Artificial intelligence can identify people most likely to have a deadly heart attack five years before it strikes, scientists have found.
Researchers from the University of Oxford used machine learning to develop a new biomarker that detects changes to blood vessels supplying the heart.
Described as a fingerprint, it can identify “biological red flags” such as inflammation, scarring and new blood vessel formation, which are pointers to a future heart attack.
Currently scans known as coronary CT angiograms (CCTA) check the coronary arteries for any narrowed or blocked segments, but there are no methods routinely used by doctors to spot these other, underlying characteristics.
Half of all heart attacks happen in people who do not have significant narrowing of the arteries.
Used alongside these scans, the new fat radiomic profile (FRP) can analyse changes to the fat surrounding the heart vessels to calculate how likely someone is to have a heart attack.
The researchers plan to make the technology available to health professionals over the next year, and hope it will be included in routine NHS practice within two years.
Professor Charalambos Antoniades, professor of Cardiovascular Medicine at the University of Oxford, said: “Just because someone’s scan of their coronary artery shows there’s no narrowing, that does not mean they are safe from a heart attack.
“By harnessing the power of AI, we’ve developed a fingerprint to find ‘bad’ characteristics around people’s arteries.
“This has huge potential to detect the early signs of disease, and to be able to take all preventative steps before a heart attack strikes, ultimately saving lives.
“We genuinely believe this technology could be saving lives within the next year.”
The findings, presented at the European Society of Cardiology (ESC) Congress in Paris, are published in the European Heart Journal.
The team compared the CCTA scans of 101 people who went on to have a heart attack or cardiovascular death within five years of the scan with those who did not, to understand what changes indicate someone is at an increased risk.
They tested the performance of the new fingerprint in 1,575 people and found that the technology could predict heart attacks to a greater accuracy than current tools used in clinical practice.
In the groups tested, the technology was 80-90% accurate at predicting the patients’ risk of a future heart attack.
The fingerprint became more accurate when more scans were used, the research, part-funded by the British Heart Foundation (BHF), found.
Professor Metin Avkiran, BHF associate medical director, said: “This research is a powerful example of how innovative use of machine learning technology has the potential to revolutionise how we identify people at risk of a heart attack and prevent them from happening.
“This is a significant advance. The new ‘fingerprint’ extracts additional information about underlying biology from scans used routinely to detect narrowed arteries.
“Such AI-based technology to predict an impending heart attack with greater precision could represent a big step forward in personalised care for people with suspected coronary artery disease.”
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