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相关概念视频

Computed Tomography01:10

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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通过使用卷积神经网络算法从斜射线影像检测宫前狭窄.

Jihie Kim1, Jae Jun Yang2, Jaeha Song3

  • 1Department of Artificial Intelligence, Dongguk University, Seoul, Korea.

Yonsei medical journal
|June 24, 2024
PubMed
概括

一个新的卷积神经网络 (CNN) 算法准确地从斜射线图中诊断出宫前狭窄. 这种人工智能工具的准确性高于人类外科医生,有可能改善这种疾病的查.

关键词:
卷积神经网络是一种卷积神经网络.宫前性狭窄的宫.宫斜率放射图 宫斜率放射图深度学习是一种深度学习.机器学习是机器学习.磁共振成像技术的使用选工具是一个选工具.

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科学领域:

  • 放射学 放射学是一门学科.
  • 人工智能的人工智能
  • 医疗成像医学成像

背景情况:

  • 宫前狭窄的诊断通常依赖于MRI,这可能是昂贵和耗时的.
  • 斜射线图是一种更容易获得的成像方式,但它们对前狭窄的诊断准确性可能有限.
  • 开发自动诊断工具可以提高临床环境中的效率和准确性.

研究的目的:

  • 开发和评估一个卷积神经网络 (CNN) 算法,用于使用斜射线图诊断宫前狭窄.
  • 为了比较CNN算法的诊断准确度与人类专家的解释.

主要方法:

  • 使用了997名患者的数据集,其中包括宫MRI和斜X射线图.
  • 斜射线图被标记为基于MRI地面真相的前狭窄.
  • 使用数据增强,预处理和转移学习开发了一个CNN模型 (DenseNet161).
  • 梯度加权类激活映射 (Grad-CAM) 用于模型可视化.

主要成果:

  • 在CNN模型中,曲线下的面积 (AUC) 为0.889.9.
  • 该模型表现出高性能,F1得分为88.5%,准确度为84.6%,精度为88.1%,回忆率为88.5%.
  • 美国有线电视新闻网的准确性明显超过了两个骨科外科医生 (64.0%和58.0%).
  • 格拉德-CAM分析表明,CNN专注于前和磁盘空间区域.

结论:

  • 一个CNN算法有效地检测到从斜X射线图的宫神经前狭窄.
  • 开发的CNN显示了具有高AUC,F1得分和准确性的有希望的结果.
  • 用这种CNN模型增强的宫斜率放射能可以作为神经前狭窄症的有效查工具.