基于人工智能的真实性,手动和全球值对人类下的细分协议
Andrea Kristine T Hernandez1, Vinicius Dutra2, Tien-Min G Chu3
1Dentmanila Inc, Makati City, Philippines.
概括
基于人工智能 (AI) 的细分显示了对一些下部区域的手动方法的可比真实性,并且优于全球值. 这种人工智能方法提供了可接受的细分精度,作为传统协议的替代方案.
科学领域:
- 口腔和牙面部外科手术
- 牙科成像 牙科成像 牙科成像
- 3D 建模 3D 建模
背景情况:
- 精确的下的3D模型对于牙科手术至关重要.
- 束计算机断层扫描 (CBCT) 数据的细分是创建这些模型的关键步骤.
- 比较不同的细分方法,包括人工智能 (AI),手动和全球值,对于优化准确性至关重要.
研究的目的:
- 为了比较基于AI,手动和全球值细分协议的真实性,用于3D下模型.
- 评估AI细分的准确性与使用黄金标准表面扫描的既定方法相比.
主要方法:
- 使用CBCT扫描了12个干燥的人类下.
- 用全球值,手动和基于AI (Diagnocat) 的协议进行了细分.
- 通过结构光表面扫描仪生成3D模型并使用网状比较软件与参考模型进行比较.
- 计算了根平均平方 (RMS) 误差以量化真实性.
主要成果:
- 以人工智能为基础的细分表明,对于整个下,膜过程和身体的手动细分,RMS误差明显高于手动细分.
- 相反,基于人工智能的细分显示,与手动细分相比,孔底和的RMS误差明显较低.
- 与手动和基于AI的方法相比,全球值始终在大多数下部区域产生显著更高的RMS误差.
- 在基于人工智能和人工细分之间的RMS误差没有显著差异,用于冠状体过程,交合体和下角.
结论:
- 基于AI的细分提供了比全球值更真实的3D模型.
- 基于AI的细分精度与特定下部区域的手动细分相当.
- 人工智能细分为牙科3D建模提供了手动和全球值协议的可行和准确的替代方案.
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