The pattern-recognition capabilities of artificial intelligence (AI) deep learning are rapidly advancing potential new ways to detect health risks early. A new study by researchers at Massachusetts General Hospital and the AI in Medicine program at the Brigham and Women's Hospital in Boston demonstrates how an AI deep learning model can predict the 10-year risk of death from stroke or a heart attack from a single chest X-ray. The researchers presented their findings at the recent Radiological Society of North America (RSNA) 2022 annual meeting. Cardiovascular diseases (CVDs) are the leading cause of death worldwide, with an estimated 15.2 million deaths globally due to heart attack and stroke in 2019, according to statistics from the World Health Organization (WHO). Early detection of cardiovascular disease gives the opportunity for potentially life-saving interventions and treatments.

The research is in the final draft stages and has yet to be published, but the findings look promising. At the annual meeting of the Radiological Society of North America, researchers shared preliminary findings on the use of artificial intelligence in predicting the 10-year risk of death from a heart attack or stroke -- all with a single chest X-ray. The researchers used about 150,000 chest X-rays to train AI to recognize risk patterns associated with severe cardiovascular events. They then tested the technology on about 11,000 people and found a "significant association" between real-life cardiovascular events and risks predicted by the AI. "Our approach may help identify individuals at high risk for cardiovascular disease, prompting risk factor assessment and targeted prevention," the report reads.

In this interview, we speak to Dr. Damini Dey from Cedars-Sinai Health System about their latest research that involved using artificial intelligence to predict heart attacks. My name is Dr. Damini Dey. I am a scientist and professor working with quantitative cardiovascular imaging at Cedars-Sinai Health System in Los Angeles. We have been working with artificial intelligence (AI) to improve the prediction of cardiovascular events, such as heart attacks, and efficient and automated measurement of imaging biomarkers. We have been working on this task for a number of years.

Researchers at the University of Leeds have worked with numerous partners to develop an AI system that can use eye scans to detect heart attack risk within the next 12 months. The AI diagnosis is based on the finding that changes in tiny blood vessels in the retina can indicate vascular disease. The AI system, developed under the leadership of the University of Leeds, can detect these changes on retinal scans and identify people who could suffer a heart attack within a year. The accuracy of the prediction system is between 70 and 80 percent, according to the researchers. It is therefore well suited as a recommendation mechanism for in-depth cardiovascular screening.

Artificial intelligence has been used for the first time to instantly and accurately measure blood flow, in a study we part-funded. The results were found to be able to predict chances of death, heart attack and stroke, and can be used by doctors to help recommend treatments which could improve a patient's blood flow. Reduced blood flow, which is often treatable, is a common symptom of many heart conditions. International guidelines therefore recommend a number of assessments to measure a patient's blood flow, but many are invasive and carry a risk. Non-invasive blood flow assessments are available, including Cardiovascular Magnetic Resonance (CMR) imaging, but up until now, the scan images have been incredibly difficult to analyse in a manner precise enough to deliver a prognosis or recommend treatment.

Machine learning, a branch of artificial intelligence, has become more accurate than human medical professionals in predicting incidence of heart attack or death in patients at risk of coronary artery disease. Machine learning, a branch of artificial intelligence, was more accurate than human medical professionals in predicting myocardial infarction (MI) or death among patients suspected of having coronary artery disease (CAD), according to an abstract presented at the 2019 International Conference on Nuclear Cardiology and Cardiac CT, held May 12-14 in Lisbon, Portugal. Physicians routinely make treatment decisions using risk scores, which are based on few variables and are typically only moderately accurate for individual patients. Machine learning can use repetition and adjustment to exploit large quantities of data and identify complex patterns that may go unnoticed by humans. "Humans have a very hard time thinking further than three dimensions (a cube) or four dimensions (a cube through time)," said the study's lead researcher, Luis Eduardo Juarez-Orozco, MD, PhD, in a statement.

Every year an estimated 20 million people die from heart disease, but now, hot on the heels of an artificial intelligence (AI) system that can predict death, a team of researchers from the University of Nottingham, the same university that's found a way to regrow teeth from stem cells, have developed a machine learning algorithm that can predict an individuals likelihood of having a heart attack, or a stroke, better than any doctor. Over the past few decades the American College of Cardiology and the American Heart Association (ACC-AHA) has developed a series of guidelines to help doctors evaluate a patient's cardiovascular risk, based on eight factors that include age, cholesterol level and blood pressure. You might think that's already good enough – but Stephen Weng and his team wanted to make it even better so they built four computer learning algorithms and fed them data from over 380,000 patients. Firstly, the new system used 295,000 records to build its internal predictive models, and then it used the remaining 85,000 records to test and refine them, and the result? After all, you don't want to be told you're likely to have a heart attack if you aren't – doctors aren't beasts you know… Translating all of that into normal language what this all means is that out of the 85,000 records it analysed the new model could have saved 355 lives, but interestingly the AI system identified a number of risk factors and predictors not covered in the existing guidelines, like severe mental illness and the consumption of oral corticosteroids.

Google's AI can now predict the risk of heart disease by looking at the patient's eye. Researchers from Alphabet (Google's parent company) subsidiary Verily discovered this new method by deploying machine learning. The research was published earlier today in the journal Nature, titled "Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning". By analysing scans of the back of a patient's eye, Verily software can predict their risk of heart disease. "With medical images, observing and quantifying associations can often be difficult because of the wide variety of features, patterns, colors, values, and shapes that are present in real data. Here, we show that deep learning can extract new knowledge from retinal fundus images," says the paper.

Welcome to Digital Health Briefing, a new email providing the latest news, data, and insight on how digital technology is disrupting the healthcare ecosystem, produced by Business Insider Intelligence. Sign up and receive Digital Health Briefing free to your inbox. We'd like to hear from you. GOOGLE'S AI HELPS TO PREDICT CARDIOVASCULAR CONDITIONS BY ANALYZING EYE SCANS: Google and Verily -- Google-parent Alphabet's life sciences unit -- are exploring new ways that artificial intelligence (AI) can be used to identify patients' risk of suffering from a cardiovascular event, such as a heart attack or stroke, according to a newly published study by the two Alphabet subsidiaries. The report describes how Google's AI analyzes noninvasive retinal fundus scans -- scans of the back of the eye -- to deduce the individual's age, sex, blood pressure, and whether they're a smoker.

Richard Adler knows a few things about the uneasy relationship between older adults and technology. It's something that Adler, a distinguished research fellow at the Institute for the Future in Palo Alto, Calif., has been studying for more than 25 years. One thing, in particular, has struck him. "The truth is that a lot of older adults are technophobes," he said. "They tend to be classically late adopters of almost any technology."