Innovation 03 · Ophthalmology

AI-Based Diabetic Retinopathy Analyzer

A computer vision model that analyzes retinal fundus images to assist in early detection of diabetic retinopathy — potentially saving millions from preventable blindness.

Testing PhaseAlgorithm Testing

AIMCS Center for Innovation

Problem Addressed

Diabetic retinopathy remains one of the leading causes of preventable blindness worldwide. Delayed screening, limited access to ophthalmologists, and shortage of trained specialists — especially in developing countries — mean millions of diabetic patients go unscreened until irreversible damage occurs.

Innovation Solution

AIMCS developed an AI-assisted diagnostic model capable of analyzing retinal fundus images to assist in the early detection of diabetic retinopathy. The system uses deep learning to identify characteristic lesions, microaneurysms, and hemorrhages, providing risk stratification support for non-specialist healthcare workers.

Key Features

Deep learning-based retinal image analysis using convolutional neural networks

Early risk identification with graded severity classification

Designed to operate with standard fundus photography equipment

Educational ophthalmology integration for training purposes

Scalable screening concept deployable in primary care settings

Clinical Applications

👁️Preventive screening programs

🎓Ophthalmology medical education

🤖AI-based diagnostic training

🌍Rural & resource-limited screening

Development Progress

Dataset Curation70%

Model Architecture75%

Training & Testing55%

Clinical Validation20%

Impact

👁️Condition

Diabetic Retinopathy

📊Method

Computer Vision / CNN

🎯Goal

Early detection & prevention

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