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基于深度学习的专家级右心室异常检测算法的开发.

Zeye Liu1,2,3,4, Hang Li1,2,3,4, Wenchao Li5

  • 1Department of Structural Heart Disease, National Center for Cardiovascular Disease, China and Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, China.

Interdisciplinary sciences, computational life sciences
|July 20, 2023
PubMed
概括

一个新的深度学习算法使用心脏MRI数据准确检测右心室 (RV) 异常. 这种人工智能工具超越了人类专家的性能,改善了RV疾病的诊断和治疗.

关键词:
人工智能的人工智能是人工智能.深度学习是一种深度学习.心脏衰竭是因为心脏衰竭.磁共振成像技术 磁共振成像技术右心室的异常情况.

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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
  • 医疗成像医学成像

背景情况:

  • 右心室 (RV) 异常需要改进的诊断算法.
  • 目前用于RV异常的诊断方法不足.
  • 人工智能为增强心脏诊断提供了潜力.

研究的目的:

  • 开发和验证用于检测RV异常的深度学习算法.
  • 将AI算法的性能与机器学习模型和人类专家进行比较.
  • 探索使用名录来评估患者的疾病风险.

主要方法:

  • 利用自动心脏诊断挑战数据集与40名受试者 (20名RV异常,20名正常).
  • 训练了一个深度学习神经网络和六个机器学习算法.
  • 与8名MRI专家对模型进行了验证,并使用AUC,精度,回忆,灵敏度和特异性来评估性能.

主要成果:

  • 在训练和验证组中,深度学习算法实现了1的AUC (95% CI: 1-1).
  • 人工智能算法超过了6个机器学习算法和87.5%的人类专家.
  • 一种名目录模型表明,它能够在0.2-0.8.8范围内评估疾病风险.

结论:

  • 一个深度学习算法可以有效地识别RV异常患者.
  • 这种人工智能工具有可能改善跨护理层次的RV疾病的检测和及时诊断.
  • 这项研究是首次验证人工智能算法对VR异常与人类专家性能进行验证.