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The latest on Technology’s best at predicting cardiac arrest

Researchers are using AI, blood tests and even your own eyes to determine your chances of having a heart attack

An algorithm that marries AI with your doctor's consultation can predict heart attacks 10 years before they occur with 76% accuracy Blood vessels in the eyes can predict heart attack risk Analyzing blood samples for specific proteins, known as troponin, released by the heart muscle when damaged, can map a reson for heart seizures

Predicting and preventing heart attacks, which could happen to anyone, even the most fit of us, has been the lifelong focus of physicians.

They use cholesterol, blood pressure, lifestyle factors and health conditions such as diabetes to predict whether a patient is likely to suffer a heart attack.

Today, they have a new set of non-invasive indicators to make better predictions.

Predictions in a heart beat

Dr. Stephen Weng, research fellow for the National Institute for Health Research developed an algorithm that marries AI and face-to-face time with your doctor. His system can predict heart attacks 10 years before they occur with 76% accuracy.

“The algorithm sorts through all of the electronic patient records within a practice, recognizing a specific set of data that suggests a heart attack is imminent. In trials, the AI was able to determine an individual’s risk in a matter of minutes,” reports

In trials, the system predicted 7.6% more events than traditional methods with 1.6% fewer false alarms.

In the sample size of 83,000 records, that means an additional 355 lives could have been saved using the AI compared to standard prediction methods.

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Your eye could be better than AI

Dr. Lily Peng, a product manager at Google, works with a team of researchers to learn how blood vessels in the eyes can predict heart attack risk. According to Peng, patients’ retinal scans contribute valuable information to her group’s algorithm.

Together, the components were quite successful at recognizing which patients had experienced a cardiovascular event. “Given the retinal image of one patient who later experienced a major cardiovascular (CV) event, such as a heart attack, and the image of another patient who did not, our algorithm could pick out the patient who had the CV event 70% of the time,” says Peng. “This performance approaches the accuracy of other CV risk calculators that require a blood draw to measure cholesterol.”

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Machine Learning and your arteries’ health

A new Biomarker ‘Fingerprint’ with AI Technology can now predict future heart attacks

Researchers at the University of Oxford have used machine learning to help estimate the health of arteries and have developed a new biomarker to predict heart disease, and prevent future heart attacks. 

The researchers claim it can identify people at risk five years before it strikes. 

A narrowing of the coronary artery isusually a good indicator for future heart seizures but no narrowing that does not mean people are safe from a heart attack.” 

The new biomarker ‘fingerprint’ is called fat radiomic profile (FRP) and was developed using machine learning. It detects biological ‘red flags’ in the perivascular space lining of blood vessels. Signs of inflammation, scarring, or any change in these blood vessels can point to the possibility of future heart attacks. 

The findings were published in the European Heart Journaland the study was funded by the British Heart.

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A simple blood test can do the trick

The American Heart Association News said that getting a simple blood test can now help doctors predict your risk for having a heart attack or stroke.

The test involves analyzing blood samples for specific proteins, known as troponin, released by the heart muscle when damaged.

The tests’ findings were published in the American Heart Association journal Circulation.

Researchers examined a group of 8,121 people, ages 54 to 74, with no history of cardiovascular disease. Troponin levels were detected in 85% of the group. Higher levels of the protein were associated with a greater chance of developing cardiovascular disease, particularly heart failure.