healthcare technology

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.

A team led by researchers from Queen Mary University of London has developed a new artificial intelligence (AI) tool that is able to automatically measure the amount of fat around the heart from MRI scan images.

Using the new tool, the team was able to show that a larger amount of fat around the heart is associated with significantly greater odds of diabetes, independent of a person's age, sex, and body mass index.

The research team 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 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.

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

read the research published at https://www.frontiersin.org/articles/10.3389/fcvm.2021.677574/full

read more at https://www.sciencedaily.com/releases/2021/07/210707112427.htm

also at the QMUL website https://www.qmul.ac.uk/media/news/2021/smd/ai-predicts-diabetes-risk-by-measuring-fat-around-the-heart-.html

New artificial intelligence technology that uses a common CT angiography (CTA), as opposed to the more advanced imaging normally required to help identify patients who could benefit from endovascular stroke therapy (EST), is being developed at The University of Texas Health Science Center at Houston (UTHealth).

Two UTHealth researchers worked together to create a machine-learning artificial intelligence tool that could be used for assessing a stroke at every hospital that takes care of stroke patients - not just at large academic hospitals in major cities. 

Research to further develop and test the technology tool is funded through a five-year, $2.5 million grant from the National Institutes of Health (NIH). 

"The vast majority of stroke patients don't show up at large hospitals, but in those smaller regional facilities. And most of the emphasis on screening techniques is only focused on the technologies used in those large academic centers. With this technology, we are looking to change that," said Sunil Sheth, MD, assistant professor of neurology at McGovern Medical School at UTHealth.

Sheth set out with Luca Giancardo, PhD, assistant professor with the Center for Precision Health at UTHealth School of Biomedical Informatics, to develop a quicker way to assess patients. The result was a novel deep neural network architecture that leverages brain symmetry. Using CTAs, which are more widely available, the system can determine the presence or absence of a large vessel occlusion and whether the amount of "at-risk" tissue is above or below the thresholds seen in those patients who benefitted from EST in the clinical trials.

"This is the first time a data set is being specifically collected aiming to address the lack of quality imaging available for stroke patients at smaller hospitals," Giancardo said.

read the complete press release with further details on the work at https://www.uth.edu/news/story.htm?id=9fccdefb-ff91-4775-a759-a786689956ea