AI Proves Your Eyes Are Key to Uncover Your Illnesses

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William Shakespeare is famously quoted as saying “The eyes are the window to your soul”.

And AI is proving it’s actually true.

Source: MidJourney

Published in the world-famous Nature magazine, a new study has proved that an AI, named RETFound, can accurately predict:

Diagnosis/Prognosis of sight-threatening diseases

And more spectacularly, 3-year incidence prediction of systemic diseases (diseases that affect the whole body) like Parkinson’s disease

All this by simply looking at your eyes.

These types of discoveries, and not ChatGPT, are the ones that are really going to change the world for the better.

The new great trend, oculomics

Oculomics is an exciting new field that provides a non-invasive way to predict several life-threatening diseases before they actually occur using your eyes.

As someone who’s had Alzheimer’s running in the family, I can’t help but feel extremely emotional about the fact that AI is already increasing our prospects for early diagnosis, or even prediction.

But how does RETFound actually work?

MAEs, the gift that keeps on giving

In short, RETFound is the encoder part of a masked autoencoder (MAE).

But what does that mean?

Put simply, MAE is an AI model that takes in a patched image and learns to reconstruct it to its original form.

By comparing to the ground truth using a differentiable loss (meaning it can be optimized through gradients as any other Neural Network), the model gets better at that reconstruction.

Source: Original MAE paper

And why do such exercise?

Well, MAEs are great for two reasons:

Building models with unlabelled data with automatic supervisory signals, called self-supervised learning

Achieving high semantic representations

As for the former, one of the reasons recent models have shown such an impressive performance is because they were fed humongous amounts of data; trillions of words in the case of LLMs (and millions of images in today’s case).

With MAEs, instead of humans pointing to the correct answer for every image, the ground truth is the original image itself.

That way, you can train your model with millions or billions of images with no human effort required.

Additionally, this ‘patching exercise’ forces the model to truly understand what it’s reconstructing.

For instance, if we show the model a patched image where only a German shepherd’s face can be seen, the model needs to ‘understand’ that it’s a German shepherd and, thus, that it should have four legs, a body, and a tail.

In other words, we force the model to learn a complex understanding of our world.

And how was RETFound built?

A long-distance cousin of ChatGPT

RETFound was trained in two steps, as per the image below:

A pre-training phase to reconstruct images from a huge dataset of natural images

A fine-tuning phase using eye with OCT and CFP images, the most common imaging modalities in ophthalmology

Source: RETFound paper

While…

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