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使用计算机视觉估计牙周稳定性

B Feher1,2,3,4, A A Werdich5, C-Y Chen1

  • 1Department of Oral Medicine, Infection, and Immunity, Harvard School of Dental Medicine, Boston, MA, USA.

Journal of dental research
|March 17, 2025
PubMed
概括
此摘要是机器生成的。

计算机视觉可以分析牙科放射图以评估牙周稳定性,有助于诊断牙周炎. 这种方法为评估口腔健康提供了传统临床探针的潜在替代方案.

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.诊断成像诊断成像的使用.医疗信息学计算计算牙周病医学 牙周病医学辐射学 放射学 辐射学

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科学领域:

  • 人工智能在牙科中的应用
  • 牙周病的诊断 牙周病的诊断
  • 医学成像分析 医学成像分析

背景情况:

  • 牙周炎显著影响口腔和全身健康.
  • 通过临床探测进行传统的诊断是耗时的,不舒服的,并且取决于操作者.
  • 需要新的方法来进行高效和准确的牙周评估.

研究的目的:

  • 调查使用计算机视觉在X线图上估计牙周稳定性的可行性.
  • 开发基于仅从放射数据的牙周健康来对牙和患者进行分类的模型.

主要方法:

  • 内射线被用来训练计算机视觉模型.
  • 一个三向分类模型将牙分为健康,稳定或不稳定.
  • 一个二进制患者分类器根据牙状态确定了整体稳定性.

主要成果:

  • 牙分类模型表现中等 (AUC为0.56-0.71,F1得分为0.45-0.57).
  • 患者分类模型实现了AUC为0.68和F1得分为0.74.
  • Saliency 地图突出显示了牙周围临床相关的区域.

结论:

  • 计算机视觉可以从X射线图中估计牙周稳定性,这可能会减少对临床探测的依赖.
  • 需要进一步改进模型,特别是对于牙级分类的准确性.
  • 使用人工智能的放射分析显示了改善牙周病管理的前景.