Application of Artificial Intelligence in Targeting Retinal Diseases

Area of Science:

• Ophthalmology
• Medical Imaging
• Artificial Intelligence

Background:

• Global rise in retinal diseases due to aging populations strains specialist capacity.
• Increasing demand for diagnostic imaging in ophthalmology necessitates technological support.
• Artificial intelligence (AI) offers solutions for enhancing clinical tasks and biomarker discovery.

Purpose of the Study:

• To review the application of AI in retinal imaging for early detection and management of retinal diseases.
• To discuss AI's role in facilitating the efficacy and accuracy of optical coherence tomography (OCT) evaluations.
• To explore AI's potential in screening, diagnosis, grading, and individualized therapy for major retinal conditions.

Main Methods:

• Review of recent advances in AI algorithms (machine learning, deep learning) for retinal imaging.
• Focus on AI applications in diabetic retinopathy, age-related macular degeneration, and retinopathy of prematurity.
• Discussion of AI integration, interpretation, and automation in disease evaluation and treatment.

Main Results:

• AI algorithms can augment optical coherence tomography (OCT) for detecting and quantifying retinal abnormalities.
• AI shows promise in early detection, diagnosis, grading, and personalized treatment strategies for key retinal diseases.
• Automated AI systems can assist in evaluating disease activity, recurrences, and treatment timing.

Conclusions:

• AI is crucial for improving the efficiency and accuracy of retinal imaging analysis.
• AI facilitates personalized therapies and optimizes clinical assistance for various retinal diseases.
• AI holds significant potential for advancing ophthalmology through automated diagnostic and therapeutic support.