Diabetic Retinopathy: A Growing Concern - Can AI Help?
Ritesh Kanjee
Making Business Easier with AI. High Accuracy GenAI RAG & Computer Vision Expert, Consultant Developer - Over 17 years experience in AI, ML and CV.
Diabetic Retinopathy is on the Rise
Diabetes is a ticking time bomb, and one of its most devastating consequences is diabetic retinopathy; a leading cause of blindness worldwide.
Diabetic retinopathy is a growing concern, especially in countries like India, where 70 million people live with diabetes, and 18% of them already have the ailment. The disease creates lesions in the back of the retina, leading to total blindness if left untreated.
With 415 million diabetics at risk of blindness worldwide, the disease is a global concern.
The Current State of Diabetic Retinopathy Screening
Current guidelines recommend yearly eye exams for individuals with diabetes to detect referable diabetic retinopathy. However, the immense screening burden has led to the development of artificial intelligence (AI) algorithms to autonomously screen for diabetic retinopathy from fundus photography without human input.
Over the last 10 years, many AI algorithms have achieved good sensitivity and specificity (>85%) for detection of referable diabetic retinopathy compared to human graders.
However, many questions still remain, and head-to-head validation studies are limited.
The Need for Accurate Diagnosis
Accurate diagnosis is crucial in preventing blindness. A team at Google has been working on using AI to help doctors prevent blindness in diabetics and have developed an AI algorithm that can identify diabetic retinopathy from retina scans with high accuracy.
A Solution: AI-Powered Diabetic Retinopathy Classification
Our solution involves classifying the severity of diabetic retinopathy in retina images across five categories, and we utilize YOLOv8 (YOLOv10 coming soon) for detection, with an integrated LLM assistant.
The interface is designed using Gradio-chat for retina image uploads and assistant dialogue. The deliverables include project code, a detailed tutorial, and a presentation.
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Physical Medicine & Rehabilitation Doctor, Brown University MPH Candidate (2025), Founder@The Digital Equity Initiative. Committed to an ethical, equitable, and accessible digital health revolution.
9 个月Dr Lili Peng at Googles deep mind successfully deployed retinal scan processing in a rural Indian hospital, 2016, and by 2018 it has become part of the screening process. Amazingly ML retinal image processing has been explored already in the early 70's. AI is a huge benefit to diagnosis and even prediction of diabetic retinopathy.
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9 个月It would be great to be able to use AI this way and help people keep their vision