IEEE J Biomed Health Inform. 2015 Mar;19(2):571-80. doi: 10.1109/JBHI.2014.2317503. Epub 2014 Apr 15.
Dental restoration begins with removing carries and affected tissues with air-turbine rotary cutting handpieces, and later restoring the lost tissues with appropriate restorative materials to retain the functionality. Most restoration materials eventually fail as they age and need to be replaced. One of the difficulties in replacing failing restorations is discerning the boundary of restorative materials, which causes inadvertent removal of healthy tooth layers. Developing an objective and sensor-based method is a promising approach to monitor dental restorative operations and to prevent excessive tooth losses. This paper has analyzed cutting sounds of an air-turbine handpiece to discriminate between tooth layers and two commonly used restorative materials, amalgam and composite. Support vector machines were employed for classification, and the averaged short-time Fourier transform coefficients were selected as the features. The classifier performance was evaluated from different aspects such as the number of features, feature scaling methods, classification schemes, and utilized kernels. The total classification accuracies were 89% and 92% for cases included composite and amalgam materials, respectively. The obtained results indicated the feasibility and effectiveness of the proposed method.
牙科修复始于使用空气涡轮旋转切割手机去除龋坏和受影响的组织，然后使用适当的修复材料来恢复丢失的组织以保留其功能。随着时间的推移，大多数修复材料最终会失效，需要更换。更换失败的修复体的困难之一是辨别修复材料的边界，这会导致无意中去除健康的牙层。开发一种客观的、基于传感器的方法是监测牙科修复操作和防止过度牙损失的有前途的方法。本文分析了空气涡轮手机的切割声音，以区分牙层和两种常用的修复材料，汞合金和复合树脂。支持向量机用于分类，选择平均短时傅里叶变换系数作为特征。从特征数量、特征缩放方法、分类方案和使用的核函数等不同方面评估了分类器的性能。包含复合树脂和汞合金材料的病例的总分类准确率分别为 89%和 92%。结果表明，所提出的方法具有可行性和有效性。
