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牙科修复设计的生成深度学习方法:叙述性审查

Alexander Broll¹, Markus Goldhacker², Sebastian Hahnel¹

¹Department of Prosthetic Dentistry, University Hospital Regensburg, Regensburg, Germany.

Journal of dentistry

|April 12, 2024

概括

深度学习 (DL) 技术显著提高了牙重建的准确性,其错误率是传统方法的两倍. 生成性DL模型的这些进步提高了牙科修复和患者的结果.

科学领域:

生物医学工程生物医学工程
计算机科学计算机科学
人工智能的人工智能

背景情况:

牙重建对于牙修复至关重要.
传统的数字化方法在准确性和效率方面存在局限性.
深度学习 (DL) 提供了新的方法来应对这些挑战.

研究的目的:

通过深度学习 (DL) 技术审查和讨论牙重建的最新进展.
识别和分析用于部分和全牙重建的新型DL方法.

主要方法:

在PubMed,谷歌学者和IEEE Xplore (2003-2023) 的系统文献搜索.
包括9篇文章 (2018-2023年),重点关注牙重建的新型DL方法.
分析数据采集 (口内/实验室扫描),数据表示 (深度图,点云,voxel) 和网络架构 (GAN,变压器).

主要成果:

基于DL的牙重建使用口腔内或实验室扫描数据.
常见的数据表示包括深度地图,点云和voxelized点云.
生成对抗网络 (GAN) 和变压器是主要架构,实现的错误率大约是传统方法的两倍.

结论:

生成式DL模型通过从牙科数据集中学习模式,有效地重建缺失的牙.
在DL的进步有助于可行和准确的牙冠重建.
新兴趋势包括基于扩散的架构和新的数据表示,如用于3D形状推断的波纹.

关键词:

深度学习 (Deep Learning) 是一种深度学习.牙假体设计牙假体设计数字牙科数字牙科牙的重建牙的重建

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