New Artificial Intelligence Tool To Predict Diabetes Risk

A new artificial intelligence (AI) tool automatically measures the amount of fat around the heart from MRI scan images. The increased amount of fat around the heart is associated with diabetes risk independent 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. Body mass index (BMI) is a commonly used measure that reflects the fat accumulation under the skin than around the internal organs.

‘A novel artificial intelligence (AI) tool predicts diabetes risk by measuring the fat around the heart.’

In the same way, fat accumulation around the heart can be a predictor of heart disease, and other linked conditions such as atrial fibrillation, diabetes, and coronary artery disease.

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, researchers from the Queen Mary University of London 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. They published their findings in the journal Frontiers in Cardiovascular Medicine.

This tool can be used by future researchers to discover more about the links between the fat around the heart and disease risk that can be a 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 more than 45,000 people, including participants in the UK Biobank, a database of health information from over half a million participants from across the UK.

Later, they discovered that the AI tool could determine the amount of fat around the heart in those images accurately and calculated diabetes risk.

If the clinical utility of this tool is demonstrated, it can be applied in clinical practice to improve patient care. This work also highlights the value of cross-disciplinary collaborations in medical research, particularly within cardiovascular imaging.



Source: Medindia