Using advanced algorithms to predict eye disease in premature infants
Official title: Surrogate Augmented Deep Predictive Learning for Retinopathy of Prematurity
Funded at University of Pennsylvania · NIH-10740289 · Data updated 2026-11-12
This study is working on new ways to use computer technology to help doctors spot a serious eye problem called retinopathy of prematurity (ROP) in premature babies, so they can get the right treatment early and avoid vision loss.
Quick facts
| Grant type | R21 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Pennsylvania NIH-funded |
| Lab location | 1 site (Philadelphia, United States) |
| Project start | September 30, 2023 |
| Project end | September 29, 2025 |
| Funding year | 2023 |
| Award (this year) | $482,124 |
| NIH project number | 1R21EY034179-01A1 |
| Project ID | NIH-10740289 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are premature infants, particularly those born before 34 weeks of gestational age.
✕Less likely to help
Patients who are not premature or who do not exhibit risk factors for ROP may not benefit from this research.
What this research studies
This research focuses on developing innovative algorithms to predict retinopathy of prematurity (ROP), a serious eye condition affecting premature infants. By analyzing a large dataset of retinal images from infants, the study aims to enhance early detection and treatment of ROP, which is crucial for preventing childhood blindness. The approach involves using deep learning techniques to identify risk factors such as birth weight and gestational age, allowing for timely interventions. The ultimate goal is to improve clinical practices in the care of premature infants at risk for ROP.
Why it matters
Potential benefit: If successful, this research could significantly reduce the incidence of childhood blindness in premature infants by enabling earlier and more accurate detection of ROP.
How similar studies have gone: Previous research has shown promise in using advanced predictive algorithms for similar conditions, indicating a potential for success in this novel approach.
Where this research is happening
-
University of Pennsylvania
Philadelphia, United States Active
Researchers
- Principal investigator: Ying, Gui-Shuang — University of Pennsylvania
- Study coordinator: Ying, Gui-Shuang
Related research
Other NIH-funded projects on the same topics.
-
Butyrate-based approach to prevent vision loss in premature babies with retinopathy
A study is exploring whether increasing butyrate levels can prevent vision-damaging retinopathy in premature infants by addressing gut bacteria and metabolic pathways.
-
TopoROP: Multi-View Analysis to Diagnose Retinopathy of Prematurity
A new AI-based tool analyzes retinal images to help doctors diagnose Retinopathy of Prematurity in premature babies and other patients at risk.
-
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.
-
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.
-
Cortical dystonia in kids born early
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.
-
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.
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.