基于卷积神经网络的深度学习方法,用于牙科患者的骨生长预测
Miran Hikmat Mohammed1, Zana Qadir Omer2, Barham Bahroz Aziz3
1Department of Basic Sciences, College of Dentistry, University of Sulaimani, Sulaimaniyah 46001, Iraq.
Journal of imaging
|November 26, 2024
概括
深度学习准确地预测了骨生长成熟度,使用椎脊椎成熟度和牙X射线的下2结石化. 这种方法可为男性和女性提供可靠的骨成熟检测.
科学领域:
- 牙科 牙科是指牙科的专业.
- 放射学 放射学是一门学科.
- 人工智能的人工智能
背景情况:
- 准确的骨生长评估对于正牙治疗计划至关重要.
- 评估骨成熟的传统方法可能是耗时和主观的.
- 深度学习为牙科放射图的自动化和精确分析提供了潜力.
研究的目的:
- 使用深度学习 (卷积神经网络 - CNN) 预测体生长成熟度,其中包括椎成熟度 (CVM) 和下2结石化.
- 评估基于CNN的多类分类的准确性,以检测骨成熟阶段的骨造影 (OPG).
- 为了比较CVM和第二结石化之间的预测准确性,用于骨成熟评估.
主要方法:
- 使用了1200张头脑测量放射和1200张OPG的数据集.
- 采用基于CNN的深度学习与多类分类来分析图像.
- 鉴定了椎脊椎成熟指数 (CVMI) 和估计的时间年龄从第二结石化水平.
主要成果:
- 在预测骨成熟阶段和性别方面,CNN模型取得了很高的准确性.
- 宫脊椎的成熟在男性中显示了98%的准确性.
- 在女性中,第二结石表现出高准确度,可靠地用于使用OPG进行生长评估.
结论:
- CNN多类分类是一种准确的方法来检测从CVM和下2结石化中骨成熟的情况.
- 下2结石化是骨生长水平的可靠指标.
- 使用这种深度学习方法来评估骨成熟度,标准的骨造影术 (OPG) 是足够的.
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