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In the study, HeartBeam AI with VCG demonstrated a 28% improvement over single-lead ECG in the detection of atrialflutter cases (sensitivity of 91.0% for single-lead ECG) without sacrificing the ability to identify those individuals without atrialflutter (specificity of 98.7% for VCG vs. 71.2% for VCG vs. 96.9%
The representation of ECG vectors in X, Y, and Z axes in a vectorcardiogram (VCG) has shown diagnostic promise beyond single lead ECG (SL) analysis, with implications for novel ECG acquisition technologies. It is unclear how deep learning applied to VCG compares to physicians (EPs) for atrialflutter (AFL) detection.
In a world where technology reigns supreme, one of the most profound tools in medicine remains the irreplaceable electrocardiogram (ECG). As technology advances, so too does our ability to capture the heart’s rhythm with greater precision. AFIB/AFL – atrial fibrillation or atrialflutter episodes.
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