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AI model cAPM significantly improves pace-mapping for VT ablation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed cAPM, a novel continual learning AI model designed to improve the efficiency of pace-mapping for ventricular tachycardia (VT) ablation. This AI system learns from past pace-mapping data to guide clinicians to the most informative pacing sites, significantly reducing the number of sites needed. In silico tests showed cAPM achieved an 81% probability of accurate localization with an average of 4.5 pacing sites, a substantial improvement over existing methods. AI

IMPACT This AI model could streamline cardiac ablation procedures, potentially reducing patient recovery times and improving outcomes.

RANK_REASON The cluster contains a research paper detailing a new AI model for a medical procedure. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI model cAPM significantly improves pace-mapping for VT ablation

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The cluster contains a research paper detailing a new AI model for a medical procedure. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Dylan O'Hara, Pradeep Bajracharya, Casey Meisenzahl, Karli Gillette, Anton J. Prassl, Gernot Plank, Saman Nazarian, Roderick Tung, John L Sapp, Linwei Wang ·

    cAPM: Continual AI-Assisted Pace-Mapping with Active Learning

    arXiv:2606.19373v1 Announce Type: cross Abstract: Ventricular tachycardia is a life-threatening rhythm disorder and a major cause of sudden cardiac death. Pace-mapping is a clinical procedure for identifying the intervention target during catheter ablation of VT. It requires clin…

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