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The structure of a crystalline solid, whether a metal or not, is best described by considering its simplest repeating unit, which is referred to as its unit cell. The unit cell consists of lattice points that represent the locations of atoms or ions. The entire structure then consists of this unit cell repeating in three dimensions. The three different types of unit cells present in the cubic lattice are illustrated in Figure 1.
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Improving teeth aesthetics using a spatially shared-parameters model for independent regular lattices.

Rui Martins1,2,3, Jorge Caldeira1,2,3, Inês Lopes4

  • 1Centro de Investigação Interdisciplinar Egas Moniz (CiiEM), Almada, Portugal.

Journal of Applied Statistics
|June 16, 2022
PubMed
Summary

Dental treatments aim for high teeth gloss by minimizing surface roughness. This study reveals that surface roughness variability, not just average roughness, significantly impacts final tooth gloss, offering a new statistical approach for dental research.

Keywords:
62F1562H1162P10Independent latticesR-NIMBLEshared variancespatial modelteeth glossteeth roughness

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Area of Science:

  • Dental materials science
  • Surface characterization
  • Biostatistics

Background:

  • Tooth gloss is a critical aesthetic and functional outcome in restorative dentistry.
  • Achieving optimal gloss involves careful selection of resins and polishing protocols to minimize surface roughness.
  • Current methods often analyze mean roughness, potentially overlooking other crucial surface characteristics.

Purpose of the Study:

  • To evaluate the impact of four polishing protocols and two resins on dental surface roughness and gloss.
  • To develop and apply a novel statistical model linking surface roughness variability to tooth gloss.
  • To identify the most effective treatment combination for maximizing tooth gloss.

Main Methods:

  • Utilized atomic force microscopy (AFM) for precise *in vitro* measurement of dental surface surrogate roughness.
  • Employed a shared parameters statistical approach to model the relationship between roughness and gloss.
  • Incorporated spatial structured random effects and within-treatment variance to capture roughness heterogeneity.

Main Results:

  • Surface roughness variability, not solely mean roughness, was found to be a key determinant of achieved tooth gloss.
  • The developed statistical model provided deeper insights than traditional two-way ANOVA.
  • Specific polishing protocols and resin combinations demonstrated differential impacts on gloss, influenced by roughness characteristics.

Conclusions:

  • The variability in surface roughness plays a central role in explaining differences in tooth gloss among various dental treatments.
  • This study introduces a more nuanced statistical framework for analyzing dental surface properties and their effect on aesthetic outcomes.
  • Understanding roughness heterogeneity is crucial for optimizing resin-polishing protocols to achieve superior dental gloss.