Das Gupta Shuvashis, Killenberger Markus, Tanner Tarja, Rieppo Lassi, Saarakkala Simo, Heikkilä Jarkko, Anttonen Vuokko, Finnilä Mikko A J
Research Unit of Medical Imaging, Physics and Technology, University of Oulu, 90220 Oulu, Finland.
Research Unit of Oral Health Sciences, Department of Cariology, Endodontology and Pediatric Dentistry, University of Oulu, 90220 Oulu, Finland.
Analyst. 2021 Mar 8;146(5):1705-1713. doi: 10.1039/d0an01938k.
Dental caries is the most common oral disease that causes demineralization of the enamel and later of the dentin. Depth-wise assessment of the demineralization process could be used to help in treatment planning. In this study, we aimed to provide baseline information for the development of a Raman probe by characterizing the mineral composition of the dental tissues from large composition maps (6 × 3 mm2 with 15 μm step size) using Raman microspectroscopy. Ten human wisdom teeth with different stages of dental caries lesions were examined. All of the teeth were cut in half at representative locations of the caries lesions and then imaged with a Raman imaging microscope. The pre-processed spectral maps were combined into a single data matrix, and the spectra of the enamel, dentin, and caries were identified by K-means cluster analysis. Our results showed that unsupervised identification of dental caries is possible with the K-means clustering. The compositional analysis revealed that the carious lesions are less mineralized than the healthy enamel, and when the lesions extend into the dentin, they are even less mineralized. Furthermore, there were more carbonate imperfections in the mineral crystal lattice of the caries tissues than in healthy tissues. Interestingly, we observed gradients in the sound enamel showing higher mineralization and greater mineral crystal perfection towards the tooth surface. To conclude, our results provide a baseline for the methodological development aimed at clinical diagnostics for the early detection of active caries lesions.
龋齿是最常见的口腔疾病,它会导致牙釉质随后还有牙本质脱矿。对脱矿过程进行深度评估可用于辅助治疗规划。在本研究中,我们旨在通过使用拉曼光谱法从大型成分图(6×3平方毫米,步长15微米)表征牙齿组织的矿物成分,为开发拉曼探针提供基线信息。检查了十颗处于不同龋齿病变阶段的人类智齿。所有牙齿均在龋齿病变的代表性位置切成两半,然后用拉曼成像显微镜成像。将预处理后的光谱图组合成一个单一数据矩阵,并通过K均值聚类分析识别牙釉质、牙本质和龋齿的光谱。我们的结果表明,使用K均值聚类可以对龋齿进行无监督识别。成分分析表明,龋损部位的矿化程度低于健康牙釉质,当病变扩展到牙本质时,矿化程度更低。此外,龋损组织矿物晶格中的碳酸盐缺陷比健康组织更多。有趣的是,我们观察到完好牙釉质中存在梯度,朝着牙齿表面矿化程度更高且矿物晶体更完美。总之,我们的结果为旨在早期检测活跃龋损病变的临床诊断方法开发提供了基线。