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 prevent heart attack


How the Valley Hospital is using AI to prevent heart attacks

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Research shows 50% of patients who will experience a heart attack will do so without experiencing prior warning symptoms. That's not good, considering heart attacks are the most common cause of death in both men and women. The Valley Hospital is working to change theat. Valley is among the first to use noninvasive, artificial intelligence imaging technology to prevent heart attacks by characterizing unstable plaque buildup with a high potential to rupture that can lead to a heart attack, the hospital said. Understanding that heart attack prevention would benefit high-risk, nonsymptomatic patients, such as individuals diagnosed with diabetes or high cholesterol, Cleerly Coronary has pioneered an alternate approach to heart attack prevention that focuses on identifying the critical characteristics of coronary artery plaque.


Google grants U of Sydney $1M to develop AI to prevent heart attacks

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The University of Sydney's Westmead Applied Research Centre has been awarded a $1 million grant from technology giant Google to research and develop ways artificial intelligence could be used in digital tools to reduce the risk of heart attacks. Westmead Applied Research Centre will utilise the Google AI Impact challenge grant to create customised digital health tools that enable clinicians and health services to support more people to prevent cardiovascular disease. By combining clinical and consumer-derived data, such as from mobile phone apps and wearables, the program will offer tailored advice using machine learning to assess participants who have been to the hospital with chest pain, harnessing their digital footprint to reduce the risk of a heart attack. Dr. Harry Klimis, a cardiologist and researcher at WARC, explained to HealthcareITNews that modifiable risk factors account for more than 90 percent of the risk of heart attack worldwide. These include abnormal cholesterol, hypertension, smoking, excess alcohol consumption, physical inactivity, diabetes, obesity, psychosocial factors and diet.