Alphabet's Verily analyzing retinas w/ machine learning to predict heart disease

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

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

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

In the gray retinal image used by the deep-learning algorithm, blood pressure is highlighted in shades of green. The algoritm was also able to infer a person's ethnicity, which is also a factor in cardiovascular related disease. These diseases have remained the leading causes of death globally for the last 15 years, the organization noted.

In a report that appeared on The Verge, Google said using technology such as machine learning and artificial intelligence (AI), the company's software can accurately deduce data, including an individual's age, blood pressure, and whether or not they smoke. Using this information, it's then possible to assess someone's risk of suffering a heart attack or developing heart disease.

However, deep learning techniques can also be used to increase the accuracy of diagnoses for these conditions, Peng wrote.

Google has allegedly been working on predicting eye diseases using artificial intelligence for a while now.

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. The discovery may point to more ways to diagnose health issues from retinal images, researchers said.

For the study, the scientists developed deep learning models using retinal fundus images of almost 3, 00,000 people available from two countries; the United Kingdom and the U.S. and validated them using those from another 13,000 patients.

The research was published earlier today in the journal Nature, titled "Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning".

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 presented images of the eyes of two different people - one who suffered a major adverse cardiac event such as a heart attack or stroke within five years of the photo and the other who did not - the algorithms could correctly pick the patient who fell ill 70 percent of the time. The latter distinction was performed with a 71-percent accuracy, while the system took blood pressure identification up a notch by predicting the patient's systolic pressure within an average of 11mmHg.

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