“The pandemic has resulted in a backlog of hundreds of thousands of people waiting for vital heart scans, treatment and care,” said Dr. Sonya Babu-Narayan, associate medical director at the BHF and consultant cardiologist. "Despite the delay in cardiac care, whilst people remain on waiting lists, they risk avoidable disability and death. That's why it's heartening to see innovations like this, which together could help fast-track heart diagnoses and ease workload so that in future we can give more NHS heart patients the best possible care much sooner.”

Worldwide, innovations are underway in the field of AI to diagnose heart disease and related conditions faster, as well as prevent them and their associated risk in the first place. Researchers at Johns Hopkins Hospital in Baltimore recently developed a machine learning score solution that more accurately predicts the likelihood of patients with suspected or known coronary artery disease dying within 10 years compared to established scores used by health professionals worldwide.


And unlike traditional methods that utilize clinical data in their calculations, the new score includes imaging information on the heart, measured by stress cardiovascular magnetic resonance (CMR), said study author Dr. Theo Pezel of the Johns Hopkins Hospital, Baltimore, US. “This is the first study to show that machine learning with clinical parameters plus stress CMR can very accurately predict the risk of death. The findings indicate that patients with chest pain, dyspnoea, or risk factors for cardiovascular disease should undergo a stress CMR exam and have their score calculated. This would enable us to provide more intense follow-up and advice on exercise, diet, and so on to those in greatest need.”

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