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相关概念视频

Teeth01:15

Teeth

383
The formation of teeth, also known as odontogenesis, is a complex process that begins in utero, around the sixth week of embryonic development. There are three stages to this process: the bud stage, the cap stage, and the bell stage.
In the bud stage, the tooth germ (an aggregation of cells) starts to form in the developing jawbone. During the cap stage, the tooth germ differentiates into enamel organ, dental papilla, and dental sac, which will later develop into the tooth's enamel, dentin...
383

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相关实验视频

Updated: Jun 24, 2025

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
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使用深度学习,对第三关节发育阶段进行完全自动化的分类.

Omid Halimi Milani1, Salih Furkan Atici1, Veerasathpurush Allareddy2

  • 1Department of Electrical and Computer Engineering, University of Illinois Chicago, Chicago, IL, USA.

Scientific reports
|June 6, 2024
PubMed
概括

这项研究开发了一种自动化方法,使用骨架影像 (OPG) 来分类第三子发育阶段. 效率网实现了83.7%的准确性,为牙科诊断和治疗计划提供了有价值的工具.

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相关实验视频

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科学领域:

  • 牙科放射学 牙科放射学
  • 人工智能在医学中的应用
  • 生物识别信息 生物识别信息

背景情况:

  • 精确的牙发育阶段的分类从骨科形图 (OPGs) 对于牙科诊断,治疗计划,年龄评估和法医科学至关重要.
  • 自动化方法可以提高分析OPG的效率和一致性.

研究的目的:

  • 开发和评估一个自动化的机器学习模型,用于使用OPG图像对第三发育阶段进行分类.
  • 为此分类任务比较各种深度神经网络架构的性能.

主要方法:

  • 一组6624张OPG图像 (Q3和Q4区域) 的数据集被精选和预处理.
  • 应用了感兴趣的区域提取,预先过和数据增强.
  • 包括EfficientNet,EfficientNetV2,MobileNet,ResNet18和ShuffleNet在内的深度神经网络模型进行了培训和评估.

主要成果:

  • 效率网在83.7%的分类准确度中显示出最高的分类准确度.
  • 其他评估的架构的准确度在71.57%至82.03%之间.
  • 模型性能有所不同,表明了架构复杂性和特征提取的影响.

结论:

  • 一个新的机器学习模型有效地估计了OPGs的低智发育阶段.
  • 开发的自动化方法显示了提高牙科诊断和治疗规划的前景.
  • 进一步的研究可以探索优化模型以提高准确性和更广泛的临床应用.