HeartBeam, Inc. (NASDAQ: BEAT) had a big surge in shares after important research data was presented at a major event. HeartBeam shares closed at $2.53, up 22.22% as the firm attracted a lot of attention and investor trust with its advances in AI technology, particularly in the area of atrial flutter detection.
Cutting-Edge AI Beats Cardiologists
HeartBeam (BEAT) released study results showing that their AI for atrial flutter, when paired with vectorcardiography (VCG), outperformed the expertise of a panel of cardiologists in this regard. This sophisticated deep learning technique is intended to more accurately identify anomalies in heartbeats. Dr. Joshua M. Lampert, an assistant professor of medicine and cardiac electrophysiologist, presented the research at the Heart Rhythm Society annual meeting in Boston.
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In the study, HeartBeam AI was utilized to analyze 173 VCGs, single-lead ECGs, and 12-lead ECGs to detect atrial flutter. A panel of three electrophysiologists (EP panel) also reviewed the same set of ECGs for comparison. Notably, the AI, when combined with VCG, showed a 40% improvement in identifying atrial flutter cases over the expert panel analyzing single-lead ECGs. Additionally, it achieved a 6% enhancement compared to the panel’s review of 12-lead ECGs, with zero variability in detection outcomes, highlighting its consistency and reliability.
Future Prospects And Technological Advancements
The study underscored that the deep learning algorithm applied to VCGs performed on par with the gold-standard 12-lead ECG, surpassing the electrophysiologists’ ability to differentiate atrial flutter from sinus rhythm. This consistency and agreement across multiple AI model predictions contrasted with significant variability among human reviewers, particularly in single-lead ECG analysis.
HeartBeam’s first anticipated application of their 3D VECG platform technology is the HeartBeam AIMIGo, a compact, credit card-sized device designed for patient use at home or elsewhere. Currently under review by the FDA, this device leverages AI to analyze comprehensive cardiac signals, aiming to enhance diagnostic accuracy and enable a more personalized approach to cardiac care.
