从口腔照片中检测牙斑和牙炎的深度学习:系统性审查
Mohammad Moharrami1,2,3, Elaheh Vahab4, Mobina Bagherianlemraski5
1Faculty of Dentistry, University of Toronto, Toronto, Canada.
Community dentistry and oral epidemiology
|June 27, 2025
概括
深度学习模型显示,使用口腔内照片检测牙斑和牙炎是有前途的. 这些人工智能工具擅长检测斑块,并可以改善远程牙科和早期疾病查.
科学领域:
- 牙科 牙科是指牙科的专业.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 牙斑和牙炎是口腔健康的关键指标.
- 早期检测对于预防牙周病进展至关重要.
- 内口摄影为口腔健康评估提供了一种非侵入性方法.
研究的目的:
- 系统地审查深度学习 (DL) 模型在检测牙斑块和牙炎方面的表现.
- 用RGB口内照片分析DL模型的方法质量和诊断准确性.
主要方法:
- 在主要数据库 (Medline,Scopus,Embase,Web of Science) 进行全面的文献搜索,截至2025年1月31日.
- 对包括23项研究的方法特征和绩效指标的分析.
- 使用QUADAS-2进行偏差风险评估,使用GRADE框架进行证据确定性评估.
主要成果:
- 深度学习模型在牙斑块细分方面表现强 (IoU 0.64-0.86,中位数为 0.74),在某些情况下表现优于牙医.
- 模型显示了牙炎检测的潜力 (IoU 0.43-0.72,中位数0.63),但与斑块检测相比表现不佳.
- 证据的确定性在斑块检测方面中度,在牙炎检测方面低.
结论:
- 深度学习模型显示出从口内照片中检测牙斑和牙炎的巨大潜力.
- 这些人工智能工具可以与智能手机等可访问设备一起使用,可以推进远程牙科和早期牙周病查.
- 需要进一步的研究来解决局限性问题,例如缺乏外部测试和多中心验证以实现现实应用.
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