基于面部平面反射对称网的自动三维面部对称参考平面构造
Yujia Zhu1, Lingxiao Zhang2, Shuzhi Liu2
1Center of Digital Dentistry/Department of Prosthodontics, Peking University School and Hospital of Stomatology, Beijing, China; National Center for Stomatology, Beijing, China; National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing, China; Beijing Key Laboratory of Digital Stomatology, Beijing, China; NHC Key Laboratory of Digital Stomatology, Beijing, China.
Journal of dentistry
|May 12, 2024
概括
一个新的深度学习模型,面部平面反射对称网 (FPRS-Net),自动构建3D面部对称参考平面. 这种人工智能工具显著减少了牙科应用中的分析时间和对专家判断的依赖.
科学领域:
- * 口腔和大面部手术
- * 医学中的人工智能
- * 3D 面部人类测量
背景情况:
- * 三维 (3D) 面部对称性分析依赖于对称性参考平面 (SRP).
- *人工智能 (AI) 越来越多地被用于牙科和口腔科学.
- *自动化SRP构建对于高效的数字工作流程至关重要.
研究的目的:
- * 开发一种新的深度学习模型,面部平面反射对称网 (FPRS-Net),用于自动3D面部SRP构建.
- *建立用于定义3D点云感兴趣区域 (ROI) 和AI模型的特征计算的方法.
- *评估开发的AI模型的临床适用性和准确性.
主要方法:
- * 开发了四个FPRS-Net模型,使用全面部和ROI数据的监督和无监督学习.
- *分别在160个和20个3D面部数据集上训练并验证了模型.
- *在40个临床3D面部数据集上评估模型性能,使用平均平方误差和角度误差,通过视觉模拟尺度 (VAS) 进行专家评估.
主要成果:
- * FPRS-Net模型在预测3D面部SRP方面取得了很高的准确性,平均角度误差低至0.84° (FPRS-NetSR) 和0.99° (FPRS-NetU).
- *使用VAS的专家评估表明,对预测的SRPs (>8) 的满意度很高.
- *40个案例的SRP构建使用FPRS-Net模型在不到4秒内完成.
结论:
- * 该研究成功地展示了一种新的AI驱动解决方案,用于在口腔临床环境中自动化3D面部SRP构建.
- * FPRS-Net模型在减少数字牙科设计工作量和时间方面取得了重大进展.
- *这种人工智能方法尽量减少对专家经验的依赖,提高治疗效率和有效性.
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