CathVision, a medical technology company developing innovative electrophysiology solutions to enhance clinical decision-making in the EP lab, announced the investigation of the Signal Complexity™ algorithm designed to visualize and quantify atrial fibrillation (AF) complexity parameters in patients with persistent AF. Ten patients have been treated to date at NYU Langone Health, one of the nation’s premier academic medical centers.
CathVision’s CARDIALYTICS suite of artificial-intelligence powered analytics, currently in development, will provide automated analysis designed to improve ablation outcomes utilizing high-quality signal data from the ECGenius System.1 The NYU Langone study evaluating the Signal Complexity analytic tool is the second study initiated to assess CARDIALYTICS algorithms. Last year the PVISION study evaluated the PVI Analyzer™ algorithm developed to automate the assessment of pulmonary vein isolation.
Signal Complexity algorithm designed to visualize and quantify atrial fibrillation (AF) complexity parameters
“The ECGenius System acquires high-fidelity, low-noise cardiac signals. It’s the quality of the signal that is the foundation of the algorithms creating the opportunity for physicians to determine intricate, personalized ablation strategies with higher confidence,” said Mads Matthiesen, CEO of CathVision. “We developed the CARDIALYTICS suite of analytic tools, based on AI algorithms, to further the benefits of the ECGenius System and to create a responsive solution to the challenges physicians face in treating the complex AF patient population. Better quality signals aligned with automated, intelligent analysis – this is the future of ablation therapy.”
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The Signal Complexity study is the fourth clinical study conducted by CathVision in the past 18 months. CathVision invests significantly in research and development as part of the company’s commitment to driving innovation in an area that has remained stagnant for decades. Clean, clear signals obtained through advanced EP recording technology can improve the accuracy and effectiveness of cardiac ablation procedures potentially lessening the economic burden of atrial fibrillation and improving patient well-being.
“Current AF treatment requires multiple procedural ablation steps. We are at a time when advanced signal analyses, AI algorithms, and high signal quality combined can deliver valuable visualization allowing the opportunity to analyze progress throughout the ablation procedure,” said Dr. Larry Chinitz, the Alvin Benjamin and Kenneth Coyle, Sr. Family Professor of Medicine and Cardiac Electrophysiology, and Director, Cardiac Electrophysiology and Heart Rhythm Center in the Department of Medicine, Leon H. Charney Division of Cardiology at NYU Langone. “The results achieved after applying the Signal Complexity algorithm to our case data support this notion, so we’re excited to see what happens in the future for the treatment of AF.”