Reflectance and colorimetric modelling of multilayer bodies of translucent dental materials

Laburpena

Natural teeth represent one of the most complex biological structures, since they consist of at least three layers of different translucent tissues. In this sense, in order to be able to match all natural teeth, a wide variety of differently shaded materials is required. In the last decade, the development of biomaterials used in dental restorations has experienced an important breakthrough. New generations of dental biomaterials already outperform the mechanical properties of the biological tissues that are meant to replace, however this variety of shades and materials together with their complex optical properties, makes it difficult to design layered restorations that match the perception of the according natural tooth. The appearance of a material may vary significantly depending on a wide range of properties such as surface topology, geometry, reflectance, transmittance and angle from which the material is viewed as well as the illumination parameters (angle of incident light, diffuse or directed illumination, etc.). In this sense, color and appearance are still determining factors that must be well-managed both in clinical practices and dental industries. Therefore, the research studies in the field of esthetic dentistry have increased considerably. In addition to knowing the optical and colorimetric properties of these biomaterials and dental structures, it is also interesting to be able to model and predict them. In the last 20 years, computational intelligence has been an indispensable and transversal tool in research in all areas of science including dentistry. The development of mathematical algorithms that are able to establish, or even predict, the final chromatic coordinates or reflectance spectra of these materials represents an important breakthrough with direct application in clinical practice and industry. In this context, many different predictive techniques such as fuzzy logic, neural networks, the Kubelka-Munk theory or linear and non-linear regression approaches have attempted to solve color prediction of these layered tooth structures and materials in the dental field. However, it remains an area not fully solved yet. It is clear that color management in dentistry is not a trivial problem and there is no simple solution for it. Therefore, the main objective of this PhD Thesis is to measure, model and predict the colorimetric properties and final appearance of translucent layered biomaterials with application in dentistry, in order to provide new color prediction methods that could be applied in both clinical practice and industry, and that could contribute to the progress of knowledge in the field of esthetic dentistry and to the development of new dental materials and prosthetic teeth. To meet this objective, several studies, based on different mathematical techniques, have been carried out in order to develop and test new color prediction algorithms. From this point, this PhD Thesis is structured in 7 chapters. First, in Chapter 1, an extensive state of the art on the different topics related to this PhD Thesis is provided. Starting with some color basics concepts and their relation to color in dentistry. After that, we dug into details on dental restorative materials, ending with an introduction to color prediction in dentistry and different mathematical concepts, highlighting principal components analysis (PCA) and linear regression Analysis. In Chapter 2, both general and specific objectives of this PhD Thesis are drawn. In Chapters 3 and 4, PCA-based reflectance prediction algorithms are developed for monolithic and layered (stacked) dental materials, respectively. Chapter 5 presents a new linear regression-based color prediction algorithm for monolithic and layered (stacked) dental materials. Afterwards, in Chapter 6, the algorithms previously proposed for stacked layered samples are tested with more complex stratified layered samples. Chapter 7 shows the final conclusions of our studies. Finally, all the references cited throughout this memory, as well as the scientific production and activities derived from the studies presented in this PhD Thesis and developed within the doctoral period, are listed.