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AI ‘could spot heart problems’

Tech has also been used to speed up the detection of fatal kidney conditions

Artificial intelligence (AI) could have discovered a new way of identifying a common condition that causes the heart to beat irregularly.

Atrial fibrillation affects one million people in the UK and increases the risk of stroke and long-term heart problems. It is relatively simple to diagnose when the heart is beating irregularly, but not when it returns to normal.

Computer modelling at the Mayo Clinic in the US may have identified signs that indicate previous abnormalities.

Researchers said it was still early days, but believe the system could lead to earlier and easier detection of the problem and, therefore, ensure patients get the right treatment, saving lives.

In the study, published in The Lancet, computer modelling was asked to look out for what doctors believe are subtle signs of past irregular rhythms, including scarring of the heart, which are unable to be spotted by the human eye from test results.

The computer modelling analysed tests carried out on nearly 181,000 patients between 1993 and 2017. They were all patients who had had normal test results at first.

The modelling correctly identified the subsequent diagnosis from the normal test results in 83% of cases.

Prof Tim Chico, an expert in cardiology at the University of Sheffield, told BBC News an AI-based approach could provide a revolutionary advance, although he added that the research is still in the early stages.

A separate study, published in the journal Nature Digital Medicine, shows a mobile phone app speeds up the detection of a potentially fatal kidney condition in hospital patients.

The new alerting system, known as Streams, developed by the Royal Free Hospital with technology firm DeepMind, sends results straight to front-line clinicians in the form of easy-to-read results and graphs.

One of the blood tests looks for high levels of a waste product called creatinine, which is normally filtered out by the kidneys. Information on other blood markers which can help treat patients is also made available quickly to specialists via the app.

Data from around 12,000 alerts on acute kidney injury using the new system was evaluated by University College London. The findings found there was no step change in patient recovery rates but there had been significant improvement in recognising acute kidney injury rapidly.