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vascular disease

Mayo Clinic AI algorithm proves effective at spotting early-stage heart disease in routine EKG data


It still remains to be seen whether the sci-fi genre is correct and artificial intelligence will one day rise up against the human race, but in the meantime, AI just might save your life. An algorithm developed by the Mayo Clinic can significantly increase the number of cases of low ejection fraction caught in its earliest stages, when it's still most treatable, according to a study published this month in Nature Medicine. The condition, in which the heart is unable to pump enough blood from its chamber with each contraction, is associated with cardiomyopathy and heart failure and is often symptomless in its early stages. Traditionally, the only way to diagnose low ejection fraction is with the use of an echocardiogram, a time-consuming and expensive cardiac ultrasound. The Mayo Clinic's AI algorithm, however, can screen for low ejection fraction in a standard 12-lead electrocardiogram (EKG) reading, which is a much faster and more readily available tool. In the study, more than 22,600 patients received an EKG as part of their usual primary care checkups, then were randomly assigned to have their results analyzed by the AI or by a physician as usual.

As the world grays, Japan's aging market showcases high-tech senior care

The Japan Times

Six years ago, Atsushi Nakanishi launched Triple W with nothing but the seed of an idea and an overwhelming passion to realize it. Today, the startup is the creator and seller of DFree -- the world's first wearable device for urinary incontinence. The tiny, noninvasive device uses ultrasound to monitor the volume of urine in the user's bladder in real time. When the bladder reaches its threshold, DFree sends an alert to the user's smartphone to tell them it is time to go to the bathroom. Nakanishi credits the ground-breaking product to a eureka moment in 2013.

Yale Study Shows Limitations of Applying Artificial Intelligence to Registry Databases


Artificial intelligence will play a pivotal role in the future of health care, medical experts say, but so far, the industry has been unable to fully leverage this tool. A Yale study has illuminated the limitations of these analytics when applied to traditional medical databases -- suggesting that the key to unlocking their value may be in the way datasets are prepared. Machine learning techniques are well-suited for processing complex, high-dimensional data or identifying nonlinear patterns, which provide researchers and clinicians with a framework to generate new insights. But the study suggests that achieving the potential of artificial intelligence will require improving the data quality of electronic health records (EHR). "Our study found that advanced methods that have revolutionized predictions outside healthcare did not meaningfully improve prediction of mortality in a large national registry. These registries that rely on manually abstracted data within a restricted number of fields may, therefore, not be capturing many patient features that have implications for their outcomes," said Rohan Khera, MD, MS, the first author of the new study published in JAMA Cardiology.

12 Innovations That Will Change Health Care and Medicine in the 2020s


Pocket-size ultrasound devices that cost 50 times less than the machines in hospitals (and connect to your phone). These are just some of the innovations now transforming medicine at a remarkable pace. No one can predict the future, but it can at least be glimpsed in the dozen inventions and concepts below. Like the people behind them, they stand at the vanguard of health care. Neither exhaustive nor exclusive, the list is, rather, representative of the recasting of public health and medical science likely to come in the 2020s.

Abbott's New Coronary Imaging Platform Powered By Artificial Intelligence Launches in Europe


ABBOTT PARK, Ill., April 26, 2021 -- Abbott today announced that its new imaging platform powered by Ultreon 1.0 Software, is now CE Marked in Europe. This first-of-its-kind imaging software merges optical coherence tomography (OCT) – an imaging tool that provides physicians a comprehensive view inside an artery or blood vessel – with the power of artificial intelligence (AI) for enhanced visualization. The new Ultreon Software can automatically detect the severity of calcium-based blockages and measure vessel diameter to enhance the precision of physicians' decision-making during coronary stenting procedures. Unlike traditional imaging methods such as conventional angiography, Abbott's OCT technology uses near-infrared light to provide high-definition, precise imaging from within a blood vessel. OCT imaging also helps improve physicians' assessment of blockages in those vessels and optimize decisions related to stent selection, placement and deployment.

Abbott launches AI-powered coronary OCT imaging system in Europe


To give clinicians a quick, cross-sectional look into potential blockages of the heart's major arteries, Abbott has combined digital imaging technology with artificial intelligence to build an automated system for cardiac procedures. The company's Ultreon software relies on catheters equipped with optical coherence tomography, which uses laser light to scan the interior of a blood vessel and the immediately surrounding tissues to detect calcium and plaque deposits, while also instantly measuring the diameter of an artery. The system--which has now received a CE Mark in Europe--is designed to provide surgeons with prompt information during the placement of coronary stents, faster and more precisely compared to conventional angiography imaging. A previous study by Abbott found having the information from OCT scans readily available led most physicians to change their treatment approach, by selecting the proper stent size and placement location. RELATED: FDA clears PhotoniCare's handheld OCT scanner for checking ear infections After planning a procedure using angiography alone, 88% of operations altered course when surgeons saw high-resolution OCT images and automatic measurements from inside the patient's arteries.

watchOS 7.4 arrives with an easier way to unlock your iPhone


Along with iOS 14.5, Apple has rolled out updates to its other operating systems. One of the key features of the latest iOS firmware is an easier way to unlock your iPhone. As long as you're wearing an Apple Watch running watchOS 7.4, you can unlock your device without the need for a Face ID match or passcode. The idea is to help people access their phones faster when we're still wearing masks much of the time. It'll save you having to pull down your mask to unlock your phone with Face ID. You'll need to turn on the "Unlock with Apple Watch" option in your iPhone's Face ID & Passcode settings to use the feature.

AI caught a hidden problem in one patient's heart. Can it work for others?


Somewhere in Peter Maercklein's heartbeat was an abnormality no one could find. He survived a stroke 15 years ago, but doctors never saw anything alarming on follow-up electrocardiograms. Then, one day last fall, an artificial intelligence algorithm read his EKGs and spotted something else: a ripple in the calm that indicated an elevated risk of atrial fibrillation. Specifically, the algorithm, created by physicians at Mayo Clinic, found Maercklein had an 81.49% probability of experiencing A-fib, a quivering or irregular heartbeat that can lead to heart failure and stroke. Just days later, after Maercklein agreed to participate in a research study, a wearable Holter monitor recorded an episode of A-fib while he was walking on a treadmill.

Are medical AI devices evaluated appropriately?


In just the last two years, artificial intelligence has become embedded in scores of medical devices that offer advice to ER doctors, cardiologists, oncologists, and countless other health care providers. The Food and Drug Administration has approved at least 130 AI-powered medical devices, half of them in the last year alone, and the numbers are certain to surge far higher in the next few years. Several AI devices aim at spotting and alerting doctors to suspected blood clots in the lungs. Some analyze mammograms and ultrasound images for signs of breast cancer, while others examine brain scans for signs of hemorrhage. Cardiac AI devices can now flag a wide range of hidden heart problems.

Artificial Intelligence–Enabled ECG may help Detect Aortic Stenosis, Finds Study


Early detection of aortic stenosis (AS) is becoming increasingly important with a better outcome after aortic valve replacement in asymptomatic severe AS patients and a poor outcome in moderate AS. Therefore, researchers of the Mayo Clinic, USA, developed an AI-ECG using a convolutional neural network to identify patients with moderate to severe AS. It was a retrospective study in which researchers identified 258 607 adults [mean age 63 16.3 years; women 122 790 (48%)] with echocardiography and an ECG performed within 180 days using the Mayo Clinic Unified Data Platform (UDP). The researchers tested the use of an AI-ECG to help identify patients with moderate to severe aortic stenosis (AS). Using echocardiography data, the researchers identified moderate to severe AS in 9723 (3.7%) patients. They performed Artificial intelligence training in 129 788 (50%), validation in 25 893 (10%), and testing in 102 926 (40%) in randomly selected subjects.