AI Technology Revolutionises Heart Attack Prediction: A Game-Changer for Healthcare
Scientists are hailing an innovative technology that can identify individuals at risk of a heart attack within the next decade. This cutting-edge artificial intelligence (AI) model, developed by Caristo Diagnostics, an Oxford University spinout company, detects inflammation in the heart that remains undetectable by traditional CT scans. Five hospital trusts in Oxford, Milton Keynes, Leicester, Liverpool, and Wolverhampton are currently piloting this breakthrough technology, with a decision on its widespread implementation within the NHS expected soon.
A Revolutionary Approach to Heart Attack Prevention
Caristo Diagnostics' CaRi-Heart AI platform represents a significant advancement in cardiovascular care. Traditional CT scans, which utilise a combination of X-rays and computer technology, often miss the subtle signs of heart inflammation. However, the CaRi-Heart AI model can detect these hidden indicators, offering a transformative approach to early diagnosis and prevention.
Professor Keith Channon from the University of Oxford highlighted the significance of this technology, stating, "For the first time, we can detect the biological processes that are invisible to the human eye, which precedes the development of narrowings and blockages within the heart."
The Pilot Project and Its Impact
The pilot project, supported by NHS England, involves analysing routine CT scans of patients suffering from chest pain using the CaRi-Heart AI platform. Trained operators verify the accuracy of this AI-driven algorithm's detection ofcoronary inflammation and plaque. Increased inflammation closely correlates with a higher risk of cardiovascular disease and fatal heart attacks, according to research.
According to the British Heart Foundation (BHF), approximately 7.6 million people in the UK live with heart disease, costing the NHS in England around £7.4 billion annually. In the UK, about 350,000 patients receive referrals for cardiac CT scans annually.
Significant Findings from the Orfan Study
The Oxford Risk Factors and Non-invasive Imaging (Orfan) study, involving 40,000 patients and published in The Lancet, revealed that 80% of individuals were sent back to primary care without a defined prevention or treatment plan. However, the study found that patients with inflammation in their coronary arteries had a 20 to 30 times higher risk of dying from a cardiac event over the next ten years. The AI technology prescribed medication or encouraged lifestyle changes for 45% of these patients, significantly reducing their risk of future heart attacks.
Real-Life Impact: A Patient's Perspective
Ian Pickard, a 58-year-old from Barwell in Leicestershire, participated in the Orfan study after experiencing persistent chest pain. The AI analysis suggested he was at high risk of a heart attack, leading to his prescription of statins, quitting smoking, and increasing exercise. Reflecting on his experience, Pickard said, "It's a huge wake-up call. When you see it on paper, you realise how serious it is. It's something you can look at each day and think, 'I've got to do something about this'."
The Future of AI in Healthcare
Professor Charalambos Antoniades, the lead of the Orfan study, emphasised the limitations of previous risk calculators, which could only assess general risk factors like diabetes, smoking, or obesity. With AI technology, healthcare professionals can identify disease activity in the arteries before it fully develops, enabling early intervention and the prevention of heart attacks.
The National Institute for Health and Care Excellence (NICE) is evaluating this technology to decide on its potential rollout across the NHS. Additionally, the AI model is under review in the US and has already received approval for use in Europe and Australia.
The introduction of AI technology for predicting heart attacks marks a pivotal moment in healthcare. By detecting early signs of heart inflammation that traditional methods miss, this innovative approach can save countless lives and significantly reduce healthcare costs. As the NHS and other global health institutions consider adopting this technology, the potential for improving patient outcomes and preventing heart disease on a large scale becomes increasingly promising.