Review of Machine Learning Applications Using Retinal Fundus Images

Area of Science:

• Ophthalmology
• Medical Imaging
• Artificial Intelligence

Background:

• Automated medical diagnosis enhances efficiency and reduces misdiagnosis rates.
• Deep learning enables machines to interpret complex medical data, driving automation.
• Ophthalmology benefits from AI for analyzing retinal images to detect diseases.

Purpose of the Study:

• To review state-of-the-art deep learning methods for automated screening and diagnosis in ophthalmology.
• To investigate AI applications for diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma.
• To cover machine learning techniques for retinal vasculature extraction from fundus images.

Main Methods:

• Review of recent literature on deep learning applied to color fundus images.
• Analysis of AI frameworks for identifying and assessing severity of retinal diseases.
• Inclusion of machine learning approaches for retinal vasculature segmentation.

Main Results:

• Deep learning models show significant promise in automating the detection of DR, AMD, and glaucoma.
• AI facilitates accurate analysis of retinal images for disease identification and severity assessment.
• Machine learning techniques are effective for extracting crucial vascular information from fundus images.

Conclusions:

• Deep learning offers powerful tools for advancing automated screening and diagnosis in ophthalmology.
• AI-driven analysis of retinal images can lead to earlier and more accurate disease detection.
• Further research is needed to address challenges in developing robust AI systems for clinical use.