Unlocking the  Power of the Retina

The eyes are not just the window to the soul—they are a window into your body and health. The retina, the innermost layer of the eye, is part of the central nervous system and the only place where veins, vessels, and nerves can be observed directly and non-invasively.

Growing evidence in medical literature suggests that retinal imaging can reveal early indicators of diabetes, hypertension, and other systemic health conditions. With advances in machine learning, these images now provide actionable health insights—offering personalized pathways to prevention and care.

DIABETIC RETINOPATHY (DR)

The Leading Cause of Preventable Blindness in Working-Age Adults¹

The American Academy of Ophthalmology advises yearly checks for diabetic retinopathy; however, a significant number of individuals—particularly those facing barriers to healthcare access—fail to receive these screenings. Although retinal imaging can readily identify diabetic retinopathy, the condition frequently remains undetected in its initial phases.

By the Numbers

1 in 3

individuals living with diabetes will develop diabetic retinopathy.²

40%

of people living with diabetes do not receive recommended screenings.³

50%

of eyes with proliferative diabetic retinopathy will be blind in 5 years if left untreated.⁴

HEALTH BEYOND THE EYE

AI-Enabled Ocular Imaging

In addition to identifying ocular diseases such as diabetic retinopathy, glaucoma, and macular degeneration, AI-powered retinal scans can also detect a wide range of health conditions affecting various parts of the body.

Cardiovascular Health

Algorithms can assess changes in retinal vessel caliber and branching patterns, which correlate with hypertension and cardiovascular risks.^5,6

Neurological Conditions

AI can detect signs in the retina associated with major neurological conditions, such as nerve fiber layer thinning in Alzheimer's disease or microvascular alterations in Parkinson’s disease and multiple sclerosis.^7,8

Inflammatory Conditions

By identifying characteristic patterns of inflammation, AI can use retinal images to suggest the presence of systemic inflammatory conditions like rheumatoid arthritis.⁹

Systemic Diseases

AI retinal imaging can act as a barometer for systemic diseases like lupus or sickle cell anemia, which often manifest with specific retinal changes.^10,11

VISION OF EQUALITY

Bridging Disparities Through AI-Enhanced Screening

AI's entry into eye screening ushers in a new era of equitable healthcare access. By facilitating mass detection and analysis, it empowers healthcare systems to deliver uniform and improved outcomes, transforming the way we approach disease prevention and wellness.

References:

1. Kropp M, Golubnitschaja O, Mazurakova A, Koklesova L, Sargheini N, Vo TKS, de Clerck E, Polivka J Jr, Potuznik P, Polivka J, Stetkarova I, Kubatka P, Thumann G. Diabetic retinopathy as the leading cause of blindness and early predictor of cascading complications-risks and mitigation. EPMA J. 2023 Feb 13;14(1):21-42. doi: 10.1007/s13167-023-00314-8. PMID: 36866156; PMCID: PMC9971534.
2. International Diabetes Federation: Diabetes Eye Health. https://idf.org/media/uploads/2023/05/attachments-46.pdf
3. American Academy of Ophthalmology: Diabetic Retinopathy PPP 2019 https://www.aao.org/education/preferred-practice-pattern/diabetic-retinopathy-ppp
4. Ferris F. Early photocoagulation in patients with either type I or type II diabetes. Trans Am Ophthalmol Soc. 1996;94:505-37.
5. 2021 Jun;5(6):498-508. doi: 10.1038/s41551-020-00626-4. Epub 2020 Oct 12. https://pubmed.ncbi.nlm.nih.gov/33046867/
6. Tan, Y., & Sun, X. (2023). Ocular images-based artificial intelligence on systemic diseases. BioMedical Engineering OnLine, 22(49). https://biomedical-engineering-online.biomedcentral.com/articles/10.1186/s12938-023-01110-1
7. Sathianvichitr, K., Lamoureux, O., Nakada, S., Tang, Z., Schmetterer, L., Chen, C., Cheung, C. Y., Najjar, R. P., & Milea, D. (2023). Through the eyes into the brain, using artificial intelligence. Annals of the Academy of Medicine, Singapore, 52(2), 88–95. https://annals.edu.sg/through-the-eyes-into-the-brain-using-artificial-intelligence/
8. Lenharo, M. (2023). AI detects eye disease and risk of Parkinson’s from retinal images. Nature. https://www.nature.com/articles/d41586-023-02881-2
9. McVeigh, K. A., & McNamara, D. J. (2024). Improving the detection of diabetic retinopathy through artificial intelligence-driven screening programs: A systematic review. Eye and Vision, 11(1), Article 3. https://eandv.biomedcentral.com/articles/10.1186/s40662-024-00384-3
10. Lin, S., Masood, A., Li, T., Huang, G., & Dai, R. (2023). Deep learning-enabled automatic screening of SLE diseases and LR using OCT images. The Visual Computer, 39(8), 3259–3269. https://link.springer.com/article/10.1007/s00371-023-02945-4
11. Browning, D. J., & Lee, C. (2021). Artificial intelligence for improving sickle cell retinopathy diagnosis. Eye, 35(10), 2675–2684. https://doi.org/10.1038/s41433-021-01556-4

Unlocking the Power of the Retina