基于深度学习的系统,用于自动分阶段下结成长的下结成长
Fernando Biskupovic1, Flavia Rosenberg2, Luz María Searle2
1Undergraduate Orthodonctic Program, Facultad de Odontología, Universidad de los Andes, Chile; Graduate Orthodontic Program, Facultad de Odontología, Universidad de los Andes, Chile.
Journal of the World federation of orthodontists
|October 10, 2025
概括
这项研究开发了一种深度学习系统,用于自动评估牙发育阶段,用于牙脸整形. Inception模型显示出高准确度,有助于在增长评估中的临床决策.
科学领域:
- 牙面部整形外科 牙面部整形外科
- 机器学习在医学中的应用
- 放射学分析 放射学分析
背景情况:
- 评估患者的成熟状态对于dentofacial骨科治疗计划至关重要.
- 根据德米尔吉安的方法,牙的发育阶段表明了骨的成熟度.
- 自动化这种评估可以提高效率和正牙干预措施.
研究的目的:
- 开发和评估用于自动化牙成熟阶段评估的深度学习模型.
- 为了比较不同卷积神经网络 (CNN) 架构的性能.
- 为了验证模型在生长评估中的临床适用性.
主要方法:
- 一项横截面研究分析了1805张全景放射图.
- 四个CNN架构 (Xception,ResNet,MobileNet,Inception) 在下第二和第三结成熟阶段进行了训练.
- 模型性能被评估在组合,只有第二,只有第三数据集;Grad-CAM可视化注意力.
主要成果:
- 启动模型在组合数据集上达到最高准确度 (0.96),在第二个摩尔数据集上达到最高准确度 (0.98).
- 在第三个摩尔数据集上,ResNet表现最好 (准确率为0.96).
- 高水平的相互审查者协议 (kappa=0.94) 和相关结构重点得到了Grad-CAM.的证实.
结论:
- 深度学习,特别是Inception模型,准确地分类牙成熟阶段.
- 该系统显示强烈同意专家评估.
- 这种人工智能工具可以支持 ортодонтической增长评估的临床决策.
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