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深度学习算法用于预测心房动与快速心室反应的心房动中的左心室缩功能障碍.

Joo Hee Jeong1, Sora Kang2, Hak Seung Lee2

  • 1Division of Cardiology, Department of Internal Medicine, Korea University College of Medicine, Korea University Anam Hospital, 73 Goryeodae-ro, Seongbuk-gu, Seoul 02841, Republic of Korea.

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人工智能准确地预测心房动患者的左心室缩功能障碍与快速心室反应. 这种人工智能工具有助于在门诊机构的早期诊断和治疗选择.

关键词:
人工智能的人工智能是人工智能.心房动是一种心房动.深度学习是一种深度学习.左心室喷射分数的部分.利率控制是为了控制利率.

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科学领域:

  • 心脏病学 心脏病学
  • 人工智能的人工智能
  • 医学诊断 医学诊断 医学诊断

背景情况:

  • 左心室喷射分数 (LVEF) 对于管理心房动 (AF) 与快速心室反应 (RVR) 至关重要.
  • 实时LVEF评估在门诊心脏病学环境中具有挑战性.
  • 乳静脉缩功能障碍 (LVSD) 对AF患者的治疗策略产生影响.

研究的目的:

  • 验证基于人工智能的深度学习算法,用于预测RVR的AF患者的LVSD.
  • 通过使用12导和1导心电图来评估算法的性能.
  • 将AI预测与已知生物标志物 (如NT-proBNP) 进行比较.

主要方法:

  • 深度学习算法的外部验证 (残余神经网络).
  • 对423名患有RVR的AF患者 (2018-2023) 的前性队列的分析.
  • 主要结果:通过12导电图检测LVSD (LVEF ≤40%);次要结果:通过1导电图预测.

主要成果:

  • 人工智能算法在预测LVSD (AUC 0.78) 中表现良好,预测值为0.88.8.
  • 人工智能性能与NT-proBNP (AUC0.78与0.70) 相比.
  • 一心电图预测不太准确 (AUC 0.68),但保持了高的NPV (0.88).

结论:

  • 基于人工智能的算法在预测RVR的AF中的LVSD方面表现出良好的表现.
  • 人工智能促进了门诊LVSD预测,可能使早期的治疗干预成为可能.
  • 这项技术可以改善经历RVR的AF患者的症状管理.