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用卷积神经网络进行深度学习,在全景射线图上检测 mesiodens:比较四个模型.

Sachiko Hayashi-Sakai1, Hideyoshi Nishiyama2, Takafumi Hayashi2

  • 1Department of Pediatric Dentistry, The Nippon Dental University School of Life Dentistry at Niigata, 1-8 Hamaura-cho, Chuo-ku, Niigata, 951-8580, Japan. sakais@ngt.ndu.ac.jp.

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概括

一个简单,轻量级的深度学习模型有效地检测了全景放射图上的 mesiodens. 这种基于人工智能的诊断可以帮助不明确的病例,但专家审查仍然至关重要,特别是对于儿童.

关键词:
人工智能的人工智能是人工智能.深度学习是一种深度学习.弥赛亚是一个人.全景射线图 (Panoramic Radiograph) 是一个全景射线图.更多的牙是多余的牙.

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科学领域:

  • 牙科 牙科是指牙科的专业.
  • 放射学 放射学是一门学科.
  • 人工智能的人工智能

背景情况:

  • 弥西奥登斯 (Mesiodens) - - 过多的牙,可能会导致并发症.
  • 在全景射线图上精确检测对于及时干预至关重要.
  • 当前的诊断方法可能会面临不清晰图像的挑战.

研究的目的:

  • 开发一个最佳,简单,轻量级的深度学习卷积神经网络 (CNN) 模型.
  • 为了评估 mesiodens 检测开发的 CNN 模型的诊断性能.
  • 为了利用SHapley添加式解释 (SHAP) 来实现模型的解释性.

主要方法:

  • 通过628张全景放射图训练并验证了四个修改后的CNN模型.
  • 评估模型性能使用准确度,精度,回忆,F1分数,ROC曲线和AUC.
  • 采用SHAP来可视化对分类至关重要的图像特征.

主要成果:

  • 一个二进制_connect_mnist_LeNet模型在四个深度学习模型中表现出最佳的诊断性能.
  • 轻量级的CNN模型成功地检测到了 mesiodens.
  • SHAP分析提供了对影响模型分类的图像特征的见解.

结论:

  • 一个简单,轻量级的深度学习模型能够检测 mesiodens.
  • 基于人工智能的诊断可以成为不清晰的全景放射图的宝贵补充.
  • 由于儿童的辐射敏感性,需要专家重新评估.