Amazing: AI algorithm by Google which can detect heart disease

AI looks into your eyes to predict cardio risk

The algorithms correctly discovered patients who had great cardiovascular risks within a 5-year window 70 percent of the time.

Publishing its findings in the journal Nature Biomedical Engineering, the researchers working with the company used the power of machine learning to analyse the datasets of 300,000 patients, including both eye scans and health details.

The researchers tested the algorithm on a set of scans from about 12,000 patients, roughly 100 of whom had a heart attack within five years following their retinal imaging.

"Here, we show that deep learning can extract new knowledge from retinal fundus images". By studying their appearance with camera and microscope, doctors can infer things like an individual's blood pressure, age, and whether or not they smoke, all of which are crucial indicators of cardiovascular health.

Training deep-learning models on data from more than a quarter of a million patients, the scientists were able to predict the cardiovascular risk factors that were not previously thought to be present in retinal fundus images.

Alun Hughes, a professor at University College London thinks Google may be on to something due to a "long history of looking at the retina to predict cardiovascular risk", but he still thinks the algorithm merits further tests to be certain, The Verge reported. Given the fact that heart diseases is one of the leading causes of deaths in the world, this algorithm could be a big breakthrough.

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-recognizing algorithms, known as neural networks.

Google researchers hope to flawless this method and machine further so that it could analyse the present data more accurately without the aid or direction of humans.

The new tech could enable doctors to screen their patients easier and give them the proper treatment and could also enable patients to assess their health risks by themselves. Using the aforementioned technology, Google's algorithm was able to tell the two apart.

"They're taking data that's been captured for one clinical reason and getting more out of it than we now do", he said. By reviewing this massive database, Google's algorithm trained itself to recognize the patterns that designated people as at-risk. The green traces are the pixels used to predict the risk factors.

That said, it's clear that artificial intelligence and machine learning have the potential to bring added convenience and affordability to the healthcare industry, even in areas as small as our eyes.

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