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相关概念视频

Teeth01:15

Teeth

717
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...
717

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基于深度学习的方法,以临床分类的第三损伤分析.

Yunus Balel1, Kaan Sağtaş2

  • 1Department of Oral and Maxillofacial Surgery, Faculty of Dentistry, Sivas Cumhuriyet University, Sivas, Turkey. yunusbalel@hotmail.com.

Scientific reports
|July 2, 2025
PubMed
概括

一个深度学习模型使用全景放射图自动化了受影响的第三关分类. 这种人工智能工具可以提高诊断精度和临床决策,提高牙科工作流程的效率.

科学领域:

  • 牙科和人工智能的人工智能
  • 医学成像分析 医学成像分析
  • 医疗保健中的机器学习

背景情况:

  • 手动对受影响的第三牙进行分类是复杂且耗时的.
  • 像佩尔和格雷戈里,温特和佩德森困难指数这样的标准化分类系统对于治疗计划至关重要.
  • 自动化这个过程可以提高效率,减少诊断变化.

研究的目的:

  • 开发和评估深度学习模型,用于自动检测和分类受影响的第三牙.
  • 在模型中使用已建立的分类系统 (Pell和Gregory,Winter's,Pederson).
  • 根据手册分类来评估模型的性能.

主要方法:

  • 为了训练YOLOv11模型,使用了2300张全景射线图的数据集,以及额外的验证和测试集.
  • 受影响的牙通过CVAT软件使用边界框手动进行注释.
  • 通过优化的超参数和数据增强技术来训练YOLOv11模型.

主要成果:

  • 深度学习模型实现了高性能指标:精度 (0.980),回忆 (0.948),F1得分 (0.974),mAP@50 (0.990),和mAP@50:95 (0.974).
  • 该模型在检测和分类受影响的第三牙方面表现出高度准确性.
关键词:
人工智能的人工智能是人工智能.分类 分类 分类 分类.深度学习是一种深度学习.检测 检测 检测 检测 检测受到冲击的第三关节受到了影响.

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  • 一些特定的分类显示了较低的F1分数,表明了潜在的改进领域.

    • 结论:

    • 开发的深度学习模型为受影响的第三关分类提供了可靠和高效的自动化解决方案.
    • 这种人工智能工具可以作为临床牙科的有价值的决策支持系统,简化工作流程.
    • 通过增强数据集多样性和完善模型处理具有挑战性的分类,可以实现进一步的改进.