At Heart Rhythm 2019, the Heart Rhythm Society’s 40th Annual Scientific Sessions, Preventice Solutions will present clinical data validating its BodyGuardian® Remote Monitoring System with the BeatLogic™ deep learning platform. This technology leverages machine learning and artificial intelligence (AI) for detection of atrial fibrillation (AF) and was validated using clinician adjudicated data. The BodyGuardian® Remote Monitoring System is designed to create a constant connection to monitor cardiovascular data in patients outside the clinic while they go about their daily activities. The data was presented by Hamid Ghanbari, MD, MPH, FACC from University of Michigan in Ann Arbor, and Ben Teplitzky, PhD, and Mike McRoberts, from the Preventice data science team.

“One of the exciting advances in the diagnosis of AF is the use of machine learning techniques and deep learning technology because it can allow physicians to manage the massive amount of data that is collected,” said Dr. Hamid Ghanbari, MD, MPH, a cardiovascular electrophysiologist at the University of Michigan, where he treats patients who have arrhythmias. “Sensor technologies are creating so much data it’s not feasible for physicians to be able to manage and review all of it. With accurate artificial intelligence to identify AF episodes, physicians can focus more on how their patients are feeling and the treatment approach they should take in each case. Artificial intelligence is freeing up the human potential with remote monitoring technologies.”

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