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深度学习方法应用于刺测试的自主图像诊断.

Ramon Hernany Martins Gomes1, Edson Luiz Pontes Perger2, Lucas Hecker Vasques1

  • 1Department of Bioprocess and Biotechnology, School of Agriculture, São Paulo State University (UNESP), Avenue Universitária, 3780, Botucatu 18610-034, SP, Brazil.

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概括

一个新的深度学习模型准确地测量皮肤刺探测试 (SPT) 面粉尺寸,有可能自动化过敏诊断并改进标准方法.

关键词:
的IgE反应反应.深度学习用于诊断的应用.测量小麦面积的小麦面积.刺刺测试试验 刺刺测试 刺刺测试对抗原的敏感化.

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科学领域:

  • 过敏和免疫学的过敏和免疫学.
  • 医学成像医学成像
  • 人工智能的人工智能是人工智能.

背景情况:

  • 皮肤刺伤测试 (SPT) 是一种用于抗原敏感化的标准诊断工具.
  • 人类对SPT中小麦尺寸的解释可能会带来变化.
  • 自动化SPT分析可能会提高诊断准确性和效率.

研究的目的:

  • 开发和评估一个深度学习模型,用于在SPT中自动化Wheal维度推断.
  • 将深度学习模型的准确性与标准测量协议和辅助图像分割进行比较.

主要方法:

  • 一个卷积神经网络 (ML模型) 在 5844 个 SPT 图像上进行了训练,用于小麦细分.
  • 使用ML模型,标准协议 (MA1) 和圆近似 (MA2) 推断了车轮尺寸.
  • 结果与使用布兰德-阿尔特曼分析,相关性测试和百分比偏差的辅助图像分割 (AIS) 相比较.

主要成果:

  • ML模型实现了85.88%的细分精度,优于其他方法.
  • 在ML模型和AIS之间观察到强烈的相关性 (ρ = 0.88).
  • 标准协议 (MA1) 显示出严重的错误,特别是在伪脚.

结论:

  • 开发的ML协议为读取SPT结果提供了更准确和潜在的自动化方法.
  • 这种深度学习方法可以减少对过敏诊断中的主观人类解释的依赖.