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In the medical field, Artificial Intelligence (AI) mainly uses computer techniques to perform clinical diagnoses and suggest treatments.
Interestingly, the AI excels at identifying the intricate patterns amid tons of data and can do so at a scale and speed beyond human capacity. Many experts believe that this technology can be harnessed to help doctors and patients make better health-care decisions.
Predicts heart attack five years before!
On this note, researchers at the University of Oxford have developed a new technology powered by AI that can identify people at high risk of a heart attack five years before it strikes.
Notably, the findings are being presented at the European Society of Cardiology (ESC) Congress in Paris and published in the European Heart Journal. This study was funded by the British Heart Foundation (BHF) along with the National Institute for Health Research.
Fat Radiomic Profile (FRP)
Researchers have developed a biomarker or fingerprint dubbed as the fat radiomic profile (FRP) with the help of machine learning. According to the study, FRP detects biological red flags in the space lining blood vessels that supply blood to the heart. It identifies inflammation, scarring, and changes to these blood vessels, which are all pointers to a future heart attack.
Professor Charalambos Antoniades, Professor of Cardiovascular Medicine and BHF Senior Clinical Fellow at the University of Oxford, said:
“We genuinely believe this technology could be saving lives within the next year.”
No fixed routine!
At present, patients experiencing chest pains are sent for a CT scan. This is a scan of the coronary arteries to check for any narrowed or blocked segments. If there is no significant narrowing of the artery, patients are sent home without treatment, despite the fact many will later go on to have heart attacks.
Antoniades 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.”
Predicts with 90 percent accuracy!
The new technology developed by Professor Charalambos Antoniades and his team can detect the dangerous build-up of fat and scar around the organ. Furthermore, this tech allows doctors to predict the likelihood of a heart attack with 90 percent accuracy over the years.
Antoniades, added:
By harnessing the power of AI, we’ve developed a fingerprint to find ‘bad’ characteristics around people’s arteries. This has enormous potential to detect early signs of disease and to be able to take all preventative steps before a heart attack strikes, ultimately saving lives.
Furthermore, the team compared the CCTA scans of people who went on to have a heart attack or cardiovascular death within five years with those who did not, to understand the changes blood vessels which indicate that someone is at higher risk of a heart attack.
Professor Metin Avkiran, our Associate Medical Director, said:
Every 5 minutes, someone is admitted to a UK hospital due to a heart attack. 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.
Also, the team tested the performance of this perivascular fingerprint in 1,575 people in the SCOT-HEART trial, showing that the FRP had an outstanding value in predicting heart attacks.
Avkiran added:
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.
Rolling out to health care professionals!
The team led by Professor Charalambos Antoniades is planning to roll out its technology to health care professionals in the next year with the hope that it will be included in routine NHS practice alongside CCTA scans in the next two years.
Stock photos from Gorodenkoff/Shutterstock
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