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Researchers and colleagues at Cedars-Sinai have created an artificial intelligence (AI) tool that employs computed tomography (CT) scans to detect patients who are at risk of decreased blood flow to the heart. The tool can correctly predict decreased blood flow within the coronary arteries as well as within the heart muscle. The benefit of this AI tool is that it could possibly be used in real-time when patients come in for a CT scan to help doctors establish the subsequent step in the treatment strategy. Coronary arteries blockages usually happen because of the accumulation of fatty plaques. This may limit blood flow to the heart, resulting in chest pain, heart attacks, or even death.

"These two heart conditions are challenging for even expert cardiologists to accurately identify, and so patients often go on for years to decades before receiving a correct diagnosis," said David Ouyang, MD, a cardiologist in the Smidt Heart Institute and senior author of the study. "Our AI algorithm can pinpoint disease patterns that can't be seen by the naked eye, and then use these patterns to predict the right diagnosis." The two-step, novel algorithm was used on over 34,000 cardiac ultrasound videos from Cedars-Sinai and Stanford Healthcare's echocardiography laboratories. When applied to these clinical images, the algorithm identified specific features - related to the thickness of heart walls and the size of heart chambers - to efficiently flag certain patients as suspicious for having the potentially unrecognized cardiac diseases. "The algorithm identified high-risk patients with more accuracy than the well-trained eye of a clinical expert," said Ouyang.

Physician-scientists in the Smidt Heart Institute at Cedars-Sinai have created an artificial intelligence (AI) tool that can effectively identify and distinguish between two life-threatening heart conditions that are often easy to miss: hypertrophic cardiomyopathy and cardiac amyloidosis. The new findings were published in JAMA Cardiology. "These two heart conditions are challenging for even expert cardiologists to accurately identify, and so patients often go on for years to decades before receiving a correct diagnosis," said David Ouyang, MD, a cardiologist in the Smidt Heart Institute and senior author of the study. "Our AI algorithm can pinpoint disease patterns that can't be seen by the naked eye, and then use these patterns to predict the right diagnosis." The two-step, novel algorithm was used on over 34,000 cardiac ultrasound videos from Cedars-Sinai and Stanford Healthcare's echocardiography laboratories.

Researchers have demonstrated that artificial intelligence can lead to better predictions of the onset and course of cardiovascular diseases, allowing physicians to prevent, treat or foresee serious heart problems even before the patient becomes aware of any underlying conditions. Although the focus of the study was for cardiovascular diseases, according to the researchers, the findings could pave the way for a new era of personalised, preventive medicine, allowing doctors to proactively alert patients on potential ailments and suggest treatments to alleviate the problems, before the onset of serious conditions. Martin Tristani-Firouzi, corresponding author of the study says, "We can turn to AI to help refine the risk for virtually every medical diagnosis. The risk of cancer, the risk of thyroid surgery, the risk of diabetes -- any medical term you can imagine." The current approaches for calculating the combined effects of various risk factors, including demographics and medical history on cardiovascular diseases are imperfect and subjective, with the methods failing to identify certain interactions that can have a profound effect on the health of the heart and the blood vessels.

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.

Artificial intelligence has been embraced for its ability to offer insight from big data. By applying the technology to genetics, a research team led by Queen Mary University of London has found clues that they say could aid the development of new drugs for heart failure and identify people at risk of the disease. Based on an AI analysis of heart MRI images from 17,000 volunteers in UK Biobank, the researchers linked genetic factors to 22% to 39% of abnormalities in the size and function of the heart's left ventricle, which pumps blood into the aorta. They published the findings in the journal Circulation. The team identified or confirmed 14 regions in the human genome that play a part in determining the size and function of the left ventricle, because they contain genes that regulate the early development of heart chambers and the contraction of heart muscle.

Dr. Partho Sengupta had a hunch. A leading cardiologist now practicing at the West Virginia University Heart and Vascular Institute, Sengupta wanted to know whether the emerging field of machine learning could help solve a problem that had long vexed heart doctors. Driven by his conviction and curiosity, Sengupta cold-called data scientists at Saffron, a pioneering artificial intelligence company in North Carolina's Research Triangle acquired by Intel in 20151, with an idea for a novel experiment. Several phone calls and one proof of concept later, Sengupta and Saffron were able to show that a particular type of machine learning can be a powerful--even lifesaving--aid to cardiologists. The groundbreaking work also holds promise for delivering on the triple aims of healthcare reform: lowering costs, elevating quality of care, and improving access. The idea for the experiment had its genesis in Sengupta's office, where, like every other cardiologist, Sengupta struggled to diagnose between two very different diseases with dangerously similar symptoms.

On a recent overnight shift in the emergency room, a woman who was having vague abdominal pain and chest discomfort for several days was referred to me. When her symptoms began, after searching google, she came up with a diagnosis list that included everything from influenza, to Zika, to lupus. She came to the hospital several days later when it became hard to breath and it turned out that she had a massive heart attack. Cardiac pain originates from the heart muscle, most typically when blood flow to the heart (through vessels called coronary arteries) become blocked. In the heart muscle, there are nerve endings which transmit signals to the brain which get interpreted as chest pain. Unfortunately, just like other pain arising in other organs in the body, cardiac pain is poorly localized.

WASHINGTON – Scientists are developing a robotic sleeve that can encase a flabby diseased heart and gently squeeze to keep it pumping. So far it has been tested only in animals, improving blood flow in pigs. But this "soft robotic" device mimics the natural movements of a beating heart, a strategy for next-generation treatments of deadly heart failure. The key: A team from Harvard University and Boston Children's Hospital wound artificial muscles into the thin silicone sleeve, so that it alternately compresses, twists and relaxes in synchrony with the heart tissue underneath. It's a dramatically different approach than today's therapies and, if it eventually is proven in people, it might offer a new alternative to heart transplants or maybe even aid in recovery.