在心房的诊断中,人工智能驱动的进步:系统性审查
Sofia Khaja1, Kevin Baijoo2, Reza Aziz2
1City University of New York (CUNY) School of Medicine, New York, United States. skhaja000@citymail.cuny.edu.
概括
人工智能 (AI) 工具,包括可穿戴设备和神经网络,在检测心房动,一种常见的心律不整方面表现有前途. 虽然人工智能显示出高精度,但需要进一步验证才能得到广泛的临床采用.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 心血管疾病是全球主要的死亡原因.
- 前庭动 (AF) 是一种需要精确检测的显著心律失常.
- 人工智能 (AI) 正在推动AF诊断和管理.
研究的目的:
- 审查最近的人工智能驱动的工具来检测心房动.
- 评估这些人工智能工具的临床相关性,局限性和变革潜力.
- 在AF诊断中检查可穿戴设备,神经网络和机器学习.
主要方法:
- 对PubMed,IEEE Xplore和ScienceDirect (2020-2024) 的系统审查.
- 包括人工智能用于AF诊断的原始临床研究.
- 排除对其他疾病或不完整数据的研究.
主要成果:
- 评估了11项关于人工智能增强的AF诊断的研究.
- 神经网络在心电图分析中实现了高精度 (80%),超过了临床医生 (75%).
- 可穿戴的人工智能设备提供可访问的实时监控 (灵敏度>94%) 但有局限性.
结论:
- 人工智能,包括可穿戴设备和神经网络,可以增强AF诊断.
- 当前的人工智能工具显示出潜力,但需要更多的验证才能在现实世界中使用.
- 未来的研究应该优先考虑公平,透明和更广泛的AI应用在心血管保健中.
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