AI could detect signs of heart failure and heart valve disease in a routine ECG in the ‘blink of an eye’.
This is according to UK research, funded by the British Heart Foundation and led by Imperial College London.
The researchers recently presented the results at the European Society of Cardiology Congress in Munich.
An ECG often contains far more information than the human eye can typically see, and AI can now detect it in less than two seconds.
The team trained the technology on ECGs from millions of patients, then tested it on more than 65,000 ECGs from hospital patients in the US.
It was able to identify up to 81% of patients with heart failure and up to 90% of patients with a common type of heart valve disease. The technology cannot be used on its own to definitively diagnose or rule out heart failure or heart valve disease, but it can strongly indicate these conditions from an ECG.
The AI is now being tested on ECGs from 590 NHS patients across London and Bristol, and the team say the AI ECG technology could be two years away from routine use.
Dr Sonya Babu-Narayan, a consultant cardiologist and clinical director of the British Heart Foundation, said: ‘It is exciting to see that AI can now deliver a read-out from an ECG in what feels like the blink of an eye.’
Dr Ahmed El-Medany, a British Heart Foundation clinical research fellow at Imperial College London who led the analysis, said: ‘These results suggest there may be far more information hidden within a routine ECG than we can recognise by looking at it ourselves. It is particularly encouraging that this superhuman AI performed well across separate groups of patients. The important next questions are how it performs in NHS patients and whether the same information can be captured using much simpler portable ECG devices.’
Professor Fu Siong Ng, the senior investigator from Imperial College London, added: ‘Patients can often wait several months for a heart ultrasound scan after being referred by their doctor. This makes it exciting that our technology could identify patients most at risk of heart failure and heart valve disease, so they could be prioritised for scans sooner.’
