人工智能在头脑测量点检测中使用的算法的准确性:系统性审查
Júlia Ribas-Sabartés1, Meritxell Sánchez-Molins1, Nuno Gustavo d'Oliveira1
1Departamento de Odontoestomatología, Facultad de Medicina y Ciencias de la Salud, Universidad de Barcelona, Campus Bellvitge, 08097 L'Hospitalet de Llobregat, Barcelona, Spain.
Bioengineering (Basel, Switzerland)
|January 8, 2025
概括
在正牙科中,人工智能 (AI) 显示出对X射线上头脑计点定位的前景. 卷积神经网络 (CNN) 是最有效的AI算法,尽管正牙医仍然更准确.
科学领域:
- 矯正牙科 矯正牙科是一種矯正牙科.
- 人工智能的人工智能
- 放射性成像成像 放射性成像成像
背景情况:
- 人工智能 (AI) 越来越多地被用于正牙科,用于诸如头脑计点定位等任务.
- 对传统方法进行AI系统的评估对于临床采用至关重要.
研究的目的:
- 为了确定最有效的AI算法用于2DX射线的头脑测量里程碑检测.
- 分析与这些AI算法相关的学习系统.
主要方法:
- 在主要的科学数据库 (PubMed-MEDLINE,Cochrane,Scopus,IEEE Xplore,Web of Science) 中进行了全面的文献搜索.
- 包括的研究 (2013-2023) 集中在检测至少13个2D射线图中的头测量地标,不包括复杂的病例.
- 使用QUADAS-2和NOS工具评估了偏差风险.
主要成果:
- 13项研究符合纳入标准,其中7项被评为低风险.
- 卷积神经网络 (CNN) 显示出高精度 (64.3%97.3%),平均误差在临床范围内.
- YOLOv3显示性能有所改善,CNNs在地标检测方面被证明是最有效的.
结论:
- 基于CNN的人工智能算法在正牙放射学中对头度点定位非常有效.
- 虽然快速和可复制,但当前的AI准确性尚未与经验丰富的牙科专家相匹配.
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