EyeNet puts AI-assisted OCT screening within reach of resource-constrained healthcare settings, from rural clinics in Africa to overstretched regional hospitals, helping catch sight-threatening conditions early enough to prevent avoidable blindness.
Specialist ophthalmologists and OCT equipment are concentrated in major cities. Rural clinics across Africa and other resource-constrained regions often have neither, leaving treatable conditions undiagnosed until vision loss is advanced or irreversible.
Conditions like CNV and DME are far more treatable when caught early. A model that can flag a likely diagnosis in seconds gives clinics without an on-site specialist a way to triage and refer patients before it's too late.
EyeNet is designed to run wherever a scan and an internet connection exist. No specialist hardware, no local GPU, no dedicated IT staff required.
EyeNet classifies an uploaded OCT scan into one of four categories, trained on the Kermany et al. retinal OCT dataset.
CNV
Choroidal neovascularization: abnormal blood vessel growth under the retina.
DME
Diabetic macular edema: fluid buildup caused by diabetic retinopathy.
DRUSEN
Early age-related macular degeneration, marked by deposits under the retina.
NORMAL
No signs of the above conditions detected.
Training data and sample scans: Kermany, D.S. et al. (2018), "Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification," via Kaggle, licensed CC BY-NC-SA 4.0.
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