HeartBeam presents results on AI capabilities for detecting arrhythmias

HeartBeam announced new data demonstrating that applying the company’s artificial intelligence, AI, algorithms to vectorcardiography showed considerably improved performance in the detection of atrial flutter over single-lead electrocardiograms and similar performance to 12-lead ECGs, the standard for diagnosing atrial flutter. This marks the first scientific presentation on the company’s deep learning algorithm, HeartBeam AI. The data was presented by Vivek Reddy, MD, Director of Cardiac Arrhythmia Services at The Mount Sinai Hospital, during the European Heart Rhythm Association conference in Berlin, Germany. In the study, HeartBeam AI with VCG demonstrated a 28% improvement over single-lead ECG in the detection of atrial flutter cases without sacrificing the ability to identify those individuals without atrial flutter. “The study presented at the EHRA conference shows that HeartBeam AI combined with VCG delivers equivalent performance to a 12-lead ECG and greatly improves detection of atrial flutter over a single-lead ECG, underscoring the limitations of current wearable technologies. This presents an opportunity for a VCG-based algorithm that offers arrhythmia detection capabilities beyond what is available today and to fill gaps in healthcare inequality when obtaining a 12-lead ECG is challenging,” said Dr. Reddy.