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ARTEFICIAL INTELLIGENCE IN DIABETIC RETINOPATHY SCREENING. A REVIEW
Summary
Artificial intelligence (AI) enhances diabetic retinopathy (DR) screening by analyzing retinal images. AI systems demonstrate high sensitivity and specificity, improving diagnostic efficiency and maintaining eye care quality.
Area of Science:
- Ophthalmology
- Medical Imaging
- Artificial Intelligence
Background:
- Diabetic retinopathy (DR) is a leading cause of vision loss.
- Early detection and screening are crucial for managing DR.
- Artificial intelligence (AI) offers potential for improving DR screening.
Purpose of the Study:
- To provide an overview of AI applications in retinal image evaluation.
- To focus on AI-driven screening for diabetic retinopathy (DR).
- To describe AI principles and algorithms used in clinical practice.
Main Methods:
- Literature review of AI approaches in retinal image analysis.
- Description of basic AI principles and algorithms.
- Analysis of current state-of-the-art AI systems for DR screening.
Main Results:
- Deep neural networks achieve over 80% sensitivity and specificity in DR screening.
- Performance metrics vary based on gold standard definitions and study parameters.
- AI systems show promise for efficient and streamlined DR diagnosis.
Conclusions:
- AI-powered retinal image evaluation can accelerate and optimize DR diagnosis.
- AI adoption can maintain high-quality eye care despite increasing diabetes prevalence.
- AI facilitates scalable and consistent DR screening programs.
