
the commercially available handheld OCTs are too slow for ultra-widefield (UWF) OCT and OCTA imaging.
Several groups (including our own) have published preliminary results using prototype 100 to 200 kHz swept-
source (SS) OCT systems, however consistent data acquisition remains challenging due to the lack of fixation
and subsequent motion in an awake neonate, which has limited the evaluation of the potential benefits of the
technology in this population. Recently, there has been much interest in using artificial intelligence (AI)
(specifically deep learning), which relies on high speed graphics processing units (GPUs) to provide real time
OCT image processing, segmentation, and tracking. This application addresses 2 fundamental gaps in
knowledge: (1) Can we overcome the technical challenges through the development of a faster ultrawide-field
view SS-OCT system coupled with a GPU-enabled DL software system to enable consistent data acquisition in
neonates? (2) Would quantitative objective metrics of ROP improve objectivity of ROP diagnosis and detect
subclinical signs of disease progression which may enable earlier intervention and improved outcomes in the
future. By leveraging our institution’s OCT, AI, and ROP expertise, we will address these questions in three
specific aims: (1) Develop an ultra-high speed, handheld, panoramic ultra-widefield OCT/OCTA system. (2)
Develop real time GPU accelerated intelligent image acquisition software. (3) Evaluate the clinical significance
OCT derived biomarkers. Successful translation of this technology to the...

## Key facts

- **NIH application ID:** 10198930
- **Project number:** 8R01HD107494-02
- **Recipient organization:** OREGON HEALTH & SCIENCE UNIVERSITY
- **Principal Investigator:** John Peter Campbell
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $377,300
- **Award type:** 8
- **Project period:** 2020-07-01 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10198930

## Citation

> US National Institutes of Health, RePORTER application 10198930, Artificial intelligence assisted panoramic Optical Coherence Tomography Angiography for Retinopathy of Prematurity (8R01HD107494-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10198930. Licensed CC0.

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