Google AI Predicts Heart Disease and Stroke From Retinal Images

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The study authors said, "The opportunity to one day readily understand the health of a patient's blood vessels, key to cardiovascular health, with a simple retinal image could lower the barrier to engage in critical conversations on preventive measures to protect against a cardiovascular event". Using its algorithms to predict which patient within five years would actually have a heart attack or other major cardiovascular event, and which patient would not.

Published study reveals that this novel technique is much accurate for predicting the various cardiovascular diseases with the help of more invasive methods, which include attaching needle in the arms of patient. These parameters could be used to predict if the person had a raised risk of heart disease or a cardiac event such as a heart attack.

Traditionally, medical discoveries are often made through a sophisticated form of guess and test making hypotheses from observations and then designing and running experiments to test the hypotheses. "However, with medical images, observing and quantifying associations can often be hard because of the wide variety of features, patterns, colors, values, and shapes that are present in real data", the researchers wrote.

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Google AI retinal scan predicts heart disease such as strokes and heart attacks, without performing any test or blood draws.

The search giant said that it is looking forward to developing and testing the algorithm on larger and more comprehensive datasets to make it more useful for patients and doctors. "But we need to validate".

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.

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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.

Verily trained these models using data from almost 300,000 patients, with the system then associating these factors together. Results are most significant when the algorithm was tasked with determining specific risk factors. So, for example, if most patients that have high blood pressure have more enlarged retinal vessels, the pattern will be learned and then applied when presented just the retinal shot of a prospective patient.

In the era of AI and machine learning, doctors are using patterns, generated by algorithms, to recognise diseases. "We hope researchers in other places will take what we have and build on it".

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