As with all deep learning analysis, neural networks were then used to mine this information for patterns, learning to associate telltale signs in the eye scans with the metrics needed to predict cardiovascular risk (e.g., age and blood pressure). With its system, Google's deep learning tech is able to predict cardiovascular risk in any given individual simply using images of their retina.
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 10 years.Through mass aggregation and deep learning, an AI program was able to accomplish this feat, and will only continue to improve as it is further trained, tested, and perhaps eventually used in the real world. It opens up a wealth of possibility in regards to preventive care and screening for heart disease. Apart from the retina scans they also recorded the medical data of these individuals.
Research states that the rear interior wall of the eye (the fundus) is chock-full of blood vessels that reflect the body's overall health.
In a paper published today (via The Verge), Verily and the Google AI teams detail their work analyzing blood vessels at the back of the eye to predict risk factors, like blood pressure and smoking, associated with cardiovascular disease.
While most of these factors can be ascertained by simply asking the patient, other factors such as cholestrol require drawing blood. Now, they found that images of the eye can also "very accurately" predict other indicators of cardiovascular health. 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. Explaining how the algorithm is making its prediction gives the doctor more confidence in the algorithm itself.
Google's parent company Alphabet has more than just the search giant under its banner; alongside its weird "X" experimental tech division sits its health science company Verily, which creates all manner of healthcare tech.
Verily trained these models using data from almost 300,000 patients, with the system then associating these factors together.