Heart failure risk can be detected early by AI: Study
For the study, scientists, including those at The Queen Mary University of London, applied AI to analyse the heart MRI images of 17,000 healthy UK Biobank volunteers.
For the study, scientists, including those at The Queen Mary University of London, applied AI to analyse the heart MRI images of 17,000 healthy UK Biobank volunteers.
For the study, scientists, including those at The Queen Mary University of London, applied AI to analyse the heart MRI images of 17,000 healthy UK Biobank volunteers.
London: Researchers have found a way to apply artificial intelligence (AI) for earlier diagnosis and treatment for people at risk of heart failure.
Scientists, including those at The Queen Mary University of London, applied AI to analyse the heart MRI images of 17,000 healthy UK Biobank volunteers.
They found that the genes of a person influence 22-39 per cent of variation in the size and function of the heart's left ventricle -- the organ's main pumping chamber, whose enlargement and reduced pumping function can lead to heart failure.
The study, published in the journal Circulation, identified 14 regions in the human genome that are associated with the size and function of the left ventricle -- each containing genes that regulate the early development of heart chambers and the contraction of heart muscle.
"It is exciting that the state-of-the-art AI techniques now allow rapid and accurate measurement of the tens of thousands of heart MRI images required for genetic studies," said lead researcher Nay Aung from Queen Mary University of London.
The study, he added, increased the possibility of identifying people at the risk of heart failure much sooner than earlier methods allowed, and enabled new targeted treatments.
According to the researchers, the study established genetic risk scores for people which could be tested in future studies to create an integrated and personalised risk assessment tool for heart failure.
"The AI tool allowed us to analyse images in a fraction of the time it would otherwise have taken," Aung said.
The research team added that they were developing these AI algorithms to analyse other aspects of cardiac structure and function.
According to Aung, the algorithm may translate to time and cost savings for the NHS, and may potentially improve the efficiency of patient care.
"Previous studies have shown that differences in the size and function of the heart are partly influenced by genes but we have not really understood the extent of that genetic influence," said co-author Steffen Petersen, Professor of Cardiovascular Medicine at Queen Mary University of London.
He added that several genes known to be important in heart failure also appear to regulate the heart size and function in healthy people.
According to Petersen, the understanding of the genetic basis of heart structure and function improved the knowledge of how heart failure evolved.
"The study provides a blueprint for future genetic research involving the heart MRI images in the UK Biobank and beyond," he added.