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Micron-scale human enamel layer characterization after orthodontic bracket debonding by intensity-based layer segmentation in optical coherence tomography images.

ABSTRACT: In clinical orthodontic practice, fixed brackets are widely used for tooth movement and adjustments. Although years of research and development have improved the workability of fixed orthodontic brackets, there are still controversies regarding its plausible destructive influence on the enamel surface of tooth. This, in turn, makes the quantitative assessment of the enamel surface after specific orthodontic treatment procedures important in order to opt for the most effective treatment procedure. Through this study, we show the practical applicability of optical coherence tomography (OCT) as a non-ionizing and nondestructive assessment tool for measuring enamel loss after each step of orthodontic bracket bonding. Two-dimensional and volumetric OCT images are used for the evaluation of the tooth enamel. From the depth intensity profile analysis of cross-sectional OCT images, the changes in the individual internal layer thickness are calculated. A software algorithm was developed to evaluate the structural connectivity in the enamel for analyzing enamel loss on the tooth surface and for detecting enamel abrasion. An intensity-based layer segmentation algorithm is also developed to analyze and evaluate enamel wear in the tooth after each step. Using the proposed algorithms, the total enamel present after each treatment procedure was measured and tabulated for analysis.

SUBMITTER: Ravichandran NK

PROVIDER: S-EPMC8149424 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundTermination of fixed orthodontic treatment is associated with bracket debonding and residual adhesive removal. These procedures increase enamel roughness to a degree that should depend on the tool used. Enamel roughening may be associated with bacterial retention and staining. However, a very limited data exists on the alteration of 3D enamel roughness resulting from the use of different tools for orthodontic clean-up.Aims1. To perform a precise assessment of 3D enamel surface roughness resulting from residual adhesive removal following orthodontic debonding molar tubes. 2. To compare enamel surfaces resulting from the use of tungsten carbide bur, a one-step polisher and finisher and Adhesive Residue Remover.Material and methodsBuccal surfaces of forty-five extracted human third molars were analysed using a confocal laser microscope at the magnification of 1080× and 3D roughness parameters were calculated. After 20 s etching, molar tubes were bonded, the teeth were stored in 0.9% saline solution for 24 hours and debonded. Residual adhesive was removed using in fifteen specimen each: a twelve-fluted tungsten carbide bur, a one-step finisher and polisher and Adhesive Residue Remover. Then, surface roughness analysis was repeated. Data normality was assessed using Shapiro-Wilk test. Analysis of variance (ANOVA) was used to compare between variables of normal distribution and for the latter-Kruskal-Wallis test.ResultsSa (arithmetical mean height) was significantly different between the groups (p = 0, 01326); the smoothest and most repeatable surfaces were achieved using Adhesive Residue Remover. Similarly, Sq (root mean square height of the scale-limited surface) had the lowest and most homogenous values for Adhesive Residue Remover (p = 0, 01108). Sz (maximum height of the scale-limited surface) was statistically different between the groups (p = 0, 0327), however no statistically significant differences were found concerning Ssk (skewness of the scale-limited surface).DiscussionConfocal laser microscopy allowed 3D surface analysis of enamel surface, avoiding the limitations of contact profilometry. Tungsten carbide burs are the most popular adhesive removing tools, however, the results of the present study indicate, that a one step polisher and finisher as well as Adhesive Residue Remover are less detrimental to the enamel. This is in agreement with a recent study based on direct 3D scanning enamel surface. It proved, that a one-step finisher and polisher as well as Adhesive Residue Remover are characterized by a similar effectiveness in removing residual remnants as tungsten carbide bur, but they remove significantly less enamel.ConclusionOrthodontic debonding and removal of adhesive remnants increases enamel roughness. The smoothest surfaces were achieved using Adhesive Residue Remover, and the roughest using tungsten carbide bur.

Project description:BackgroundMicroarray image analysis processes scanned digital images of hybridized arrays to produce the input spot-level data for downstream analysis, so it can have a potentially large impact on those and subsequent analysis. Signal saturation is an optical effect that occurs when some pixel values for highly expressed genes or peptides exceed the upper detection threshold of the scanner software (2(16) - 1 = 65, 535 for 16-bit images). In practice, spots with a sizable number of saturated pixels are often flagged and discarded. Alternatively, the saturated values are used without adjustments for estimating spot intensities. The resulting expression data tend to be biased downwards and can distort high-level analysis that relies on these data. Hence, it is crucial to effectively correct for signal saturation.ResultsWe developed a flexible mixture model-based segmentation and spot intensity estimation procedure that accounts for saturated pixels by incorporating a censored component in the mixture model. As demonstrated with biological data and simulation, our method extends the dynamic range of expression data beyond the saturation threshold and is effective in correcting saturation-induced bias when the lost information is not tremendous. We further illustrate the impact of image processing on downstream classification, showing that the proposed method can increase diagnostic accuracy using data from a lymphoma cancer diagnosis study.ConclusionsThe presented method adjusts for signal saturation at the segmentation stage that identifies a pixel as part of the foreground, background or other. The cluster membership of a pixel can be altered versus treating saturated values as truly observed. Thus, the resulting spot intensity estimates may be more accurate than those obtained from existing methods that correct for saturation based on already segmented data. As a model-based segmentation method, our procedure is able to identify inner holes, fuzzy edges and blank spots that are common in microarray images. The approach is independent of microarray platform and applicable to both single- and dual-channel microarrays.

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Micron-scale human enamel layer characterization after orthodontic bracket debonding by intensity-based layer segmentation in optical coherence tomography images.

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