Can Artificial Intelligence Make Screening Faster, More Accurate, and More Accessible?

Area of Science:

• Ophthalmology
• Medical Imaging
• Artificial Intelligence

Background:

• Diabetic retinopathy, glaucoma, and age-related macular degeneration are primary causes of global vision loss.
• Early stages of these eye diseases are often asymptomatic, necessitating proactive screening.
• Deep learning (DL) is emerging as a significant technology in ophthalmology.

Purpose of the Study:

• To review key findings on applying DL for classifying eye diseases using standard imaging.
• To provide an overview of DL's role in ophthalmic diagnostics.

Main Methods:

• Literature review of studies applying DL to ophthalmic image classification.
• Analysis of DL model performance across various common eye imaging modalities.

Main Results:

• DL models demonstrate significant potential in classifying major eye diseases from images.
• Effectiveness of DL varies depending on the specific disease and imaging technique.

Conclusions:

• DL-based AI offers a promising approach for improving the efficiency and cost-effectiveness of eye disease screening.
• Future integration of DL into screening programs could enhance early detection and patient management.