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基于深度学习的口内三维扫描自动点云补丁基于深度学习的口内三维扫描.

Qianhan Zheng1, Yimin Wang2, Mengqi Zhou1

  • 1Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Clinical Research Center for Oral Diseases of Zhejiang Province, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, Zhejiang, China.

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概括

这项研究引入了一种深度学习方法,可以在口内扫描 (IOS) 中自动恢复缺失的数据. 人工智能模型准确地重建不完整的3D点云,增强数字牙科工作流程.

关键词:
完成 完成 完成深度学习是一种深度学习.在口腔内扫描扫描.一个点云点云.

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科学领域:

  • 数字牙科数字牙科
  • 医疗保健中的人工智能
  • 3D成像和重建工作.

背景情况:

  • 口腔内扫描 (IOS) 对数字牙科至关重要,但由于复杂的口腔环境,它经常遭受数据丢失.
  • 来自IOS的不完整的3D点云阻碍了数字正牙工作流程的准确性和效率.

研究的目的:

  • 开发和评估一种基于深度学习的方法,用于在口内3D点云中自动恢复缺失的区域.
  • 通过解决 IOS 中的数据丢失,提高数字牙工作流程的准确性和效率.

主要方法:

  • 一个点断层网络架构被用于重建不完整的IOS数据.
  • 用了314个IOS扫描 (4162颗牙) 的数据集,用于训练和验证,模拟数据丢失 (5-20%).
  • 用Chamfer距离 (CD) 评估模型性能,以量化点云完成精度.

主要成果:

  • 深度学习模型表现出强大的性能,在各种数据丢失级别中实现平均CD值低于0.01.
  • 视觉评估证实了完成和原始3D点云之间的高几何准确性.
  • 该模型在大约0.5秒内处理了每个点云,从而实现了近乎实时的恢复.

结论:

  • 开发的深度学习模型准确地恢复丢失的IOS数据,大大提高了数字牙科工作流程的精度和效率.
  • 该方法的速度和准确性支持实时临床应用,减少手动校正,改善治疗结果.
  • 这种人工智能驱动的方法有可能最大限度地减少人为错误,提高牙科修复的精度,并促进更广泛的AI整合到临床实践中.