You may be twice as likely to develop late-life memory and cognitive issues if you are overweight or have high blood pressure or high glucose levels in your 20s/30s. They found that high BMI and blood pressure in early adulthood can double the rate of cognitive decline -- while high blood glucose levels increased in five-fold. The researchers cautioned, however, that they only established an association between these health issues and late-life cognitive problems, not a causal link. 'These results are striking and suggest that early adulthood may be a critical time for the relationship between these health issues and late-life cognitive skills,' said paper author and neurologist Kristine Yaffe of the University of California, San Francisco. 'It's possible that treating or modifying these health issues in early adulthood could prevent or reduce problems with thinking skills in later life.'

Artificial intelligence is constantly being used in new and different applications in healthcare. A research team from the University of Gothenburg, is now using the power of AI in combination with conventional statistical methods in a study of risk factors in type 1 diabetes. The study's objective was to identify the most important indicators of elevated risk for cardiovascular disease and death. "What's unique about this study is that we've included machine learning analyses - that is, algorithms for AI - to assess strength of association for cardiovascular risk factors," Aidin Rawshani, PhD, of Sahlgrenska Academy, University of Gothenburg, said in a release. Dr. Rawshani is the corresponding author of a new article in the journal Circulation.

A new algorithm developed by Google and its sibling-company Verily Life Sciences can assess a person's risk of cardiovascular diseases. The AI scans images of a person's retina and accurately predicts the risk of major cardiac events such as heart attack or stroke. The robotic AI uses standard retinal scans to analyze the blood vessels in the retina. The study led by researchers from Google, Verily Life Sciences, and the Stanford School of Medicine used deep-learning algorithms to extract new knowledge from retinal images. The AI scanned the retinal images and identified cardiovascular risk factors based on age, gender, blood pressure, and smoking status, among others.

A retinal fundus image is a photograph of the back of the eye taken through the pupil. For more than 100 years these images have been used for detecting eye disease. Now Google has introduced a surprising new use for retinal images: combined with artificial intelligence, they can also predict a patient's risk of heart attack or stoke. Research arm Google Brain today published a paper in the journal Nature Biomedical Engineering which demonstrates how deep learning models can use retinal images to detect a patient's age, gender, smoking status and systolic blood pressure; calculate cardiovascular risk factors; and predict the risk of major adverse cardiac events occurring over the next five years. A problem with today's mainstream cardiovascular risk calculators such as the Pooled Cohort Equations, Framingham, and Systematic Coronary Risk Evaluation is that they require the input of multiple features such as blood pressure, body mass index, glucose and cholesterol levels, etc. to generate a disease risk result. A study by the American College of Cardiology's Practice Innovation And Clinical Excellence Program concluded that the data required to calculate 10-year risk was available for less than 30% of patients.

Have you ever wondered why doctors examine your eyes even when the ailment is seemingly somewhere else? One's eyes, or rather the retina, doctors say, can reveal a lot about your general well being. It turns out what doctors can, Google can do better. On Tuesday, Google published an official blog saying its deep learning algorithm can accurately predict a person's cardiovascular health by evaluating retinal images. In other words, Google's AI can tell if your heart is in a good state. The discovery was put forward by scientists Ryan Poplin, Avinash V. Varadarajan, Katy Blumer, Yun Liu, Michael V. McConnell, and Greg S. Corrado in a research paper titled'Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning', published in Nature Biomedical Engineering.

Researchers from Google discovered a deep learning algorithm that can accurately predict cardiovascular risk factors based on images of a patient's eyes, according to a Monday Google Research Blog post. Heart disease and stroke are the world's largest causes of death, accounting for more than half of all deaths worldwide in 2015, according to the World Health Organization. These diseases have remained the leading causes of death globally for the last 15 years, the organization noted. Using deep learning technology to aid in diagnosis could help scientists create more targeted hypotheses, and drive a wide range of future research on these and other conditions, Google noted. For doctors, assessing a patient's risk for cardiovascular disease is a critical first step toward reducing the likelihood that the patient suffers a cardiovascular event in the future, Lily Peng, Google Brain Team's product manager, wrote in the post.

These authors contributed equally: Ryan Poplin, Avinash V. Varadarajan, Lily Peng and Dale R. Webster. The authors are employees of Google and Verily Life Sciences.