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相关概念视频

Tooth Anatomy01:21

Tooth Anatomy

501
The human tooth enables us to eat a variety of foods, speak clearly, and even aid in shaping our faces. Teeth are composed of various elements that work together. Here's a detailed look at the anatomy of a human tooth.
The Crown, Neck, and Root
The visible part of the tooth is referred to as the crown. It's covered by enamel, the hardest substance in the human body. The crown is uniquely shaped for each type of tooth, allowing for different functions such as cutting, tearing, or...
501

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相关实验视频

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Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
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在咬翼放射图片中自动检测:第I部分-深度学习

Lukáš Kunt1, Jan Kybic2, Valéria Nagyová3

  • 1Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic.

Clinical oral investigations
|November 15, 2023
PubMed
概括

卷积神经网络 (CNN) 可以自动检测咬牙翼放射图中的牙腐烂,实现人类水平的性能. 虽然对大多数病变有效,但检测小或初始仍然是一个挑战.

关键词:
咬伤翅膀咬伤翅膀是什么意思卷积神经网络是一种卷积神经网络.牙腐烂检测 检测牙腐烂的检测一起组合在一起.一些X射线图像.

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科学领域:

  • 人工智能在牙科中的应用
  • 医学成像分析 医学成像分析
  • 机器学习用于医疗保健

背景情况:

  • 检测牙腐烂对于及时干预至关重要.
  • 手动解读牙科X射线图可能是耗时和主观的.
  • 自动化牙损伤检测可以提高牙科诊断的效率和准确性.

研究的目的:

  • 开发和评估卷积神经网络 (CNN),用于自动检测咬牙的牙损伤.
  • 为训练和测试人工智能模型创建一个大,注释数据集的咬翼放射图片.
  • 通过深度学习,在检测病变方面实现人类水平的性能.

主要方法:

  • 一个数据集的3989咬翼放射图被注释了7257种病变.
  • 多个CNN架构 (YOLOv5,更快的R-CNN,RetinaNet,EfficientDet) 进行了训练和测试.
  • 采用模型组装和后处理技术来提高检测准确度.

主要成果:

  • 经过测试的CNN架构实现了F1分数在0.72-0.76.6之间.
  • 模型组装改善了F1的得分,达到0.79-0.80.
  • 最好的组合模型在IoU=0.5时达到0.83的精度,回忆0.77和0.86的平均精度 (AP),小病变的精度略低 (AP 0.82).

结论:

  • 对象检测CNN集团在虫检测方面表现出令人满意的准确性,与经验丰富的牙医相提并论.
  • 使用CNNs自动检测牙损伤是可行的.
  • 检测初始病变仍然是一个挑战,可能是由于数据集不一致.