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Diffuse reflectance spectroscopy in dental caries detection and classification

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Abstract

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.

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Acknowledgements

This research was supported by grant projects of the Ministry of Health CR (FN HK 00179906) and the Charles University at Prague, Czech Republic (PROGRES Q40), as well as by the project PERSONMED – Centre for the Development of Personalized Medicine in Age-Related Diseases, Reg. No. CZ.02.1.010.00.017 0480007441, co-financed by the European Regional Development Fund (ERDF) and the governmental budget of the Czech Republic. All procedures were approved by the local ethics committee as stipulated by the Helsinki Declaration.

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Authors and Affiliations

  1. Department of Stomatology, The First Faculty of Medicine, Charles University, 121 08, Prague, Czech Republic

    Jindřich Charvát & Lucie Himmlová

  2. Department of Computing and Control Engineering, University of Chemistry and Technology in Prague, 166 28, Prague, Czech Republic

    Aleš Procházka & Matěj Fričl

  3. Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, 166 36, Prague, Czech Republic

    Aleš Procházka

  4. Department of Neurology, Faculty of Medicine in Hradec Králové, Charles University in Prague, 500 05, Hradec Králové, Czech Republic

    Aleš Procházka & Oldřich Vyšata

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  1. Jindřich Charvát
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  2. Aleš Procházka
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  5. Lucie Himmlová
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Correspondence to Aleš Procházka.

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Charvát, J., Procházka, A., Fričl, M. et al. Diffuse reflectance spectroscopy in dental caries detection and classification. SIViP 14, 1063–1070 (2020). https://doi.org/10.1007/s11760-020-01640-4

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  • DOI: https://doi.org/10.1007/s11760-020-01640-4

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