Google Uses Deep Learning To Help Predict Heart Disease Risk

До чего техника дошла в Google научились предсказывать болезни сердца по сетчатке глаза

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Google researchers used scanned images of retinas of more than 280,000 patients to develop this algorithm.

Deep-learning networks have already been used before to come up with algorithms that are able to diagnose diseases such as melanoma and blindness resulting from diabetes.

Google scientists are presenting research on an algorithm that can accurately assess a patient's heart attack risk by examining retinal fundus images.

Heart disease and stroke are the world's largest causes of death, accounting for more than half of all deaths worldwide in 2015, according to the World Health Organization.

Apple late past year launched a heart study tied to its Apple Watch to see if it could detect and alert people to irregular heart rhythms that could be a sign of atrial fibrillation, a leading cause of stroke. Researchers from Alphabet (Google's parent company) subsidiary Verily discovered this new method by deploying machine learning. This can then be used to predict their risk of suffering a major cardiac event - such as a heart attack - with roughly the same accuracy as current leading methods.

Veirly's software can also look at data such as individual's age, blood pressure, and whether or not they smoke before it can predict outcomes.

If the eyes are the window to the soul, Google thinks it's proven that the retina can offer a window to the state of the heart. The AI was then able to gather patterns from this data and understand which signs in the eyes scans pointed towards cardiovascular risk.

To train the algorithm, the researchers used a data set of nearly 300 thousand patients.

According to Peng, the computer vision algorithm can distinguish between the retinal images of a smoker from that of a non-smoker at least seven out of 10 times.

Despite the proof that machine learning can be accurate for interpreting risk factors based on medical images, one of the greatest challenges for wider adoption of machine learning in medicine is showing how these algorithms arrive at their conclusions. When given the retinal image of a patient who experienced a major cardiovascular event up to five years after the image was taken, and the image of a patient who did not, the algorithm could determine which patient experienced the health event 70% of the time. For comparison, a conventional method for the assessment of cardiovascular risk requires analysis of blood and gives the correct predictions in 72% of cases.

Google also made sure to determine how the algorithm was making its prediction. In addition, this technique could help generate hypotheses for future scientific investigations into CV risk and the retina.

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