
Assessing Cardiovascular Risk Factors with Computer Vision - mxschumacher
https://research.googleblog.com/2018/02/assessing-cardiovascular-risk-factors.html
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rkaplan
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](https://twitter.com/JeffDean/status/965720435290791936)

~~~
gwern
Something to keep in mind the next time someone assures you that 'gaydar' is
pseudoscience and totally impossible and any paper must be datamining -
everything is correlated. We are routinely surprised what can be extracted
from data, and anyone telling you a priori what cannot be done is pushing
their politics.

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carbocation
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.

~~~
epmaybe
> paying "attention" to blood vessels and other structures that are relevant
> to humans

Why is that necessarily a good thing? The point of machine learning is to
unveil relationships that we may or may not have thought of. For all we know
there's a structure we've never looked at that provides better correlates with
disease, and if a model pays attention to that it would be an equally
promising sign.

I understand that in medical research it would be best to have a
physiologically relevant correlate that is being recognized by the model, but
it doesn't mean that we should completely disregard the other pixels that a
model could find important either.

~~~
carbocation
I agree with you. It's a double edged sword. It's good because if you want to
put something like this into clinical use, physicians are going to be
skeptical if it's not interpetable. It's bad because, if humans have already
identified the relevant structural relationships, there is less likely to be a
big breakthrough. Nevertheless, one could imagine a model identifying features
that humans wouldn't consider even within the structures that we already know
about.

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melling
“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

~~~
moreless
Probably not. They already know the outcomes and don't seem to care.

However 71% seems like a very low number... Does that mean that in 7 out of 10
cases the system guesses correctly if the patient is smoker or not? That's not
really impressive IMHO. What am I missing?

~~~
VMG
what is impressive is that humans cannot do it

~~~
TeMPOraL
They can get to 50% with a coin.

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truculation
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.

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sungam
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.

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leereeves
> 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?

~~~
graeham
To be truely useful as a diagnostic in major decision-making, it needs to be
90-95%+ accurate.

But unfortunately there isn't anything close to this yet.

High blood pressure, for example, probably more accurate than the retinal
image technique here.
[https://www.ncbi.nlm.nih.gov/pubmed/25632496](https://www.ncbi.nlm.nih.gov/pubmed/25632496)

The major goal here would be to screen for people to go for a more advanced
test, like an MRI. But even _that_ wouldn't hit the 90-95% level of predictor.

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carbocation
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](https://news.ycombinator.com/item?id=16414263)

