Artificial intelligence-based retinal imaging for brain health assessment: a scoping review

Area of Science:

• Ophthalmology and Neurology
• Artificial Intelligence in Medicine
• Oculomics

Background:

• Brain health requires lifelong optimization.
• The retina is an accessible window to study brain health and related diseases.
• Neurodegenerative and cerebrovascular diseases pose significant global health challenges.

Purpose of the Study:

• To propose an ecosystem for deploying AI-based retinal imaging in brain health assessment.
• To explore the potential of oculomics for early detection of neurodegenerative and cerebrovascular diseases.
• To review the current landscape of AI in retinal imaging for brain health.

Main Methods:

• Scoping review methodology.
• Analysis of advancements in artificial intelligence and retinal imaging.
• Identification of potential applications in oculomics for systemic diseases.

Main Results:

• AI-based retinal imaging shows significant potential for assessing brain health.
• Oculomics can identify ocular imaging-based markers for systemic diseases.
• The review outlines a framework for integrating AI retinal imaging into clinical practice.

Conclusions:

• AI-based retinal imaging is a promising tool for brain health assessment.
• Clinical translation requires standardized datasets, model transparency, and interdisciplinary collaboration.
• Further validation and regulatory standards are essential for real-world implementation.