Medtronic plc (NYSE: MDT) today announced its AccuRhythm™ AI algorithm technology is the winner of the 7th annual MedTech Breakthrough Awards program as the “Best New Monitoring Solution.”
AccuRhythm AI algorithms now cleared by FDA for the Reveal LINQ ICM; enhancements made to the AF algorithm for the LINQ II ICM DUBLIN, May 16, 2023 /PRNewswire/ -- Medtronic plc (NYSE: MDT) today announced its AccuRhythm™ AI algorithm technology is the winner of the 7th annual MedTech Breakthrough Awards program as the “Best New Monitoring Solution.” AccuRhythm AI is an artificial intelligence algorithm that improves the accuracy of heart rhythm event data from the Medtronic LINQ II™ insertable cardiac monitor (ICM) so physicians can better care for people with abnormal heart rhythms. The Medtronic AI technology was a standout from a field of nearly 4,000 nominations globally. MedTech Breakthrough, an independent market intelligence organization that recognizes the top companies, technologies and products in the global health and medical technology market, conducts the MedTech Breakthrough Awards program. The LINQ family of insertable cardiac monitors (ICMs) provide continuous, long-term monitoring for patients with increased risk of abnormal heart rhythms who experience infrequent symptoms including dizziness, palpitations, syncope (fainting) and chest pain, thereby requiring long-term monitoring or ongoing management. “We are thrilled to win the MedTech Breakthrough Award for a technology that makes such a profound impact on patients and helps clinicians focus on cardiac events that are most relevant,” said Stacey Churchwell, vice president and general manager of the Cardiovascular Diagnostics and Services business within the Cardiac Rhythm Management Operating Unit. “We look forward to building on this AI technology with future enhancements across our LINQ family of ICMs.” Medtronic introduced AccuRhythm™ AI algorithms in 2022 to patients and clinicians in Australia, countries throughout Europe, the United States, and New Zealand to help improve the accuracy of information physicians receive so they can better treat their patients. The two AI algorithms — specific to the most common ICM false alerts, atrial fibrillation (AF) and pause (asystole) 1, 2 — have been shown to reduce the number of false alerts by as much as 84%, saving clinicians approximately 319 hours of clinic review time annually (for every 200 LINQ II patients).3 An enhancement to the AF algorithm for the LINQ II ICM further reduces AF false alerts, which is anticipated to further reduce clinic review time.4 AccuRhythm AI Expanded to Reveal LINQ ICMs “Adding AccuRhythm AI to the thousands of Reveal LINQ ICMs currently implanted in patients globally is important as it will help reduce the data burden on many hospitals and clinics that still regularly use this device for monitoring their patients,” said Alan Cheng, M.D., vice president and chief medical officer for Cardiac Rhythm Management Operating Unit, which is part of the Cardiovascular Portfolio at Medtronic. Medtronic developed the AccuRhythm AI platform and algorithms using its proprietary, diverse and debiased database of more than one million electrocardiogram heart rhythm episodes. Medtronic will continue to apply AccuRhythm AI in the cloud automatically to facilitate seamless care for patients. The AccuRhythm AI algorithms for the Reveal LINQ ICM and updated AF algorithm for LINQ II are anticipated to go live later this year. To learn more about how Medtronic is harnessing the power of AI and its commitment to the ethical use of artificial intelligence in healthcare, visit: https://www.medtronic.com/us-en/our-company/ai-healthcare-technology.html. About Medtronic About MedTech Breakthrough 1 Catherine O`Shea, Melissa E. Middeldorp, Anthony G. Brooks, Jeroen M. Hendriks, Celine Gallagher, Niraj Varma, Rakesh Gopinathannair, Suzanne A. Feigofsky, Dennis H. Lau, Kevin R. Campbell, Prashanthan Sanders. Remote Monitoring of Implantable Loop Recorders: False-positive Alert Episodes. Poster presented at: HRS 2020 Science Online. May 2020. https://cslide-us.ctimeetingtech.com/hrs20/attendee/eposter/poster/71.
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