Google Develop AI That Can Predict Risk Of Heart Disease Through Eye Scan

Google Develop AI That Can Predict Cardiovascular Problems Through Eye ScanImage used is for illustration purpose only

Researchers of Google and its health-tech subsidiary, Verily, have developed an artificial intelligence algorithm (AI) that can predict the risk of heart disease by scanning a person’s eyes.

Google AI approach uses deep learning to draw connections between changes in the human anatomy and disease, akin to how doctors learn to associate signs and symptoms with the diagnosis of a new disease.

Google said using technology such as deep learning and AI, the company’s software can accurately predict their risk of suffering a major cardiac event, such as a heart disease.

By analyzing scans of the back of a patient’s eye, the company’s software is able to accurately deduce data, including an individual’s age, blood pressure and whether or not they smoke.

The reason the eye is so important for research is that the rear interior wall of the eye is full of blood vessels which can be used to judge a person’s overall health.

The algorithm potentially makes it quicker and easier for doctors to analyze a patient’s cardiovascular risk, as it doesn’t require a blood test.

Google researchers fed scans from the retinas of more than 280,000 patients into its intricate pattern-recognizing algorithms, known as neural networks.

Those scans helped train the networks on which telltale signs tended to indicate long-term health dangers.

AI technology opens almost entirely new doors and finding new ways of doing things.

It shows the potential of AI to investigate, quantify and put to concrete evidence what even the most knowledgeable human may chalk up to a hunch.

Google’s algorithm could mean quicker predictions of cardiovascular risk. But, the method will need to be tested more thoroughly before it can be used in a clinical setting.

The results have been published in an article entitled “Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning” in the Nature journal Biomedical Engineering.

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