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在缺血性中风诊断中使用神经网络算法:系统性审查

Suebsarn Ruksakulpiwat1, Lalipat Phianhasin1, Chitchanok Benjasirisan1

  • 1Department of Medical Nursing, Faculty of Nursing, Mahidol University, Bangkok, Thailand.

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概括

人工神经网络 (NN) 显示越来越多的证据用于诊断缺血性中风 (IS). 需要进一步的研究来确认它们的临床可行性和成本效益,以便广泛采用.

关键词:
缺血性中风 中风神经网络的神经网络的神经网络系统性审查 系统性审查

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 神经学 神经学

背景情况:

  • 缺血性中风 (IS) 诊断依赖于先进的成像技术.
  • 人工神经网络 (NN) 提供了提高诊断准确性的潜力.

研究的目的:

  • 系统地审查人工神经网络 (NN) 对成年人缺血性中风 (IS) 诊断有效性的证据.

主要方法:

  • 按照PRISMA指南进行系统审查.
  • 在PubMed,MEDLINE,Web of Science和CINAHL Plus中搜索了全文 (2018-2022年).
  • 使用诊断测试准确性研究的批判性评估检查清单进行评估的研究.

主要成果:

  • 包括九项研究,重点是非对比计算断层扫描 (NCCT) 和计算机断层扫描血管学 (CTA).
  • 深度卷积神经网络 (DCNNs) 是使用最多的算法 (33.33%).
  • 其他算法包括3D-CNNs,双阶段DCNNs,GL-HOSVD和AD-CNNnet.

结论:

  • 越来越多的证据支持NNs用于IS诊断.
  • 临床实施需要进一步研究可行性和成本效益.