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评估深度学习骨龄算法对临床图像变化的稳定性,使用计算压力测试.

Samantha M Santomartino1, Kristin Putman1, Elham Beheshtian1

  • 1From the Drexel University College of Medicine, Philadelphia, Pa (S.M.S.); University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, 670 W Baltimore St, 1st Fl, Room 1172, Baltimore, MD 21201 (S.M.S., K.P., E.B., V.S.P., P.H.Y.); and Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, Md (P.H.Y.).

Radiology. Artificial intelligence
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PubMed
概括

一个获奖的儿科骨年龄深度学习模型显示在转换的手部放射图上有不一致的预测,尽管对外部数据进行了良好的概括. 这突出了医疗成像中人工智能在现实世界图像变化的挑战.

关键词:
卷积神经网络是一个卷积神经网络.手 手 手的 手的 手.儿科 儿科 儿科放射学 放射学 放射学 放射学

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科学领域:

  • 医学成像和人工智能 医学成像和人工智能
  • 儿科放射学 儿科放射学
  • 医疗保健中的深度学习

背景情况:

  • 深度学习 (DL) 模型越来越多地用于医学图像分析.
  • 评估模型对现实世界图像变化的稳定性对于临床采用至关重要.
  • 骨质年龄评估是儿科放射学中常见的任务.

研究的目的:

  • 评估领先的儿科骨年龄DL模型的强度.
  • 为了对模拟现实条件的图像外观变化进行性能评估.
  • 为了确定模型的准确性是否受到常见图像转换的影响.

主要方法:

  • 2017年RSNA儿科骨龄挑战赛中获胜的DL模型的回顾性评估.
  • 在两个数据集上进行测试:RSNA验证集和数字手 Atlas (DHA).
  • 应用七个图像转换 (旋转,翻转,亮度,对比度,反转,横向标记,分辨率) 来模拟变化.

主要成果:

  • DL模型对外部DHA数据集进行了很好的概括 (MAD 6.9个月与RSNA 6.8个月).
  • 在两个数据集中的大多数转换中,观察到平均绝对差异 (MAD) 的显著差异.
  • 在DHA数据集上的57%的转换中,临床上显著的错误增加了.

结论:

  • 儿科骨年龄DL模型对外部数据进行了良好的概括.
  • 该模型在接受常见的图像转换时表现出不一致的预测.
  • 对于现实世界图像变化的稳定性仍然是当前儿科骨年龄DL模型的挑战.