This appeared last
week:
Google's neural networks detect heart attack risk by looking at patients' eyes
Drew Harwell &
Carolyn Y. Johnson
Published:
February 20 2018 - 1:50PM
By looking at the
human eye, Google's algorithms were able to predict whether someone
had high blood pressure or was at risk of a heart attack or stroke,
researchers at the company have confirmed, opening a new opportunity
for artificial intelligence in the vast and lucrative global health
industry.
The algorithms
didn't outperform existing medical approaches such as blood tests,
according to a study of the finding published in the journal Nature
Biomedical Engineering. The work needs to be validated and
repeated on more people before it gains broader acceptance, several
outside physicians said.
But the new
approach could build on doctors' current abilities by providing a
tool that people could one day use to quickly and easily screen
themselves for health risks that can contribute to heart disease, the
leading cause of death worldwide.
"This may be
a rapid way for people to screen for risk," Harlan Krumholz, a
cardiologist at Yale University who was not involved in the study,
wrote in an email. "Diagnosis is about to get turbo-charged by
technology. And one avenue is to empower people with rapid ways to
get useful information about their health."
Google researchers
fed images scanned from the retinas of more than 280,000 patients
across the United States and United Kingdom into its intricate
pattern-recognising algorithms, known as neural networks. Those scans
helped train the networks on which telltale signs tended to indicate
long-term health dangers.
Medical
professionals today can look for similar signs by using a device to
inspect the retina, drawing the patient's blood or assessing risk
factors such as their age, gender, weight and whether they smoke. But
no one taught the algorithms what to look for; instead, the systems
taught themselves by reviewing enough data to learn the patterns
often found in the eyes of people at risk.
The true power of
this kind of technological solution is that it could flag risk with a
fast, cheap and noninvasive test that could be administered in a
range of settings, letting people know if they should come in for
follow-up.
Lots more here:
I am sure that
this is just the ‘thin end of the wedge’ as they say and that we
will see more and more systems trained on vast data-sets to
out-diagnose the diagnosticians. The time is slowly but surely
coming!
David.
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