Using deep learning to diagnose retinopathy of prematurity
Official title: Deep Learning-based Diagnosis of Retinopathy of Prematurity
Funded at The Trustees of the Stevens Institute of Technology · NIH-10872726 · Data updated 2026-11-12
This study is working on a new tool that uses advanced technology to help doctors quickly and accurately diagnose retinopathy of prematurity (ROP), a condition that can cause blindness in babies, making it easier for them to get the care they need.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | The Trustees of the Stevens Institute of Technology NIH-funded |
| Lab location | 1 site (Hoboken, United States) |
| Project start | September 1, 2024 |
| Project end | August 31, 2028 |
| Funding year | 2024 |
| Award (this year) | $580,331 |
| NIH project number | 1R01EY036203-01 |
| Project ID | NIH-10872726 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are premature infants who are at risk for developing retinopathy of prematurity.
✕Less likely to help
Patients who are not premature or do not exhibit signs of retinopathy of prematurity may not benefit from this research.
What this research studies
This research focuses on developing a deep learning-based diagnostic tool for retinopathy of prematurity (ROP), a significant cause of childhood blindness. The project aims to address the challenges of limited training data and variations in image quality that hinder accurate diagnosis. By augmenting the training dataset and improving image normalization techniques, the researchers hope to create a reliable diagnostic model that reduces reliance on clinical expertise. This innovative approach could lead to more timely and consistent diagnoses for affected infants.
Why it matters
Potential benefit: If successful, this research could significantly improve the early diagnosis and treatment of retinopathy of prematurity, potentially preventing childhood blindness.
How similar studies have gone: Other research has shown promise in using deep learning for medical diagnoses, indicating that this approach could be effective for retinopathy of prematurity as well.
Where this research is happening
-
The Trustees of the Stevens Institute of Technology
Hoboken, United States Active
Researchers
- Principal investigator: Kang-Mieler, Jennifer J — The Trustees of the Stevens Institute of Technology
- Study coordinator: Kang-Mieler, Jennifer J
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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.