It would be easy to wonder what Zachi Attia is doing in the cardiac operating rooms of one of America's most prestigious hospitals. He has no formal medical training or surgical expertise. The first time he watched a live procedure, he worried he might faint. But at Mayo Clinic, the 33-year-old machine learning engineer has become a central figure in one of the nation's most ambitious efforts to revamp heart disease treatment using artificial intelligence. Working side by side with physicians, he has built algorithms that in studies have shown a remarkable ability to unmask heart abnormalities long before patients begin experiencing symptoms.
There are few clinical areas more in need of innovation than heart disease. It's the leading killer among men and women in the US, accounting for one in every four deaths by the CDC's numbers, and yet it's also a body of health conditions that could be substantially mitigated through a number of lifestyle and behavior changes. So of course it's little surprise that cardiovascular health and care has long been in the crosshairs of digital health, whether it be in the form of fitness trackers, tele-rehabilitation and coaching programs, mobile ECG hardware, disease detection algorithms or any number of other novel approaches. Much like any other healthcare tools though, these novel approaches all need to win the approval of the clinical community before they'll begin to see meaningful use at scale -- and what better time to do so than one of the year's largest gatherings of cardiovascular clinicians, researchers, educators, program managers and vendors? MobiHealthNews was on-site last weekend at the American Heart Association Scientific Sessions 2019 in Philadelphia to check out posters, oral presentations and news from this year's show.
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.
Today, with a software update, Apple switched on two highly anticipated features of its popular wearable: The first uses optical sensors to detect irregular heart rhythms on Apple Watch Series 1 and later iterations. The second enables wearers of Apple Watch Series 4 to record an electrocardiogram, or ECG, directly from their wrist. The features are the most ambitious to date in Apple's growing suite of health-monitoring tools--but they are noteworthy also for producing a palpable tension in the healthcare community. Some experts say the Apple Watch's arrhythmia notifications and ECG have enormous potential to benefit public health. But those same experts also express caution and concern.