New AI tech developed to detect heart failure earlier

Medic using stethoscope on womanImage source, getty
Image caption,

The algorithm uses patient data to try and predict those at risk of heart failure

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Artificial intelligence (AI) could be used to identify patients at risk of heart failure, meaning they could be treated earlier, Leeds-based researchers have said.

An algorithm, known as Find-HF, has been "trained" by the Leeds University researchers to detect early symptoms of the condition using patient records.

According to the British Heart Foundation (BHF), there are currently more than one million people in the UK with heart failure.

Prof Chris Gale, from Leeds Teaching Hospitals NHS Trust and the University of Leeds, said the tech would open a "crucial window of opportunity" for patients.

For the study, which was funded by the BHF, researchers used the patient records of 565,284 UK adults to train the AI algorithm,

It was then further tested on a database of 106,026 records from Taiwan National University Hospital.

The AI was able to accurately predict the patients at the highest risk of developing heart failure, and those who could be admitted to hospital with the condition, within five years, the researchers said.

'Quality of life'

Prof Gale, a consultant cardiologist, said: "This is an extremely powerful and unique national resource, and it is time to use these data to benefit patients.

"Find-HF could potentially bring diagnoses forward by two years."

The researchers suggested the platform could be used by GPs as an early warning system, allowing them to test and diagnose patients earlier.

Dr Ramesh Nadarajah, a health data research UK fellow at the University of Leeds, said: "Many people receive their diagnosis of heart failure at too late a stage when disease-modifying treatments are potentially less effective, especially women and older people.

"We are using machine learning tools with routinely collected data to identify people with heart failure earlier, so that they can get the right treatment and prevent hospital admissions and death, and improve quality of life."

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