在Spee评估曲线上的临床进展:深度学习用于Invisalign中自动检测牙里程碑
Songyang Ma1, Yang Liu2, Yue Zhao3
1Department of Orthodontics, Stomatological Hospital of Chongqing Medical University, Chongqing, China; Chongqing Key Laboratory of Oral Disease and Biomedical Sciences, Chongqing, China; Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, Chongqing, China.
概括
一种新的深度学习方法从口腔内扫描中准确地测量了Spee曲线 (COS),提高了效率. 这项技术有助于预测正义牙科治疗结果,特别是对于具有不同骨模式的患者.
科学领域:
- 牙科 牙科是指牙科的专业.
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 斯皮曲线 (COS) 对于评估闭塞功能和正牙结果至关重要.
- 从口内扫描进行传统的COS测量是手动的,耗时的,容易出现操作员错误.
- 使用深度学习的自动化COS评估提供了更高的效率和准确性.
研究的目的:
- 引入基于深度学习的方法,用于完全自动化的速度曲线评估.
- 从口内扫描数据提高COS测量的效率.
- 评估在Invisalign治疗期间,在具有多种垂直骨模式的患者中,COS平衡的可预测性.
主要方法:
- 一个结构意识的长期短期记忆网络被训练在194个下弓模型上进行自动化COS测量.
- 一种两阶段的方法检测了粗和细的牙标志,以确定COS深度.
- 基准准确度通过平均辐射误差和成功检测率进行评估;COS深度通过配对的威尔科克森测试进行评估.
主要成果:
- 深度学习网络实现了高精度,近似手动测量,平均辐射误差为0.32 ± 0.09毫米,用于地标检测.
- 对于COS深度的中位数测量误差为0.05 mm (P <0.001).
- 治疗的可预测性因骨模式而异 (超分歧:69%,低分歧:58%),第一个状挤出精度为65%.
结论:
- 深度学习为自动化速度曲线测量提供了有效的工具.
- 在设定COS等级目标时,ClinCheck治疗计划应考虑垂直骨模式.
- 需要特别注意的是,与闭合平面相比,第一个牙的挤出.
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