TopoROP: Multi-View Analysis to Diagnose Retinopathy of Prematurity
Official title: TopoROP: Multi-View Topology-Integrated Analytics for Diagnosis of Retinopathy of Prematurity
Funded at Queens College · NIH-11424612 · Data updated 2026-11-12
A new AI-based tool analyzes retinal images to help doctors diagnose Retinopathy of Prematurity in premature babies and other patients at risk.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Queens College NIH-funded |
| Lab location | 1 site (Flushing, United States) |
| Project start | September 15, 2026 |
| Project end | August 31, 2030 |
| Funding year | 2026 |
| Award (this year) | $604,000 |
| NIH project number | 1R16EY039206-01 |
| Project ID | NIH-11424612 on NIH RePORTER |
Who could benefit from this research
✓Could help
Premature infants at risk for Retinopathy of Prematurity and adults with age-related retinal vascular concerns may benefit from the platform’s diagnostic insights.
✕Less likely to help
If a patient’s condition is unrelated to retinal vascular disease or if data quality is insufficient, they may not benefit from this tool.
What this research studies
This grant supports developing an AI platform that looks at the retinal blood vessel network in multiple views to improve early and accurate detection of Retinopathy of Prematurity. The approach aims to go beyond single-image checks by capturing network-level features that reflect disease progression. Researchers will build and test models on retinal data from affected patients and compare performance to current diagnostic methods. If successful, the tool could assist clinicians in making timely treatment decisions to prevent vision loss in vulnerable populations.
Why it matters
Potential benefit: Clinicians could diagnose ROP more accurately and earlier, potentially reducing childhood blindness and its social and economic impact.
How similar studies have gone: Prior AI approaches often face limitations with local vessel features; this project emphasizes network-level biomarkers, representing an innovative but increasingly explored avenue with promising early signals.
Where this research is happening
-
Queens College
Flushing, United States Active
Researchers
- Principal investigator: Tsai, Chia-Ling — Queens College
- Study coordinator: Tsai, Chia-Ling
Related research
Other NIH-funded projects on the same topics.
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Deep learning tool to diagnose retinopathy of prematurity in newborns
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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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AI-enhanced wide-field eye scans for detecting retinopathy of prematurity
Using faster handheld wide-field eye scans plus AI to find early signs of retinopathy of prematurity in premature babies and help guide treatment.
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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.