А cloud-based solution to detect diabetic retinopathy using photographic images of eye fundus through the ML\AI.
THE PROBLEM
Diabetic retinopathy (DR) is a serious complication of diabetes that occurs when high blood sugar levels damage the retina, the part of the eye responsible for vision. If DR is not diagnosed and treated in time, it can lead to blindness. Although it can take several years for DR to progress to a stage where it severely threatens your vision, the damage it causes is irreversible. Experts estimate that each year, approximately 40,000 people worldwide with diabetes develop symptoms of diabetic retinopathy.
HOW IT WORKS
Step 1 - Classification
Data Collection: Gather and label retinal images by disease stage.
Model Development: Build a Convolutional Neural Network (CNN) to classify the images.
Training: Optimize the network to accurately predict retinopathy stages.
Testing: Validate the model with new images.
Application: Use the trained model to diagnose retinopathy stage (No DR, Mild, Moderate, Severe, Proliferate) and support clinical decisions.
HOW IT WORKS
Step 2 - Segmentation
Purpose: Identify and isolate specific regions of the retina, such as blood vessels, microaneurysms, and exudates.
Model Development: Use Convolutional Neural Networks (CNNs) or specialized architectures to segment and label these regions.
Training: Train the network with annotated retinal images, teaching it to distinguish between healthy and diseased tissue.
Testing & Validation: Evaluate the model on new images to ensure accurate segmentation.
Application: Use the segmented regions to assist in more precise diagnosis and treatment planning for diabetic retinopathy.
SUMMARY
AI significantly aids in preventing diabetic retinopathy by enabling early, accurate detection of retinal changes, allowing for timely intervention. Its automated screening capabilities improve access to quality care, especially in underserved areas, ultimately helping to preserve vision and enhance patients' quality of life.
