The Lead Episode 35: A Novel ECG-Based Deep Learning Algorithm to Predict Cardiomyopathy in Patients With Premature Ventricular Complexes-The Lead Speaker Information Episode 35

The Lead Speaker Information Episode 35

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This discussion focuses on a recent article titled "A Novel ECG-Based Deep Learning Algorithm to Predict Cardiomyopathy in Patients With Premature Ventricular Complexes." The authors of the article are Joshua Lampert, MD, Akhil Vaid, MD, William Whang, MD, Jacob Koruth, MBBS, Marc A. Miller, MD, Marie-Noelle Langan, MD, Daniel Musikantow, MD, Mohit Turagam, MD, Abhishek Maan, MD, Iwanari Kawamura, MD, Srinivas Dukkipati, MD, Girish N. Nadkarni, MD, and Vivek Y. Reddy, MD. The article was published in the Journal of the American College of Cardiology: Clinical Electrophysiology.<br /><br />The discussion is moderated by Jason T. Jacobson, MD, FHRS from Westchester Medical Center-New York Medical College. The two contributors for this discussion are Jagmeet P. Singh, MD, PhD, FHRS from Massachusetts General Hospital and Daniel Frenkel, MD, FHRS from Westchester Medical Center.<br /><br />The article presents a deep learning algorithm that utilizes electrocardiogram (ECG) data to predict the presence of cardiomyopathy in patients with premature ventricular complexes (PVCs). The authors discuss the methodology of the algorithm and the results of their study. They found that the algorithm had a high accuracy in identifying patients with cardiomyopathy based on ECG data alone.<br /><br />During the discussion, the contributors highlight the potential importance of this algorithm in clinical practice. They discuss the challenges and limitations of using deep learning algorithms in the field of cardiology and the need for further validation and refinement of the algorithm.<br /><br />The moderators and contributors also disclose their financial relationships and potential conflicts of interest related to the topic of discussion.<br /><br />In conclusion, this discussion revolves around a novel deep learning algorithm that predicts cardiomyopathy using ECG data in patients with PVCs. The algorithm shows promising results, but further research is needed to validate its accuracy and potential clinical utility.

Keywords

ECG-based deep learning algorithm

cardiomyopathy prediction

premature ventricular complexes

Joshua Lampert

Akhil Vaid

William Whang

Jacob Koruth

Marc A. Miller

Marie-Noelle Langan

Daniel Musikantow