Objective:

To evaluate the accuracy of a deep learning model in estimating biological aging through retinal imaging and its implications for systemic disease screening.

Key Findings:

• The model demonstrated improved accuracy over previous systems.
• Patients with diabetes, cardiac disease, or a history of stroke had significantly higher retinal age gaps.
• The model's predictions focused on the optic disc, macula, and major vascular arcades, indicating their relevance to systemic vascular health.

Interpretation:

Limitations:

• The study population was predominantly Asian, affecting generalizability.
• Performance declined in more diverse datasets.
• Image quality and acquisition variability influenced accuracy.

Conclusion: