Objectives: Determine visual 50:50% color difference acceptability thresholds (AT) for regions of the dental color space with varying chromaticity.
Methods: A 40-observer panel belonging to two different groups (dentists and laypersons) evaluated 144 dental resin composites pairs (divided in three different sets of 48 pairs according to chroma value: Low Chroma (LC), Medium Chroma (MC) and High Chroma (HC) placed 40 cm away and inside of a viewing cabinet (D65 Standard light source; diffuse/0° geometry). A Takagi-Sugeno-Kang (TSK) fuzzy approximation was used for fitting the data points and calculate the 50:50% acceptability thresholds in CIEDE2000. A paired t-test was used to evaluate the statistical significance between thresholds differences and Bonferroni correction was applied.
Results: The CIEDE2000 50:50% AT were ∆E = 2.84, ∆E = 2.31 and ∆E = 1.80 for LC, MC and HC sets of sample pairs, respectively. The 50:50% AT values were statistically significant between the different sets of sample pairs, as well as the 50:50% AT values obtained for different observer groups.
Conclusions: 50:50% CIEDE2000 acceptability thresholds for dentistry are significantly different depending on the chromaticity of the samples. Observers show higher acceptability for more achromatic samples (low chroma value) than for more chromatic samples.
Clinical Significance: The difference in the AT for distinct regions of the dental color space can assist professionals as a quality control tool to assess clinical performance and interpret visual and instrumental findings in clinical dentistry, dental research, and subsequent standardization processes.
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http://dx.doi.org/10.1111/jerd.13153 | DOI Listing |
J Esthet Restor Dent
January 2025
Department of Prosthodontic, Faculty of Dentistry, Cukurova University, Adana, Turkey.
Objective: The purpose of this study was to investigate how cigarette smoking affects the surface roughness (R) and stainability of additively and subtractively manufactured resins.
Materials And Methods: Two additively manufactured definitive resins (Dentafab, DF and Formlabs, FL) and a subtractively manufactured resin nanoceramic (Cerasmart, CS) were used to fabricate 60 specimens (14 × 12 × 1 mm). After taking baseline R and color measurements (ΔE), they were divided into two groups (n = 10).
J Esthet Restor Dent
January 2025
State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chengdu, China.
Objective: To investigate how surface treatment affects the color of enamel and dentin, and to evaluate whether the color differences are acceptable.
Materials And Methods: Freshly extracted premolars were prepared using diamond burs (blue, red, and yellow tapes). Tooth surfaces were divided into control and acid-etched areas and treated with phosphoric acid (5, 15, 30, 45, and 60 s).
Food Sci Biotechnol
January 2025
Department of Food Science and Technology, Ohio State University, Columbus, OH 43210 USA.
This review examines analytical methodology for food flavor analysis. Traditionally, flavor chemistry research has relied on sensory-guided chromatography techniques to identify individual compounds responsible for aroma or taste activity. Among the over 12,000 volatile compounds identified in foods, hundreds have been linked to aroma characteristics, and many taste-active compounds have also been discovered.
View Article and Find Full Text PDFJ Exp Biol
January 2025
Program in Ecology, Evolution, and Conservation, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
Eggshell recognition in parental birds is vital for nest management, defense against brood parasitism, optimal embryonic development, and minimizing disease and predation risks. This process relies on acceptance thresholds balancing the risk of rejecting own eggs against the benefit of excluding foreign ones, following signal detection theory. We investigated the role of object shape in egg rejection decisions among three host species of the brown-headed cowbird (Molothrus ater), each with a varying known response to parasitic eggs.
View Article and Find Full Text PDFInt Dent J
January 2025
Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, Indiana, USA. Electronic address:
Objective: This study aimed to predict long-term growth-related changes in skeletal and dental relationships within the craniofacial complex using machine learning (ML) models.
Materials And Methods: Cephalometric radiographs from 301 subjects, taken at pre-pubertal (T1, age 11) and post-pubertal stages (T2, age 18), were analysed. Three ML models-Lasso regression, Random Forest, and Support Vector Regression (SVR)-were trained on a subset of 240 subjects, while 61 subjects were used for testing.
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