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相关概念视频

Teeth01:15

Teeth

707
The formation of teeth, also known as odontogenesis, is a complex process that begins in utero, around the sixth week of embryonic development. There are three stages to this process: the bud stage, the cap stage, and the bell stage.
In the bud stage, the tooth germ (an aggregation of cells) starts to form in the developing jawbone. During the cap stage, the tooth germ differentiates into enamel organ, dental papilla, and dental sac, which will later develop into the tooth's enamel, dentin...
707

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相关实验视频

Updated: Sep 14, 2025

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
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牙科中的机器学习:一个范围审查.

Shrey Lakhotia1, Hormazd Godrej2, Amandeep Kaur3

  • 1Helios Enter Data Warehouse IT Exp., Henry Ford Health System, Detroit, Michigan, United States of America.

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牙科中的机器学习 (ML) 显示出诊断和治疗的前景,但许多研究缺乏方法论严谨性. 改进模型验证,偏差评估和可重复性对于在牙科护理中采用真实世界的AI至关重要.

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科学领域:

  • 牙科信息学 牙科信息学
  • 机器学习应用程序 机器学习应用程序
  • 医疗人工智能 医疗人工智能

背景情况:

  • 人工智能 (AI),特别是机器学习 (ML),在牙科应用中越来越多地用于诊断,预后和治疗计划.
  • 尽管广泛采用,但缺乏对牙科中这些ML模型的方法质量和报告完整性的全面评估.

研究的目的:

  • 进行关于牙科ML的已发表文献的范围审查.
  • 使用TRIPOD + AI标题评估ML模型的方法完整性和报告质量.
  • 确定牙科应用的ML研究中的关键缺口和改进领域.

主要方法:

  • 2018年1月1日至2023年12月31日期间发表的PubMed索引文章的范围审查,使用任何牙科专业的ML.
  • 研究使用TRIPOD + AI检查清单进行评估,重点关注数据预处理,模型验证和临床绩效报告.
  • 确定了1506篇文章,其中280篇符合详细分析的纳入标准.

主要成果:

  • 口腔和大面部放射学,外科和一般牙科是最受代表的专业.
  • 很大一部分研究 (22.9%) 没有将其模型与临床参考标准或现有模型进行比较.
  • 常见的局限性包括偏差评估不足,异常值报告不足,校准评估不足,可重复性低,数据访问受限制.

结论:

  • 虽然ML具有牙科护理的变革潜力,但成功临床实施需要在模型校准评估和公平评估方面进行重大改进.
  • 未来的研究应该优先提高错误的解释性,异常报告,可重复性,公平性评估,并进行前性验证研究.