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对一种新的基于深度学习的病理近视查系统的评估.

Pei-Fang Ren1, Xu-Yuan Tang1, Chen-Ying Yu1

  • 1Department of Ophthalmology, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, Zhejiang Province, China.

International journal of ophthalmology
|September 19, 2023
PubMed
概括

一个基于深度学习的人工智能系统在识别病态近视 (PM) 和近视冠状腺新血管化 (mCNV) 中表现出高准确性. 这种人工智能工具有望成为临床查的辅助诊断辅助工具.

关键词:
人工智能的人工智能是人工智能.冠状腺新血管化 冠状腺新血管化深度学习是一种深度学习.病理性的近视近视.

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科学领域:

  • 眼科医生 眼科 眼科
  • 人工智能的人工智能
  • 医疗成像医学成像

背景情况:

  • 病态近视 (PM) 是导致视力障碍的主要原因.
  • 近视性冠状腺新血管化 (mCNV) 是PM的严重并发症.
  • 准确及时诊断PM和mCNV对于有效管理至关重要.

研究的目的:

  • 评估基于深度学习的人工智能 (AI) 模型用于诊断PM的临床实用性.
  • 评估人工智能模型在识别mCNV方面的能力.
  • 将AI模型的性能与经验丰富的眼科医生进行比较.

主要方法:

  • 一组数据集包括1156张彩色 fundus 照片,这些照片被策划和注释为PM.
  • 一个人工智能系统 (PM-AI) 和四名眼科医生独立分析了这些图像.
  • 计算和比较了包括灵敏度,特异性和卡帕值在内的绩效指标.

主要成果:

  • 该PM-AI系统实现了PM识别的高灵敏度 (98.17%) 和特异性 (93.06%),与人类专家相美或超过.
  • 对于mCNV检测,人工智能系统的灵敏度为84.06%,特异性为95.31%.
  • 人工智能系统对PM和mCNV的Kappa值表明了实质性的协议,类似于高级专家.

结论:

  • 基于深度学习的PM-AI系统在识别PM和mCNV方面表现出色.
  • 人工智能系统作为一个有价值的辅助诊断工具用于PM和mCNV的临床查.
  • 人工智能辅助诊断可能会提高眼科评估的效率和准确性.