| Title: | Glaucoma detection based on artificial intelligence |
| Lead Institution: | University of Tennessee Health Sciences Center |
| Principal investigator: |
Application 89676
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About
Glaucoma is the second leading causes of blindness worldwide. Glaucoma will become even more prevalent in the coming decades due to the aging population. The burden of care will therefore continue to grow, without a competing increase in the number of ophthalmologists or available resources. Therefore, the rationale is that technology may play a critical role in augmenting clinics for improved detection of people who will lose vision due to glaucoma. We propose to develop such technologies (based on artificial intelligence) to detect glaucoma using non-invasive retinal imaging data. Our models will aid clinicians to make decision regarding glaucoma. Our proposed studies could enhance detecting glaucoma thus may lead to cost-effective programs for screening the population for glaucoma as well. We will develop these models overt six years and hope to uncover novel markers of glaucoma based on digital data. Such markers may improve identifying individuals at-risk of future vision loss thus impacting public care significantly.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 15580 | Prediction of heart failure risk factors from retinal optical imaging via explainable machine learning | Sona M. Al Younis (+5) | 2025 | Frontiers in Medicine |
Enabling scientific discoveries that improve human health