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Updated: Jun 25, 2025

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
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基于人工智能的新工具用于使用卷积神经网络在CBCT上进行一次性牙细分:一个验证研究.

Sara Elsonbaty1,2,3, Bahaaeldeen M Elgarba1,2,4, Rocharles Cavalcante Fontenele1,2

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概括

人工智能平台在CBCT扫描上准确地划分母牙,比手工扫描快35倍. 这种自动化细分 (AS) 为儿科牙科治疗计划提供专家级准确性和一致性.

关键词:
在CBCT中,CBCT是CBCT.人工智能的人工智能是人工智能.圆束计算机断层扫描技术卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.牛奶牙是一种牙.一个主要的牙主要的牙.

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科学领域:

  • 牙科 牙科是指牙科的专业.
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 在形光束计算机断层扫描 (CBCT) 扫描上精确细分母牙对儿科牙科治疗计划至关重要.
  • 传统的手动细分方法是劳动密集型的,需要专门的专业知识.

研究的目的:

  • 验证基于云的人工智能 (AI) 平台,用于在CBCT扫描中对母牙进行自动细分 (AS).
  • 将人工智能平台的准确性,时间效率和一致性与手动细分 (MS) 进行比较.

主要方法:

  • 使用了来自37次CBCT扫描的402颗母牙的数据集.
  • 手动细分 (MS) 作为地面真相,自动细分 (AS) 在同一平台上执行.
  • 基于voxel和表面的指标,细分时间和类内相关系数 (ICC) 用于比较.

主要成果:

  • 自动细分实现了高精度 (98 ± 1%) 和子相似系数 (DSC; 95 ± 2%).
  • 人工智能平台的速度是手动细分的35倍,平均细分时间为24秒.
  • 无论是MS还是AS都表现出了很好的一致性 (ICC分别为0.99和1).

结论:

  • 人工智能平台为CBCT扫描中的初级牙细分提供专家级准确性.
  • 自动化方法具有高度的时间效率和一致性,大大帮助儿科牙科治疗计划.