使用卷积神经网络和CBCT对牙厚度进行临床导向的3D可视化和定量分析
Lan Yang1,2, ZiCheng Zhu3, Yongshan Li2
1School of Stomatology, Craniomaxillofacial Implant Research Center, Fujian Medical University, Fuzhou, Fujian, China.
Frontiers in dental medicine
|September 3, 2025
概括
这项研究引入了一种使用深度学习和CBCT数据的新型3D可视化系统,以空间地绘制牙厚度 (GT). 这种先进的工具提高了牙科植入器手术规划的精度.
科学领域:
- 生物医学工程
- 牙科成像
- 医学的人工智能
背景情况:
- 传统的牙厚度 (GT) 评估缺乏空间细节,阻碍了精确的植入手术规划.
- 目前的方法只提供点测量或基本分类,无法捕获3D变化.
- 需要先进的工具来提供全面的空间GT数据.
研究的目的:
- 开发基于卷积束计算机断层扫描 (CBCT) 的3D可视化系统,用于牙厚度评估.
- 利用深度学习创建一个新的牙科植入体手术空间评估工具.
- 通过增强GT评估,提高植入程序的准确性和可预测性.
主要方法:
- 收集了50名患者的CBCT和口内扫描 (IOS) 数据.
- 使用DeepLabV3+架构进行牙和骨组织的语义细分.
- 开发了一个3D可视化算法,将2D切片转换为连续的3D表面,并进行渐变色彩映射.
主要成果:
- 实现了85.92%的语义细分精度.
- 成功构建了牙厚度的3D空间分布模型.
- 证明了毫米精度量化和多角度GT评估,克服了二维限制.
结论:
- 开发的系统将GT评估从定性分析转变为空间定量分析.
- 这种3D可视化工具有助于识别高风险区域进行个性化手术规划.
- 该系统提高了美学和复杂的牙植入案例的可预测性.
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