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基于深度学习的上呼吸道细分的准确性

Yağızalp Süküt1, Ebru Yurdakurban2, Gökhan Serhat Duran3

  • 1Department of Orthodontics, Gülhane Faculty of Dentistry, University of Health Sciences, Ankara 06010, Turkey.

Journal of stomatology, oral and maxillofacial surgery
|September 7, 2024
PubMed
概括

从形束计算机断层扫描 (CBCT) 扫描来细分上呼吸道体积的自动和半自动方法都显示出临床上可接受的准确性. 这些开源工具提供了高效和可比的结果,以手动细分为 ортодонтика治疗规划.

关键词:
人工智能 (AI) 是一种人工智能.圆束计算断层扫描 (CBCT) 是一种卷积神经网络 (CNN) 是一种神经网络.上空气道的细分上空气道的细分.

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科学领域:

  • 医疗成像医学成像
  • 矯正牙科 矯正牙科是一種矯正牙科.
  • 计算解剖学的计算解剖学

背景情况:

  • 对上呼吸道体积和形态的准确评估对于正牙诊断和治疗规划至关重要.
  • 圆束计算机断层扫描 (CBCT) 是评估上呼吸道的关键成像方式.
  • 从CBCT数据中对上呼吸道进行细分可以使用手动,半自动或自动方法.

研究的目的:

  • 为了比较自动上呼吸道细分模型与半自动方法和手动细分模型的准确性.
  • 评估开源工具的临床适用性,用于顶部气道分析在正牙科.

主要方法:

  • 使用MONAI标签框架开发了一个自动细分模型.
  • 使用ITK-SNAP进行了半自动细分.
  • 精度与手动细分使用子相似系数 (DSC),精度,回忆,95%的豪斯多夫距离 (HD) 和体积差异进行了评估.

主要成果:

  • 自动 (DSC:0.915±0.041) 和半自动 (DSC:0.940±0.021) 方法都显示出临床上可接受的准确性.
  • 与自动细分 (95% HD: 1.447±0.674) 相比,半自动细分的准确性更高 (95% HD: 0.997±0.585).
  • 在自动/半自动和手动方法之间没有发现统计学上显著的体积差异,用于总体,口腔和口腔气道体积.

结论:

  • 开源自动和半自动方法都提供了精确和高效的上呼吸道细分,与手动细分相美.
  • 这些方法可以通过简化细分过程来帮助正义牙科的决策.
  • 这些工具的实施可以增强在正义牙科实践中的诊断能力.