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New AI Tool Predicts Diabetes Risk In Under Three Seconds

#artificialintelligence

For quite some time now, fat accumulation around the heart has been linked with cardiovascular and metabolic disease. However, until now, there hasn't been a simple way to measure it. A team from the Queen Mary University of London has developed a new artificial intelligence (AI) tool that can automatically quantify these fat deposits from regular MRI scan images. There is a particular collection of fat tissue surrounding the surface of the heart called Pericardial adipose tissue (PAT). High levels of PAT (separate from body mass index and body weight) have been associated with a more significant risk of coronary heart disease and diabetes.


Rev Run’s new campaign

FOX News

Joseph "Reverend Run" and Justine Simmons are a busy couple. On top of hosting "Rev Run's Sunday Suppers" on the Cooking Channel and "Rev Runs Around the World" on the Travel Channel, the hip hop icon and his wife of 26 years must be extra careful to keep their health in check as they're both at risk for diabetes. Rev Run and Simmons are more susceptible to the disease because they are both African Americans over the age of 45 with a family history of type 2 diabetes. Now, they are urging people to visit the AskScreenKnow.com to learn about diabetes risk factors and how to keep the disease at bay. When the husband and wife duo found out they had a high chance of becoming diabetic, they made changes to their lifestyle including becoming more active and cooking with healthier methods.


Younger Type 2 Diabetes Patients More At Risk Of Heart Disease, Stroke

International Business Times

Type 2 diabetes (T2D) is one of the leading causes of death in the U.S. According to statistics by the Centers for Disease Control and Prevention (CDC), around one in every 10 adults over the age of 20, suffer from this condition.


Smart Healthcare in the Age of AI: Recent Advances, Challenges, and Future Prospects

arXiv.org Artificial Intelligence

The significant increase in the number of individuals with chronic ailments (including the elderly and disabled) has dictated an urgent need for an innovative model for healthcare systems. The evolved model will be more personalized and less reliant on traditional brick-and-mortar healthcare institutions such as hospitals, nursing homes, and long-term healthcare centers. The smart healthcare system is a topic of recently growing interest and has become increasingly required due to major developments in modern technologies, especially in artificial intelligence (AI) and machine learning (ML). This paper is aimed to discuss the current state-of-the-art smart healthcare systems highlighting major areas like wearable and smartphone devices for health monitoring, machine learning for disease diagnosis, and the assistive frameworks, including social robots developed for the ambient assisted living environment. Additionally, the paper demonstrates software integration architectures that are very significant to create smart healthcare systems, integrating seamlessly the benefit of data analytics and other tools of AI. The explained developed systems focus on several facets: the contribution of each developed framework, the detailed working procedure, the performance as outcomes, and the comparative merits and limitations. The current research challenges with potential future directions are addressed to highlight the drawbacks of existing systems and the possible methods to introduce novel frameworks, respectively. This review aims at providing comprehensive insights into the recent developments of smart healthcare systems to equip experts to contribute to the field.


Heart Disease Statistics 2016: Rates Down 20 Percent Since 1983, But Remain Number One Cause Of Death

International Business Times

Heart disease, the leading cause of death in the United States, is less prevalent than it once was. Rates of heart disease dropped by 20 percent from 1983 to 2011, researchers at Duke University in Durham, North Carolina found. Researchers analyzed the data from 14,000 patients from five past studies and split them into two groups. The first group contained patients treated from 1983 to 1990, while the second group included patients from 1996 to 2012, with follow-ups ending in 2011. The survey found 20 percent fewer instances of heart disease in the latter group, according to the results published in the Journal of the American Medical Association Tuesday.