AI-enhanced wide-field eye scans for detecting retinopathy of prematurity
Official title: Artificial Intelligence Assisted Panoramic Optical Coherence Tomography Angiography for Retinopathy of Prematurity
Funded at Oregon Health & Science University · NIH-11381770 · Data updated 2026-11-12
Using faster handheld wide-field eye scans plus AI to find early signs of retinopathy of prematurity in premature babies and help guide treatment.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Oregon Health & Science University NIH-funded |
| Lab location | 1 site (Portland, United States) |
| Project start | July 1, 2020 |
| Project end | April 30, 2030 |
| Funding year | 2026 |
| Award (this year) | $657,651 |
| NIH project number | 5R01HD107494-06 |
| Project ID | NIH-11381770 on NIH RePORTER |
Who could benefit from this research
✓Could help
Premature infants who meet standard ROP screening criteria and are receiving care in neonatal intensive care units are the ideal candidates.
✕Less likely to help
Full-term infants without ROP risk or infants with already extensive, irreversible retinal detachment are unlikely to benefit from this work.
What this research studies
If your baby was born prematurely and is at risk for retinopathy of prematurity, this project uses newly developed faster, wide-field handheld OCT and OCTA scanners to take detailed images of the infant's retina. An artificial-intelligence tool helps read those images to reduce examiner differences and detect disease features earlier than the traditional eye exam. The research team has built prototypes and collected thousands of neonatal eye images to improve the scans and the AI. The aim is to make screening more objective so fewer babies are under-treated or over-treated.
Why it matters
Potential benefit: This could enable earlier, more objective detection and treatment of ROP, lowering the risk of preventable retinal detachment and vision loss.
How similar studies have gone: OCT, OCTA, and AI approaches have improved early detection in adult retinal diseases such as diabetic retinopathy, but using wide-field handheld OCTA with AI in neonatal ROP is a newer and less-tested application.
Where this research is happening
-
Oregon Health & Science University
Portland, United States Active
Researchers
- Principal investigator: Jian, Yifan — Oregon Health & Science University
- Study coordinator: Jian, Yifan
Related research
Other NIH-funded projects on the same topics.
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TopoROP: Multi-View Analysis to Diagnose Retinopathy of Prematurity
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Long-term endotypes of prematurity-related lung disease in former preemies
A long-term study looking at why some babies born very early develop lasting breathing problems and how their genes might explain these different paths.
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Deep learning tool to diagnose retinopathy of prematurity in newborns
A new AI-based diagnostic tool uses eye images from premature babies to help clinicians identify treatment-needed retinopathy of prematurity.
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Researchers are studying why premature birth leads to dystonia in cerebral palsy and looking for new ways to help children and adults who have this condition.
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Retinopathy of Prematurity: Using AI to Improve How We Watch for Change in Preterm Babies' Eyes
A Duke study is using artificial intelligence to analyze baby eye images and other retinal data to make monitoring for retinopathy of prematurity easier and less stressful for preterm infants.
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.