A team of researchers at the University of Western Australia is coming up with an artificial intelligence-based risk assessment tool to better detect plaque in heart computed tomography scans.


The research team comprising AI and cardiac imaging experts was recently awarded a grant worth AU$896,606 (about $695,000) from the government's Medical Research Future Fund to develop a tool predicting the risk of coronary heart disease from heart CT scans.

In a statement, UWA said the tool could "determine if plaque build-up has narrowed the coronary arteries", identifying patients most at risk of adverse cardiovascular events. It veers from traditional methods which are said to be "cumbersome, time-consuming and may have limited accuracy".

This approach, UWA claimed, will enable "more accurate diagnosis and faster reporting across all aspects of healthcare, improving the quality and consistency of patient care".

Coronary heart disease is the leading single cause of death in Australia in 2018, claiming 17,500 lives, according to the Australian Institute of Health and Welfare. This accounted for 42% of all cardiovascular deaths in the year. The institute noted that an estimated 580,000 adult Australians had the disease in 2017-2018.


Medical technology firm Artrya, the organisation that will co-develop the AI tool, recently raised AU$15 million ($11.6 million) to scale its AI-driven imaging software that accurately detects signs of heart disease, including plaque that are presently difficult to spot.

Meanwhile, in April, Caristo Diagnostics received a CE mark for its AI technology that can identify people at risk of a fatal heart attack, years before it strikes. Utilising coronary CT angiogram scans, the technology is touted to accurately measure inflammation of blood vessels in and around the heart.


"Our artificial intelligence-based risk prediction system will be able to define groups based on heart CT scans and will identify patients at risk of heart attack and also those who would most benefit from treatment," said Professor Girish Dwivedi, who leads the research team.



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