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Diffuse reflectance spectroscopy in dental caries detection and classification
Signal, Image and Video Processing ( IF 2.3 ) Pub Date : 2020-01-30 , DOI: 10.1007/s11760-020-01640-4
Jindřich Charvát , Aleš Procházka , Matěj Fričl , Oldřich Vyšata , Lucie Himmlová

Machine learning and augmented reality form very important computational tools in biomedicine, neurology and stomatology as well. The present paper is devoted to a novel method of spectroscopic detection of caries lesions that changes the optical properties of the affected tissue. This method of the diffuse reflectance spectroscopy is used in many biomedical areas even though the analysis of associated data suffers from a large variance of acquired signals’ shape and their properties. The proposed methodology of measured spectra analysis is based upon general methods of signal feature evaluation and the use of computational intelligence for their classification. The paper compares properties of dental feature clusters for the set of 578 tissues with different levels of their changes. Classification results of selected features by the support vector machine, Bayesian method, k -nearest neighbour method and neural network enable to distinguish the healthy tissue and caries lesions with the accuracy from 94.1 to 98.4% and the cross-validation error lower than 8.3%. These results suggest how the augmented reality and general mathematical signal processing methods can be beneficial for diagnostic purposes in dental research and possibly in the clinical practice as well.

中文翻译:

漫反射光谱在龋齿检测和分类中的应用

机器学习和增强现实在生物医学、神经病学和口腔医学领域也形成了非常重要的计算工具。本论文致力于一种改变受影响组织光学特性的龋齿病变的光谱检测新方法。这种漫反射光谱的方法被用于许多生物医学领域,即使相关数据的分析受到采集信号形状及其特性的很大变化的影响。所提出的测量光谱分析方法基于信号特征评估的一般方法和使用计算智能进行分类。该论文比较了具有不同变化水平的 578 个组织的牙齿特征簇的特性。通过支持向量机、贝叶斯方法、k-最近邻方法和神经网络对所选特征的分类结果能够区分健康组织和龋病病变,准确率从94.1%到98.4%,交叉验证误差低于8.3%。这些结果表明增强现实和通用数学信号处理方法如何有益于牙科研究和临床实践中的诊断目的。
更新日期:2020-01-30
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