Google Retinal Scans Can Predict if You Will Have a Heart Attack

Google AI-can-predict-cardiovascular-problems

The report states: "Most cardiovascular risk calculators use some combination of these parameters to identify patients at risk of experiencing either a major cardiovascular event or cardiac-related mortality within a pre-specified time period, such as ten years". Using this data, the company's software can predict the risk of cardiovascular diseases such as heart attack or stroke.

"Traditionally, medical discoveries are made by observing associations, making hypotheses from them and then designing and running experiments to test the hypotheses", reads the preamble to a paper published today on Nature Biomedical Engineering.

Google and Verily's scientists used machine learning to analyze a medical dataset of almost 300,000 patients, as per the report.

"They're taking data that's been captured for one clinical reason and getting more out of it than we now do", he said. After seeing hundreds of thousands of these kinds of images, the machine will start to see patterns correlated with the medical data inserted. "Our work also suggests avenues of future research into the source of these associations, and whether they can be used to better understand and prevent cardiovascular disease", conclude the authors of the study. Their results indicate that their predictions are accurate 70% of the time.

Further research will help determine whether the programme needs to be adjusted to analyse larger or smaller images, the website adds, while a "larger data set" would help improve the AI system's accuracy. Discovering that we could do this is a good first step. Google previously found that these methods can accurately detect diabetic eye disease. Using attention techniques, the researchers generated a heatmap that showed which pixels were the most important for predicting a specific cardiovascular risk factor.

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While doctors can typically distinguish between the retinal images of patients with severe high blood pressure and normal patients, the algorithm could go further to predict the systolic blood pressure within 11mm Hg on average for patients overall, including those with and without high blood pressure.

As part of this, researchers trained deep learning algorithms using data on 284,335 patients.

Deep learning networks have already been applied to produce algorithms capable of diagnosing diseases like melanoma and blindness caused by diabetes.

By predicting cardiovascular risks early, Google and Verily are not trying to replace doctors.

Do you believe that deep learning systems, like the one developed by Google AI, could one day expedite the process of determining diseases before they cause significant harm to patients?

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