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基于深度学习的多发性硬化症诊断分类使用多中心光学连贯断层扫描数据.

Zahra Khodabandeh1, Hossein Rabbani1, Neda Shirani Bidabadi2

  • 1Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan 817467346, Iran.

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概括
此摘要是机器生成的。

人工智能 (AI) 对光连贯断层扫描 (OCT) 视网膜扫描的分析准确地检测到多发性硬化症 (MS). 这种人工智能方法为早期MS诊断和管理提供了一个有希望的,非侵入性的生物标志物.

关键词:
人工智能的人工智能多发性硬化症是多发性硬化症.视神经病理学 视神经病理学光学连贯性断层扫描技术视网膜层分析

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科学领域:

  • 眼科医生 眼科 眼科
  • 神经学 神经学
  • 人工智能的人工智能

背景情况:

  • 多发性硬化症 (MS) 是一种需要精确诊断的中枢神经系统炎症性疾病.
  • 光学连贯断层扫描 (OCT) 检测视网膜变化,潜在的MS生物标志物.
  • 超越原始图像检查的先进分析方法需要在成员国中微妙的OCT变化.

研究的目的:

  • 评估人工智能 (AI) 模型用于分类多发性硬化症 (MS) 使用光学连贯性断层扫描 (OCT) 获得的视网膜特征.
  • 为了确定最有信息的视网膜层厚度和表面特征用于MS检测.
  • 评估AI模型在MS分类中的可解释性和通用性.

主要方法:

  • 研究了三个AI模型类别:使用自动编码器 (AE) 和浅层网络进行特征提取,定制深度网络和微调预训练网络.
  • 分析了OCT的视网膜层厚度和表面图,通过通道智能组合和马赛克集成特征.
  • 在38个健康对照 (HC) 和78个MS眼睛的数据集上,利用了闭塞灵敏度和Grad-CAM用于模型解释性.

主要成果:

  • 结合视网膜神经纤维层 (RNFL),质细胞和内状层 (GCIPL) 和内核层 (INL) 厚度图的深度网络实现了97.3%的平衡精度.
  • 当将公共和本地数据集结合用于内部交叉验证时,观察到高性能.
  • 在交叉数据集评估中,绩效显著下降,突出了有限的外部通用性,特别是在对公共数据进行培训时.

结论:

  • 基于人工智能的OCT衍生的视网膜特征分析提供了准确和可解释的MS分类.
  • 这种方法支持OCT衍生的视网膜生物标志物用于MS诊断的潜力.
  • 需要进一步的研究,以提高AI模型在不同数据集中的通用性.