A new AI tool that automatically measures the amount of fat around the heart from MRI scans could help predict the risk of developing diabetes and other diseases.
Using the new tool, the team led by researchers from Queen Mary University of London was able to show that a larger amount of fat around the heart is associated with significantly greater chances of developing diabetes, regardless of a person’s age, sex, and body mass index.
The distribution of fat in the body can influence a person’s risk of developing various diseases. The commonly used measure of body mass index (BMI) mostly reflects fat accumulation under the skin, rather than around the internal organs. In particular, there are suggestions that fat accumulation around the heart may be a predictor of heart disease, and has been linked to a range of conditions, including atrial fibrillation, diabetes, and coronary artery disease.
In a statement, lead researcher Dr Zahra Raisi-Estabragh from Queen Mary University of London said: “Unfortunately, manual measurement of the amount of fat around the heart is challenging and time-consuming. For this reason, to date, no-one has been able to investigate this thoroughly in studies of large groups of people.
“To address this problem, we’ve invented an AI tool that can be applied to standard heart MRI scans to obtain a measure of the fat around the heart automatically and quickly, in under three seconds. This tool can be used by future researchers to discover more about the links between the fat around the heart and disease risk, but also potentially in the future, as part of a patient’s standard care in hospital.”
The research team tested the AI algorithm’s ability to interpret images from heart MRI scans of over 45,000 people, including participants in the UK Biobank, a database of health information from over half a million participants from across the UK. The team found that the AI tool was accurately able to determine the amount of fat around the heart in those images, and it was also able to calculate a patient’s risk of diabetes.
“The AI tool also includes an in-built method for calculating uncertainty of its own results, so you could say it has an impressive ability to mark its own homework,” said Dr Andrew Bard from Queen Mary University of London.
The research, published in the journal Frontiers in Cardiovascular Medicine, was funded by the CAP-AI programme.