AI transforms ECG signals from smartwatch into a diagnostic tool for heart failure

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A study published in Natural medicine reports on the ability of a smartwatch ECG to accurately detect heart failure in non-clinical settings. Mayo Clinic researchers applied artificial intelligence (AI) to Apple Watch ECG recordings to identify patients with a weak heartbeat. Study participants recorded ECGs from their smartwatch remotely whenever they wanted, from anywhere. Periodically, they uploaded the ECGs to their electronic medical records automatically and securely through a smartphone app developed by the Mayo Clinic Digital Health Center.

“Currently, we diagnose ventricular dysfunction (a weak heart pump) through an echocardiogram, CT scan or MRI, but these are expensive, time consuming and sometimes inaccessible. The ability to diagnose a heart pump remotely weak, from an ECG that a person records using a consumer device such as a smart watch, enables timely identification of this life-threatening disease on a mass scale,” says Paul Friedman, MD, chair of the Department of Cardiovascular Medicine at Mayo Clinic in Rochester. Dr. Friedman is the lead author of the study.

People with a weak heart pump may not have symptoms, but this common form of heart disease affects about 2% of the population and 9% of people over the age of 60. When the heart cannot pump enough oxygen-rich blood, symptoms can develop, including shortness of breath, fast heart rate, and swelling in the legs. Early diagnosis is important because, once identified, there are numerous treatments to improve quality of life and decrease the risks of heart failure and death.

Mayo researchers interpreted single-lead ECGs from the Apple Watch by modifying an earlier algorithm developed for 12-lead ECGs that has been shown to detect weak heart pumping. The 12-lead algorithm for low ventricular ejection fraction is licensed to Anumana Inc., an AI-powered healthcare technology company co-created by nference and Mayo Clinic.

While the data is early, the modified AI algorithm using single-lead ECG data had an area under the curve of 0.88 for detecting weak cardiac pumping. By comparison, this measure of accuracy is as good as or slightly better than a treadmill diagnostic medical test.

“These data are encouraging because they show that digital tools enable convenient, inexpensive, and scalable screening for important conditions. Through technology, we can remotely collect useful information about a patient’s heart in an accessible way that can meet the needs people’s needs where they are,” says Zachi Attia, Ph.D., AI senior scientist in Mayo Clinic’s Department of Cardiovascular Medicine. Dr. Attia is the first author of the study.

“Developing the ability to ingest data from portable consumer electronic devices and provide analytics capabilities to prevent disease or improve health remotely in the manner demonstrated by this study can revolutionize healthcare. Solutions like this enable more than just prediction and prevention of problems, but also eventually help decrease health disparities and the burden on health systems and physicians,” says Bradley Leibovich, MD, medical director of Mayo Clinic’s Center for Digital Health and co-author of the study.

The 2,454 study participants were Mayo Clinic patients from across the US and 11 countries. They downloaded an app created by the Mayo Clinic Digital Health Center to securely upload their ECGs from Apple Watch to their electronic health records. Participants recorded more than 125,000 previous and new Apple Watch ECGs in their electronic health records between August 2021 and February 2022. Clinicians had access to view all ECG data on an AI dashboard integrated into the record. electronic health, including the day and time it was made. Recorded.

Approximately 420 participants underwent an echocardiogram, a standard test that uses sound waves to produce images of the heart, within 30 days of recording an Apple Watch ECG in the app. Of these, 16 patients had a low ejection fraction confirmed by echocardiography, which provided an accuracy comparison.

This study was funded by the Mayo Clinic without technical or financial support from Apple. Drs. Attia and Friedman, along with others, are co-inventors of the low ejection fraction algorithm licensed to Anumana and can benefit from its commercialization.

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Materials provided by mayo clinic. Originally written by Terri Malloy. Note: Content can be edited for style and length.

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