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机器学习用于定位早发性心室收缩的起源:一篇评论

Rui Yang¹, Yiwen Wang¹, Yanan Wang¹

¹Department of Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai, P.R. China.

Pacing and clinical electrophysiology : PACE

|October 21, 2024

概括

机器学习 (ML) 有助于精确地定位过早心室收缩 (PVC) 的起源,常见的心律不整. 本综述探讨了用于临床医生和研究人员的PVC本地化中的ML应用,好处和挑战.

科学领域:

心脏病学心脏病学
医疗信息学医疗信息学
人工智能的人工智能

背景情况:

过早的腹腔收缩 (PVC) 是一种常见的心律失常,源于腹腔外宫跳动.
精确地定位PVC来源对于有效的电生理学研究和治疗至关重要.
临床数据 (ECG,CT,MRI) 和机器学习 (ML) 的进步提供了新的分析能力.

研究的目的:

审查ML的开发和应用,以定位PVC的来源.
概述ML在PVC相关的电生理学研究中的作用.
作为临床医生和研究人员使用ML进行PVC诊断和研究的参考.

主要方法:

在PVC原产地定位中对ML应用的文献综述.
对临床数据 (ECG,CT,MRI) 应用的ML技术的分析,包括深度学习 (DL).
评估ML在这个领域的优点,缺点和未来方向.

主要成果:

ML,特别是DL,已成为分析用于电生理学研究的临床数据的强大工具.
ML技术在提高PVC原产地定位的精度方面具有显著的潜力.
该审查综合了有关ML对了解和管理PVC的影响的当前知识.

结论:

在电生理学研究中,ML对于精确的PVC定位变得越来越重要.
了解ML的能力和局限性对于其有效的临床应用至关重要.
未来的研究应该专注于进一步完善ML技术,以改善PVC诊断和管理.

关键词:

节律失常 (arrhythmia) 是一种心律失常.心脏电生理学心脏电生理学电心电图 (ECG) 是一种心电图.机器学习是机器学习.步伐绘制 - 步伐绘制过早的心室收缩过早的心室收缩

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