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English(EN) cAPM: Continual AI-Assisted Pace-Mapping with Active Learning

AI模型cAPM显著改善室性心动过速消融的节律标测

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了cAPM，一种新颖的持续学习AI模型，旨在提高室性心动过速（VT）消融的节律标测效率。该AI系统从过去的节律标测数据中学习，指导临床医生找到信息量最大的起搏位点，显著减少所需的位点数量。计算机模拟测试表明，cAPM在平均4.5个起搏位点的情况下，实现了81%的准确标测概率，相比现有方法有了显著改进。 AI

影响 该AI模型有望简化心脏消融手术，可能缩短患者的恢复时间并改善治疗效果。

排序理由 该集群包含一篇详细介绍用于医疗程序的AI新模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

AI模型cAPM显著改善室性心动过速消融的节律标测

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该集群包含一篇详细介绍用于医疗程序的AI新模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [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：具有主动学习能力的持续人工智能辅助配速映射

    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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