先进的深度学习技术用于使用全景X射线图像对牙疾病进行分类
Alireza Golkarieh1, Bahareh Afjehsoleymani2, Kiana Kiashemshaki3
1Department of Computer Science and Engineering, Oakland University, Rochester, MI, USA.
BMC oral health
|February 7, 2026
概括
混合深度学习模型将卷积神经网络 (CNN) 与随机森林分类器结合起来,在全景放射图中显示出在检测牙状况 (如填充物和腔) 中的卓越性能. 这些人工智能工具显示出作为支持性诊断辅助工具的希望,但需要进一步验证与临床专业知识一起.
科学领域:
- 人工智能在牙科中的应用
- 对于医学成像的深度学习
- 放射性牙科诊断 牙科诊断 牙科诊断
背景情况:
- 在全景射线图中自动检测牙科疾病对于有效诊断至关重要.
- 为了完成这个任务,已经探索了各种深度学习模型,包括定制的CNN,混合方法和预训练的架构.
- 评估和比较这些不同的AI方法对于确定最佳解决方案至关重要.
研究的目的:
- 为了比较定制CNN,混合CNN机器学习模型和精心调整的预训练架构的性能,用于检测牙疾病.
- 评估这些模型在从全景X射线中识别填充物,腔,植入物和受冲击的牙方面的有效性.
- 确定用于自动牙科诊断的最准确和最有效的深度学习方法.
主要方法:
- 用于基于AI的分类,使用了1,512张全景X射线和11,137条注释的数据集.
- 通过随机下样管理类不平衡,创建一个平衡的数据集.
- 使用五倍交叉验证来评估定制的CNN,混合模型 (CNN + SVM,决策树,随机森林) 和微调的VGG16,Xception和ResNet50.
主要成果:
- 混合CNN-随机森林模型实现了最高的准确度 (85.4%) 和宏观F1得分 (0.843),超过了定制CNN的11%.
- 在预训练的架构中,VGG16表现最好 (82.3%准确率,0.817宏F1),其次是Xception和ResNet50.
- CNN-随机森林模型在填充物检测方面表现出色 (F1:0.860),但系统的错误分类突出了诊断方面的挑战.
结论:
- 基于CNN的混合方法与随机森林分类器提供了在注释区域中检测牙疾病的优越区分能力.
- 计算效率高的混合模型显示出作为补充诊断工具的潜力.
- 观察到的错误分类强调了人工智能系统需要增强,而不是取代临床判断,需要未来的验证.
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