Using AI to improve diagnosis of eye diseases through various data sources
Natural language processing and medical imaging analysis for multi-modality computer assisted diagnosis of ophthalmic diseases
This study is working on using smart computer technology to help doctors better diagnose eye diseases by looking at different types of information, like medical images and patient histories, so that everyone can get the right care faster and prevent vision loss.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Yale University NIH-funded |
| Lab location | 1 site (New Haven, United States) |
| Project ID | NIH-11169455 on NIH RePORTER |
What this research studies
This research aims to enhance the diagnosis of ophthalmic diseases by developing advanced artificial intelligence models that integrate multiple types of data, including medical images, clinical notes, and patient history. By utilizing natural language processing, the study seeks to extract valuable information from electronic health records to improve diagnostic accuracy and timeliness. The goal is to create a more comprehensive approach to diagnosing eye conditions, which is crucial for preventing vision loss. The research will also address the generalization of AI models across diverse patient demographics.
Who could benefit from this research
Good fit: Ideal candidates for this research include individuals experiencing symptoms related to ophthalmic diseases or those at risk for such conditions.
Not a fit: Patients with non-ophthalmic conditions or those who do not have access to the required imaging and data resources may not benefit from this research.
Why it matters
Potential benefit: If successful, this research could lead to earlier and more accurate diagnoses of eye diseases, ultimately improving patient outcomes and preserving vision.
How similar studies have performed: Other research has shown promise in using AI for medical diagnosis, particularly in ophthalmology, indicating that this approach has the potential for success.
Where this research is happening
New Haven, United States
- Yale University — New Haven, United States (Active)
Researchers
- Principal investigator: Chen, Qingyu — Yale University
- Study coordinator: Chen, Qingyu
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.