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Problem:
Glaucoma is one of the leading causes of irreversible blindness worldwide. It affects over 76 million people globally (and this number is projected to reach 111 million by 2040). The scary part is that most people don’t even know they have it until it’s too late, because the early stages have almost no symptoms.
Currently, testing for glaucoma requires expensive machines and specialists. In many regions, patients either can’t afford regular checkups or don’t have easy access to trained ophthalmologists. This delay in detection often means permanent damage by the time treatment starts.
Solution:
I propose building an AI-powered system that can analyze eye images (like fundus scans or OCT scans) to screen for glaucoma automatically. Here’s how it would work:
Collect and train the AI on thousands of labeled eye images from patients with and without glaucoma.
Use deep learning models to detect early abnormalities in the optic nerve that even humans might miss.
Deploy this as a low-cost mobile/desktop tool so it can be used in small clinics, rural health centers, or even on smartphones with an attachment.
Impact:
Makes glaucoma detection affordable and accessible.
Helps doctors by acting as a “second opinion” and speeds up diagnosis.
Early detection - timely treatment - fewer people losing vision unnecessarily
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