Science
AI research gives unprecedented insight into heart genetics and structure
A ground-breaking research study we’ve part-funded has used AI to understand the genes that determine aspects of the heart’s left ventricle, using three-dimensional images of the organ.
The international team, led from the University of Manchester, used cutting-edge unsupervised deep learning to analyse over 50,000 three-dimensional magnetic resonance images (MRIs) of the heart from UK Biobank.
The study, published in the leading journal Nature Machine Intelligence, focused on uncovering the intricate genetic causes behind cardiovascular traits. It has revealed 49 new genetic locations that have an association with traits such as the structure of the left ventricle, along with 25 additional locations which possibly also have an effect.
The findings have significant implications for cardiology and precision medicine. By uncovering more about the genetic basis of cardiovascular traits, the research paves the way for the development of targeted therapies and interventions for people at risk of heart disease.
“Power of big data”
Professor Bryan Williams, our Chief Scientific and Medical Officer, said: “This new research shows the huge power of big data linking genes to heart structure. Machine learning has made this possible by transforming how we process, analyse and gain insights from big data to tackle the biggest questions in cardiovascular research.
“This pioneering new method has uncovered many more genes that influence the structure and function of the heart, which will lead to new insights into why abnormal structure and function can lead to heart disease.
“Heart and circulatory diseases are still devastating millions of lives each year in the UK. AI could unlock more information about the genes that contribute to the structure of the heart. In future this could lead to real improvements for patients, including the development of tailored, precision treatments for people with heart problems.”
“A beacon for future studies”
Professor Bernard Keavney, BHF Professor of Cardiovascular Medicine at the University of Manchester, who was involved in the study, said: “Employing cutting-edge deep learning to integrate genetic and imaging data has shed light on the genetic underpinnings of heart structure. This approach is a beacon for future organ studies and understanding genetic influences on organ anatomy.”
The research was also funded by the Royal Academy of Engineering (RAEng), The Royal Society, and the Argentinean National Scientific and Technical Research Council (CONICET). It was a collaboration between the University of Manchester, University of Leeds, the Argentinian National Scientific and Technical Research Council, and IBM Research.