Artificial intelligence could be used to predict those at risk of a fatal heart attack up to five years in advance, new research has found.

Experts at the University of Oxford have developed a “fingerprint”, or biomarker, using machine learning.

The fat radiomic profile (FRP) detects biological red flags in blood vessels that supply blood to the heart, identifying inflammation, scarring and changes to the vessels – all pointers to a future heart attack.

When a patient is admitted to hospital with chest pain, it’s standard procedure for a coronary CT angiogram (CCTA) to be performed. If no narrowing of the arteries is detected – about 75% of cases – then the patient is sent home – yet some of them suffer a heart attack in the future.

There’s currently no method routinely used by doctors to spot all underlying red flags of a future heart attack. The researchers hope their AI tool will bridge this gap.

Lead researcher Charalambos Antoniades, professor of cardiovascular medicine and British Heart Foundation senior clinical fellow 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.”

Professor Antoniades and his team analysed the expression of genes associated with inflammation, scarring and new blood vessel formation. These were matched to the CCTA scan images from 167 cardiac surgery patients to determine which features best indicated changes to the fat surrounding the heart vessels.

Next, the team compared the CCTA scans of 101 people, from a pool of 5,487 patients, who went on to have a heart attack or cardiovascular death within five years of having a CCTA, to a selection of scans from patients who did not have a heart attack.

Using machine learning they developed the FRP fingerprint, which captures the level of risk. The more heart scans that are added to the system, the more accurate the predictions will become, and the more information that will become core knowledge.

The British Heart Foundation (BHF) part-funded the research.

Professor Metin Avkiran, associate medical director at BHF, 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.”

The research was presented at the European Society of Cardiology Congress in Paris and published in the European Heart Journal.

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