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可解释的CNN-Radiomics融合和组合学习用于牙科X射线图中的多模式损伤分类

Zuhal Can¹, Emre Aydin¹

¹Computer Engineering Department, Engineering and Architecture Faculty, Eskisehir Osmangazi University, Eskisehir 26040, Türkiye.

Diagnostics (Basel, Switzerland)

|August 28, 2025

概括

这项研究引入了人工智能框架,将深度学习和放射学融合在一起,以改善牙科放射图中的根端疾病检测,实现高精度和透明度.

科学领域:

在牙科领域的人工智能
医学成像分析
放射学和深度学习融合

背景情况:

在诊断根末部疾病时, 周围的X射线非常重要.
射线图的解释错误限制了诊断的准确性.
需要更好的,可靠的牙周诊断工具.

研究的目的:

开发一种可解释的多式人工智能框架,用于增强根端疾病检测.
将深层卷积神经网络 (CNN) 嵌入与放射性纹理描述器进行融合.
使用Grad-CAM和SHAP值提供双层可解释性.

主要方法:

通过六个CNN架构处理的2285个周围X线图的数据集.
通过Grad-CAM热图识别的病变相关像素提取了放射性特征.
融合了CNN的嵌入和放射性特征, 将它们输入到随机森林或XGBoost分类器中.
使用五视图测试时间增强 (TTA) 进行增强的推断.

主要成果:

原始CNN的准确度达到了52%和AUC达到了0.60.
多模式融合显著提高了性能,达到95. 4%的精度和0. 9867AUC.
使用TTA的顶部组合模型实现了97.16%的准确性和0. 9914 AUC.
在Grad-CAM中强调了周边地区,而SHAP值则证实了共同特征的贡献.

结论:

拟议的人工智能框架集成了CNN嵌入和面具向放射学,用于最先进的病变检测.
双层可解释性 (Grad-CAM + SHAP) 提高了人工智能预测的透明度和信任度.
这种多模式方法解决了根端疾病的诊断准确性和解释性的局限性.

关键词:

放射性核聚变美国深度学习牙科的X光片尺寸缩小可解释性周围病变的检测辐射学测试时间的增加

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