Google, which presented its findings Monday in the journal Nature Biomedical Engineering, says that such a method is as accurate as predicting cardiovascular disease through more invasive measures that involve sticking a needle in a patient's arm. "However, we don't precisely know in a particular individual how these factors add up, so in some patients, we may perform sophisticated tests ... to help better stratify an individual's risk for having a cardiovascular event such as a heart attack or stroke", declared study co-author Dr. Michael McConnell, a medical researcher at Verily.
"Traditionally, medical discoveries are made by observing associations, making hypotheses from them and then designing and running experiments to test the hypotheses".
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 and Verily's scientists used machine learning to analyze a medical dataset of almost 300,000 patients, as per the report. According to the team, they were able to quantify the association between the retinal vessels and cardiovascular risks identified by researchers from previous medical studies. Using the aforementioned technology, Google's algorithm was able to tell the two apart.
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"They're taking data that's been captured for one clinical reason and getting more out of it than we now do", he said. Discovering that we could do this is a good first step.
Doctors usually take into account various risk factors for patient assessment such as some genetic data (like age and sex) and data related to lifestyle components (such as a smoking and blood pressure). According to her, the operational methodology of the algorithm can in future allow Google to generate a heatmap that shows which pixels were the most important elements for a predicting a specific CV risk factor. 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 gray retinal image used by the deep-learning algorithm, blood pressure is highlighted in shades of green. "Rather than replacing doctors, it's trying to extend what we can actually do". DeepMind, the London-based AI-development firm bought by Google in 2014 that often operates autonomously, released research earlier this month showing similar algorithms could help detect signs of glaucoma and other eye diseases.