混合两级CNN用于检测和分期牙周炎在全景放射图上
Rini Widyaningrum1,2, Eha Renwi Astuti1, Adioro Soetojo3
1Oral and Maxillofacial Radiology Specialist Study Program, Faculty of Dental Medicine, Universitas Airlangga, Jl. Mayjen Prof. Dr. Moestopo No.47, Surabaya, 60132, Indonesia.
Journal of oral biology and craniofacial research
|September 10, 2025
概括
这项研究引入了一种混合深度学习模型,用于从全景X射线诊断牙周病. 人工智能在诊断牙周炎的准确性达到了80%,显示出作为临床诊断工具的希望.
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:
- 牙周病导致牙损失,原因是牙支组织的慢性炎症.
- 准确的诊断依赖于临床和放射性评估,全景放射图对于严重程度的分期至关重要.
- 深度学习,特别是卷积神经网络 (CNN) 和密集卷积网络 (DenseNet),提供先进的图像分析功能.
研究的目的:
- 评估一种混合的两阶段CNN模型,将Mask R-CNN和DenseNet169.9集成在一起.
- 为了评估模型在检测和分期牙周炎的性能,使用全景射线图.
主要方法:
- 用于培训,验证和测试的600张全景射线图的数据集,补充了100张外部射线图.
- 附注包括牙细分,放射性骨损失 (RBL),水泥结 (CEJ) 和牙周炎分期 (第1-4阶段).
- 面膜R-CNN对牙,CEJ和RBL进行了细分;DenseNet169分类了牙周炎的阶段.
主要成果:
- 混合CNN模型在外部测试组上实现了0.88的特异性和0.80的精度,用于牙周炎的分期.
结论:
- 混合两级CNN显示出作为一种AI驱动的牙周炎诊断辅助器的潜力.
- 进一步的研究可以提高这种深度学习方法的临床适用性和准确性.
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