• Computer Science > Computer Vision and Pattern Recognition [Submitted on 4 Feb 2026] Title:VISION-ICE: Video-based Interpretation and Spatial Identification of Arrhythmia Origins via Neural Networks in Intracardiac Echocardiography View PDF HTML (experimental)Abstract:Contemporary high-density mapping techniques and preoperative CT/MRI remain time and resource intensive in localizing arrhythmias. • AI has been validated as a clinical decision aid in providing accurate, rapid real-time analysis of echocardiographic images. • Building on this, we propose an AI-enabled framework that leverages intracardiac echocardiography (ICE), a routine part of electrophysiology procedures, to guide clinicians toward areas of arrhythmogenesis and potentially reduce procedural time. • Arrhythmia source localization is formulated as a three-class classification task, distinguishing normal sinus rhythm, left-sided, and right-sided arrhythmias, based on ICE video data. • We developed a 3D Convolutional Neural Network trained to discriminate among the three aforementioned classes. • In ten-fold cross-validation, the model achieved a mean accuracy of 66.2% when evaluated on four previously unseen patients (substantially outperforming the 33.3% random baseline).

Article Summaries:

  • Computer Science > Computer Vision and Pattern Recognition [Submitted on 4 Feb 2026] Title:VISION-ICE: Video-based Interpretation and Spatial Identification of Arrhythmia Origins via Neural Networks in Intracardiac Echocardiography View PDF HTML (experimental)Abstract:Contemporary high-density mapping techniques and preoperative CT/MRI remain time and resource intensive in localizing arrhythmias. AI has been validated as a clinical decision aid in providing accurate, rapid real-time analysis of echocardiographic images. Building on this, we propose an AI-enabled framework that leverages intrac

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