Investigators from the Mayo Clinic and AliveCor demonstrated that a trained artificial intelligence network can help identify people at increased risk of arrhythmias and sudden cardiac death despite displaying a normal heart rhythm on their electrocardiogram.
Up to half of patients with long QT syndrome can show a normal interval on a standard test, the personal EKG manufacturer AliveCor said in a statement. Correct diagnoses and treatment can be crucial, especially when using drugs that may prolong heartbeats.
The researchers’ deep neural network generated the results using data from a single lead of a 12-lead EKG—measuring the voltage between the left and right arms—suggesting that simpler, portable devices may be used to detect the concealed heart condition, the company said. The network had an overall accuracy of 79%, with 73% sensitivity and 81% specificity.
“There can be no better illustration of the importance of our AI to medical science than using it to detect that which is otherwise invisible,” said AliveCor CEO Vic Gundotra. A study abstract was unveiled at the Heart Rhythm Scientific Sessions conference in Boston.
Earlier this year, the company demonstrated an algorithm that can identify people with high potassium levels using EKG data, also through its collaboration with the Mayo Clinic.
The inherited form of long QT syndrome affects 160,000 people in the U.S., causing 3,000 to 4,000 deaths in children and young adults annually. LQTS can also be caused by nearly 100 FDA-approved medications, including certain antibiotics and antidepressants, the company said.
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AliveCor received the first FDA clearance for an Apple Watch accessory with its KardiaBand late last year, which replaces the original watch band with a medical device that takes EKG readings to detect normal sinus heart rhythms and atrial fibrillation. The company had previously sold a separate, credit card-sized device, KardiaMobile, which can be attached to the back of a smartphone or tablet.
The company’s collaboration with the Mayo Clinic was formed in October 2016, with efforts in AI detection of LQTS announced in July of last year.