Detection of Systemic Diseases From Ocular Images Using Artificial Intelligence: A Systematic Review

Area of Science:

• Ophthalmology
• Artificial Intelligence
• Medical Diagnostics

Background:

• Current healthcare systems lack precise and accessible screening tools.
• The eye's connection to systemic diseases offers a novel screening perspective.
• Ocular imaging combined with AI presents a potential solution.

Purpose of the Study:

• To systematically review artificial intelligence applications in ocular image-based systemic disease detection.
• To identify current trends and future directions for AI in systemic disease screening.
• To evaluate the diagnostic potential of ocular AI for various health conditions.

Main Methods:

• A systematic literature search was performed across PubMed, Google Scholar, and Web of Science.
• The search included terms related to ocular images, systemic diseases, and artificial intelligence.
• No date restrictions were applied to the search.

Main Results:

• Thirty-three papers were included in the review.
• AI-based ocular analysis detected a range of systemic diseases, including cardio-cerebrovascular, central nervous system, renal, and hepatological conditions.
• Approximately one-third of the studies focused on predicting risk factors for these diseases.

Conclusions:

• Ocular image-based AI demonstrates significant potential for screening diverse systemic diseases.
• Early detection of Alzheimer's and chronic kidney disease is achievable with these AI models.
• Further research is required to validate AI models for practical clinical implementation.