The retina is the back layer of the eye that senses light and sends signals to the brain for vision. It contains key components like the optic disc and blood vessels. Retinal imaging allows examination of the retina and provides insight into a patient's overall health. Advances in mobile retinal cameras since the 2000s now enable high-quality retinal images to be captured. Recent applications of artificial intelligence to retinal images have shown the ability to predict personal health information like age, gender, cardiovascular risks, and neurological conditions from the retina alone. However, more work is still needed to translate these research findings into clinically useful tools and ensure high-quality annotated retinal data is available.

2. How technology is changing
healthcare industry
… in eyecare

3. The eye is the modern day Tricorder

4. What is the retina?
The retina is the back wall inside the
eye. This layer senses light and sends
signals to the brain so you can see.
To “see” the light travels through your
pupil and the lens, and lands on the
macula, the photosensitive part of
your retina.

5. Components of the retina
The retina, as imaged by a fundus
camera.
It is comprised of several key
components, such as:
- Optic Disc
- Fovea / Macula
- Blood vessels

6. Why the retina?
Central nervous system and vasculature visualized from exterior.
Fundus imaging is a great way to get a sense for the patient’s overall health.

7. Obtaining a retinal image
Classic ophthalmoscope (invented 1847)
Retinal Camera (invented 1926)
Mobile Retinal Camera (early 2000)

8. Application of AI in the retina
In 2018 Google sent shockwaves through the AI and medical communities.

9. AI Retinal predictions
Paper published in Nature
Biomed predicting Age,
Gender, Smoking, and BMI.
Poplin, R., Varadarajan, A.V., Blumer, K. et al.
Prediction of cardiovascular risk factors from
retinal fundus photographs via deep learning. Nat
Biomed Eng 2, 158–164 (2018).
https://doi.org/10.1038/s41551-018-0195-0

10. Prediction of Age and Systolic Blood Pressure

12. What’s next?
Age, Gender, Blood pressure… it’s cool but not clinically useful.
New articles being published every day:
- Systemic: age, gender, smoking, BMI and waist-hip ratio.
- Cardiovascular: Heart Attack, Stroke, MACE
- Neurological: Parkinsons, Stroke
- Nephrology: Chronic Kidney Disease

14. How do we translate this from an academic setting
to use in clinical use?
How do we capture annotated, high quality data?

22. How can we leverage this information?
Democratize access to healthcare for all
Save vision and prevent unnecessary blindness
Build this tricorder for diagnostics
Change our habits, with nudges