Assessing Cardiovascular Risk Factors with Computer Vision

Great anecdote from Jeff Dean's twitter [1] about how they thought to try this experiment, given that doctors didn't know such a thing was possible:

"Funny story. We had a new team member joining, and @lhpeng suggested an orientation project for them of "Why don't you just try predicting age and gender from the images?" to get them familiar with our software setup, thinking age might be accurate within a couple of decades,and gender would be no better than chance. They went away and worked on this and came back with results that were much more accurate than expected, leading to more investigation about what else could be predicted."

[1]: https://twitter.com/JeffDean/status/965720435290791936

Just want to point out a few things that didn't get covered in the other thread:

- The standard CV risk calculators that we actually use in medicine don't do a tremendous job at discriminating risk. The AUC for the pooled risk equations tends to be around 0.74. As a refresher, AUC can be interpreted as the probability that someone with CVD has a risk score that is higher than someone without.

- This deep learning model had an AUC of about 0.7, similar to the pooled risk equation which was about 0.72

- Adding this deep learning model to the pooled risk equation did not change the AUC (0.72 -> 0.72). There may have been recategorization, but we can't tell from the paper as published.

- The pooled risk equations in this study did not have the second most important component: the lipid profile. (Age is the most important risk factor.) That absence hamstrings the baseline model compared to the deep learning model. We aren't seeing the standard pooled risk equations here.

- As my PI argues on Twitter, you could see this deep learning model as a very good age predictor.

- The fact that the model is paying "attention" to blood vessels and other structures that are relevant to humans is a promising sign.

“our algorithm could distinguish the retinal images of a smoker from that of a non-smoker 71% of the time.”

Wonder if smoking would decrease if people could see the change over time

It feels like we're approaching the time where a home diagnostic kit could be economically employed to check health status and screen for a host of diseases. You'd receive sample containers at home for blood, saliva, urine, breath. Premium package includes cheap stethoscope, retinoscope, EEG attachments for your smartphone. Samples are couriered to the test lab and the data analysed by AI software with the results available online both to you and your GP if desired.

I would be interested to know to what extent the prediction of cardiovascular risk goes beyond a linear combination of the other factors such as hypertension, diabetes, age. I.e. if we just prediction these variables with the network then run a linear regression is it less accurate? This would give some indication as to whether there is additional independent information that is useful in the retinal imaging.

> Given the retinal image of one patient who (up to 5 years) later experienced a major CV event (such as a heart attack) and the image of another patient who did not, our algorithm could pick out the patient who had the CV event 70% of the time.

To put that in perspective, in this study, a random choice would be right 50% of the time.

So this is interesting, but would it be useful diagnostic tool?

See also this discussion from a few hours prior (with a more prosaic but less informative title thanks to the Washington Post): https://news.ycombinator.com/item?id=16414263