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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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使用深度学习进行口腔内扫描细分.

Shankeeth Vinayahalingam1,2,3, Steven Kempers1,2, Julian Schoep4

  • 1Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands.

BMC oral health
|September 5, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一个自动化的深度学习系统,用于精确的牙细分和标记从口腔内扫描. 人工智能模型实现了高精度,大大提高了牙科治疗计划的效率.

关键词:
人工智能的人工智能是人工智能.计算机辅助的规划 计算机辅助的规划深度学习是一种深度学习.数字成像技术的数字成像在口腔内扫描扫描.

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科学领域:

  • 牙科技术 牙科技术
  • 医学中的人工智能
  • 医疗图像分析 医学图像分析

背景情况:

  • 口腔内扫描 (OS) 对于牙科治疗至关重要,需要精确的牙细分.
  • 手动细分是耗时的,主观的,劳动密集的.
  • 需要自动化方法来提高效率和一致性.

研究的目的:

  • 开发和验证一个深度学习系统,用于自动化牙细分和标记从口腔内扫描.
  • 将自动化系统的性能与手动细分进行比较.
  • 评估AI在简化牙科工作流程方面的潜力.

主要方法:

  • 开发了一个结合PointCNN和3D U-net的深度学习模型.
  • 该模型在1400次口腔内扫描 (OS) 上进行了训练和验证.
  • 在350个操作系统的测试组上,使用交叉与欧盟 (IoU) 和外国直接投资 (FDI) 标签准确度来评估性能.

主要成果:

  • 自动化系统在牙细分方面实现了0.915的平均IOU.
  • 外国直接投资者牙标签准确度达到0.894.4的平均值.
  • 光学检查证实与手动细分有很好的一致性,边缘有轻微的差异.

结论:

  • 拟议的深度学习方法为牙细分和标签提供了有效的时间和独立于观察者的解决方案.
  • 这项技术有望提高各种牙科专业的虚拟治疗计划.
  • 建议进行进一步的临床影响研究,以探索其在实践中的整合.