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深度学习用于虚拟的牙科支架移除:工具的建立和应用.

Ruomei Li¹, Cheng Zhu¹, Fengting Chu¹

¹Department of Orthodontics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, 500 Quxi Road, Shanghai, 200011, China.

Clinical oral investigations

|January 27, 2024

概括

一个新的深度学习工具使虚拟正牙支架能够高精度和速度移除,即使没有原始牙数据. 这项技术有助于评估支架的定位,并可能在解锁之前设计正牙器件.

关键词:

人工智能的人工智能直接粘合技术是一种直接粘合技术.神经网络的神经网络的神经网络 ортодонтический支架位置评估评估支架位置.ортодонтик的 (((s) 的一个)

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科学领域:

生物医学工程生物医学工程
人工智能在牙科中的应用
矯正牙科矯正牙科是一種矯正牙科.

背景情况:

准确的支架放置对于成功的正牙治疗至关重要.
目前用于支架评估的方法可能耗时,可能需要物理操作.
在正牙科中,对先进的数字工具的需求正在增长.

研究的目的:

开发一种基于深度学习的工具,用于虚拟的正统牙科支架移除.
为了评估该工具在从结合的牙中提取特征的准确性和效率.
为了证明其在评估支架位置和偏差时的应用.

主要方法:

使用978颗结合牙的数据集训练了一个细分网络.
该工具的性能在一个单独的数据集上验证了118个结合的牙.
像临床冠状中心和支架方向等特征被提取用于分析.

主要成果:

虚拟支架去除的时间为每颗牙2.9毫秒.
这些数据集的高准确率为98.93%和97.42%.
该工具成功分析了支架的角度和分布,揭示了牙周医生的偏好.

结论:

开发的工具是高效的,精确的,并且在不需要原始牙数据的情况下运行.
它有效地可视化粘合偏差用于支架位置评估.
潜在的应用包括避免不必要的支架移除和预制造正牙器件.