A nurse takes an electrocardiogram of a patient. Sergei Bobylev | TASS | Getty Images

It was a typical Saturday morning. Angela Watschke was shuffling some of her kids around four years ago while the others were at home. Her then-2-year-old daughter, Abrielle, was lying on the bed when she started slipping off as her eyes rolled back. The toddler was experiencing cardiac arrest. One of Watschke's daughters texted her asking for help. The color faded from Abrielle as her father performed CPR and called 911. When Watschke arrived home, she found her street filled with ambulances and paramedics. She and her husband clung to each other until finally, someone said Abrielle was breathing on her own. The hospital wasn't sure what to do with the toddler. They sent Abrielle to another emergency room nearby, where doctors informed Watschke her daughter had a heart condition called long QT syndrome. "I had no idea a normal, healthy 2-year-old in literally one moment to the next could go from perfectly fine to heart stop," said Watschke, who lives near Minneapolis. Named for the abnormal electrical wave patterns that characterize it, LQTS causes dangerously fast irregular heartbeats in response to stress and exercise, according to the National Institutes of Health. Many people don't know they have it until it causes them to faint or have seizures. Artificial intelligence could change that. AliveCor, a medical device start-up, and Mayo Clinic used artificial intelligence to identify LQTS in patients whose EKG results appear normal. Their findings from a study, published in an abstract Thursday at the Heart Rhythm Scientific Sessions conference, found the technology accurately diagnoses the genetic condition 79 percent of the time.

It could one day help doctors diagnose the condition earlier and more accurately than they currently can. It could also help consumers access tests more easily than they can now. "I will submit that when the QT interval is caught in our patients early. This will be a life-saving modifier that we will have come upon," said Dr. Michael Ackerman, Abrielle's doctor and director of Mayo Clinic's Genetic Heart Rhythm Clinic and the Windland Smith Rice Sudden Death Genomics Laboratory.

Signs of LQTS can appear in an electrocardiogram, but they're not always apparent and physicians don't always recognize the syndrome. About 1 in 7,000 people are estimated to have LQTS, but no one knows for sure because it usually goes undiagnosed, according to the NIH. A paper published in 2009 says it could be even more common at 1 in 2,000 live births. The condition causes about 3,000 to 4,000 sudden deaths in children and young adults in the U.S. every year, according to the NIH. LTQS can either be inherited or acquired. Neither of Abrielle's parents carried the condition. One of her genes morphed when she was developing, called a de novo mutation, causing her to be born with it. AliveCor built a deep neural network using EKG results from more than 1,000 patients with congenital LQTS and more than 1,000 patients without it. The system identified relevant features and continued to learn from the data. It detected the condition in people where the length of electrical waves measured in an EKG were indistinguishable from normal ones — picking up on signals doctors hadn't seen. This could help diagnose people earlier and prevent sudden deaths, Ackerman said. To conduct the study, researchers used a traditional EKG machine but used only one lead, or sensor, instead of the usual 12. That way, they could see whether this type of testing could be added to AliveCor's EKG devices that use one lead. Its Kardia Mobile sensor attaches to a smartphone and takes readings when users put their fingers on the pads. AliveCor also offers an Apple Watch band that doubles as a sensor.

LTQS is often the culprit in stories of teen athletes suddenly collapsing and dying during a game. AliveCor CEO Vic Gundotra said he dreams that one day every coach in every high school would have a device that could perform a 30-second EKG on student athletes and screen them for LQTS. He said it could also be used in hospitals to try to prevent some cases of sudden infant death syndrome. Pharmacists could also one day use it to avoid giving people drugs that may cause them to develop the acquired form of the condition. "There are so many opportunities if the science is right and we go through the correct regulatory pathways. The applicability of this technology in schools, in homes, in hospitals, in pharmacies, is kind of extraordinary," said Gundotra, a former executive at Google and Microsoft. Abrielle's life would have been different if she had been diagnosed with LQTS before she went into cardiac arrest. She could've been treated with medicine instead of having her ribs cracked open and undergoing open heart surgery to have a defibrillator implanted in her. "It's hard to even put into words how traumatic that was and all that she experienced in (three weeks) in the hospital," Watschke said. "Everything from the repeated shock, the needles, the pokes, the prodding, the barrage of medications. Boy, it's hard to even think about when thinking back and it's hard to talk about her being in the hospital." Mayo Clinic invested an undisclosed amount in AliveCor and partnered with the start-up in 2016 to identify hidden health signals displayed in EKGs. The pair started with looking for abnormal potassium levels and announced last summer they would work together to detect LQTS. The findings shared Thursday are early signals that AI could help diagnose LQTS, Gundotra said, but it could take years before the technology is introduced in the market. More studies will be required, and any new technology would need to receive approval from the Food and Drug Administration.

Abrielle Watschke went into cardiac arrest four years ago and was clinically dead for 10 minutes before spending three weeks in the hospital. She's now thriving, but she could have been spared the trauma had she been diagnosed with Long QT Syndrome before the incident. Source: Angela Watschke