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相关实验视频

Updated: Jul 9, 2025

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
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基于深度学习的骨头移植材料的自动细分,大鼻腔增大后.

Baoxin Tao1,2,3,4,5,6, Jiangchang Xu7, Jie Gao1,2,3,4,5,6

  • 1Department of Second Dental Center, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Clinical oral implants research
|November 30, 2023
PubMed
概括

深度学习准确地使用圆束计算断层扫描 (CBCT) 在鼻增大 (SA) 中对移植材料进行细分. 这种人工智能模型在速度和精度上明显优于外科医生的手动细分.

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.数字牙科数字牙科神经网络的神经网络的神经网络鼻增大 鼻增大 鼻增大

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科学领域:

  • 生物医学成像学 生物医学成像学
  • 人工智能在牙科中的应用
  • 进行外科手术的计划.

背景情况:

  • 大鼻腔增大 (SA) 对于牙植入物放置至关重要.
  • 对移植材料进行准确的体积评估对于治疗成功至关重要.
  • 从圆束计算机断层扫描 (CBCT) 进行移植材料的手动细分是耗时且主观的.

研究的目的:

  • 为了评估深度学习的准确性和可靠性,用于自动移植材料细分.
  • 将深度学习模型的性能与经验丰富的外科医生手动细分的性能进行比较.

主要方法:

  • 一个深度学习模型 (3D V-Net和3D注意力V-Net) 使用100个配对的CBCT扫描被开发出来.
  • 两名外科医生和一名计算机工程师的共识建立了基本真理.
  • 模型的性能被评估使用子系数,豪斯多夫距离和测试组的平均表面距离.

主要成果:

  • 深度学习模型实现了 90.36% ± 2.53% 的 Dice 系数.
  • 该模型表现出优异的精度,95%的豪斯多夫距离为1.59±0.82毫米,平均表面距离为0.38±0.11毫米.
  • 自动细分需要7.2秒,而手工细分需要19.15分钟.

结论:

  • 深度学习提供了一个非常准确和高效的方法,用于SA.之后的移植材料细分.
  • 模型的性能超过了经验丰富的外科医生.
  • 这项技术可以增强体积变化评估,植入物规划和数字牙科工作流程.