Color difference thresholds for computer‐simulated human Gingiva

Abstract

The data on visual thresholds for healthy human gingiva can be used as quality control tool/guide for selection and evaluation of dental materials, interpretation of color-related findings in clinical dentistry and research, and for standardization in dentistry. It is of particular value that this study was designed based on in-vivo color evaluation of healthy keratinized gingiva of subjects of different ethnicities, age groups, and gender.

Gingival shade guides: Colorimetric and spectral modeling

“…Repeated evaluations leaving part of the data aside, guarantees the aforementioned performance on any new unseen gingiva data. None of the shade guide models provided CE lower than or equal to PTs for gingival color . However, achieving CE at or below PT is not a realistic goal or objective in dentistry.…”

“…Although an overall CE was not reported, the CEs were in all cases greater than AT for gingival color. Furthermore, the reported ME represented the extremely unacceptable color differences . A third study reported on color of healthy gingiva of a total of 362 subjects .…”

“…Different shade guide sizes (containing 1 to 10 clusters) were designed; CE and ME were used to evaluate their performance for each of the studied Groups. Finally, the optimal number of tabs in computer designed gingival shade guides was selected based on comparisons with the 50%:50% Acceptability Thresholds (AT) for gingival color: CIEDE2000 AT = 2.8; CIELAB AT = 3.7 …”

“…The AT of = 4.6 and = 4.1 have been reported . In a more recent study, PT and AT for computer‐simulated samples of human gingiva were determined using both CIELAB (PT = 1.7; AT = 3.7) and CIEDE2000 (PT = 1.1; AT = 2.8) color difference formulas …”

“…23 In a more recent study, PT and AT for computer-simulated samples of human gingiva were determined using both CIELAB (PT 5 1.7; AT 5 3.7) and CIEDE2000 (PT 5 1.1; AT 5 2.8) color difference formulas. 25 The objective of this study was to compute models of colorimetric and spectral gingival shade guides that adequately represent the color of human gingiva for a multiracial and varied age cohort. A novel clustering optimization method aiming to minimize CE and at the same time the largest color difference (ME) for the gingiva dataset was proposed.…”