Using advanced algorithms to predict eye disease in premature infants
Surrogate Augmented Deep Predictive Learning for Retinopathy of Prematurity
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 ID | NIH-10740289 on NIH RePORTER |
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.
Who could benefit from this research
Good fit: Ideal candidates for this research are premature infants, particularly those born before 34 weeks of gestational age.
Not a fit: Patients who are not premature or who do not exhibit risk factors for ROP may not benefit from this research.
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 performed: 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
Philadelphia, United States
- University of Pennsylvania — Philadelphia, United States (Active)
Researchers
- Principal investigator: Ying, Gui-Shuang — University of Pennsylvania
- Study coordinator: Ying, Gui-Shuang
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 below.