The Influence of the Extent of Color-Vision Deficiency on Shade-Matching Ability.

OBJECTIVE: To evaluate the influence of the extent of color-vision deficiency on visual shade-matching ability. MATERIALS AND METHODS: Six groups were investigated: the control group (N = 68), the protan medium deficiency (PMED) group (N = 5), the protan strong deficiency (PSTD) group (N = 5), the deutan mild deficiency (DMID) group (N = 5), the deutan medium deficiency (DMED) group (N = 5) and the deutan strong deficiency (DSTD) group (N = 8). The color vision of the participants was evaluated monocularly using the Hardy-Rand-Rittler (HRR) test and on an HMC Anomaloskop MR (Rayleigh test). The final exam on a Toothguide Training Box consisted of 15 lightness-chroma-hue tasks. The color difference (∆E*ab) and the shade-matching score (Σ∆E*ab) were computed. The means and the standard deviations for the Σ∆E*ab were calculated. An independent t-test was used for statistical analyses of the data and a comparison of means (α = .05) for protan groups and a one-way analysis of variance (ANOVA) and a post-hoc Bonferroni test (α = .05) for deutan groups. RESULTS: The PSTD group had a mean Σ∆E*ab of 63.38 ± 9.52, which means their selections were significantly worse in comparison to the PMED group (Σ∆E*ab = 47.62 ± 9.88, p = 0.033). The selections of the control group were significantly better in comparison to all groups with color-vision deficiency (control - PMED, p = 0.031; control - PSTD, p < 0.0001; control - DMED, p < 0.0001; control - DSTD, p < 0.0001), except in comparison with DMID group (p = 0.082). The comparisons between deutan groups were not significantly different (DMID - DMED, p = 0.352; DMID - DSTD, p = 0.323; DMED - DSTD, p = 1.000). CONCLUSION: Participants with strong protan color-vision deficiency are worse at shade matching than participants with medium protan color-vision deficiency.

Do Type 1 Diabetes Mellitus and Color-Vision Deficiencies Influence Shade-Matching Ability?

PURPOSE: To evaluate the effect of color-vision deficiencies and type 1 diabetes mellitus (DM) on visual shade-matching ability. MATERIALS AND METHODS: Four groups of participants were investigated: a control group (n = 68); a group with protanomalia (n = 10); a group with deuteranomalia (n = 19); and a group with type 1 DM (n = 13). Color vision was evaluated monocularly using the Ishihara test, Farnsworth-Munsell 100 hue (FM100H) test, Hardy Rand Rittler (HRR) test, and with an HMC Anomaloskop MR (Rayleigh and Moreland tests). The final exam was on a Toothguide Training Box (TTB) and consisted of 15 lightness-chroma-hue tasks. The color difference (ΔE*ab) and the shade-matching score (ΣΔE*ab) were computed, and the correct lightness (L*), chroma (C*), and hue (h*) selections were counted. The means and standard deviations for the ΣΔE*ab, ΔE*ab, L*, C*, h*, Ishihara, HRR, FM100H, and Rayleigh and Moreland tests were calculated. One-way analysis of variance (ANOVA) and post hoc Bonferroni test were used for statistical analyses and a comparison of means (α = .05). The data analyses were performed using SPSS 22.0 for Windows (IBM). RESULTS: The control group selected the shade tab on the TTB significantly better (ΣΔE*ab = 31.57 ± 13.50) than the group with protanomalia (ΣΔE*ab = 55.50 ± 12.36; P < .0001) and the group with deuteranomalia (ΣΔE*ab = 59.18 ± 16.35; P < .0001), but not significantly better than the group with type 1 DM (ΣΔE*ab = 39.43 ± 11.46; P = .368). The group with type 1 DM selected the shade tab on the TTB significantly better than the group with protanomalia (P = .038) and the group with deuteranomalia (P < .0001). CONCLUSION: Participants with color-vision deficiencies are less accurate at shade matching than the control group and the group with type 1 DM.

Tooth Shade-Matching Ability Between Groups of Students with Different Color Knowledge.

PURPOSE: The aim of this study was to evaluate the effect of gender and knowledge of color in dentistry on the visual shade-matching ability of dental students with no experience in shade matching and without identification skills. MATERIALS AND METHODS: A total of 32 color-normal participants, 16 female (F) and 16 male (M), completed all phases of the experiment. The control group did not listen to a 60-minute lecture (FNL = females that did not listen; MNL = males that did not listen); the other group listened to a lecture about color in dentistry (FL = females that listened; ML= males that listened). The Toothguide Training Box (TTB) (VITA Zahnfabrik) was used. The final exam consisted of a total of 15 lightness-chroma-hue tasks. The correct selection of lightness (L*), chroma (C*), and hue (h*) was observed, as was the computed shade-matching score, ΣΔE*ab, for each participant. Mann-Whitney U test was used for statistical analysis of the data (α = .05) (SPSS 22.0 for Windows [IBM]). RESULTS: Gender was found to play an important role in shade matching. The FL group selected L* better (L* = 12.11) in comparison with the MNL group (L* = 11.00), which is not significantly different (P = .19). The FL group selected L* better in comparison with the ML group (L* = 10.57), which is not significantly different (P = .10). The FNL group selected C* statistically significantly better (C* = 9.86) than did the ML group (C* = 8.57) (P = .016). The shade-matching score, ΣΔE*ab, for group FL (ΣΔE*ab = 22.50) and group ML (ΣΔE*ab = 31.79) was marginally statistically significant (P = .06). CONCLUSION: A 60-minute lecture from the field of color in dentistry has a minimal impact on tooth-shade matching, whereas gender plays an important role.

Does gender and experience influence shade matching quality?

OBJECTIVES: To evaluate the influence of gender and level of experience on shade matching quality. METHODS: A study was simultaneously performed at 15 universities located in 9 countries. A total of 614 color normal participants completed all phases of the experiment. Among them, there were 305 females and 309 males, 319 dental students and 295 dental professionals. A lecture on color matching in dentistry was given to all participants. Initial training was performed using Toothguide Trainer software (TT), while Toothguide Training Box (TTB) was used for both training and testing of participants' shade matching results. The test task was to successively match 15 shade guide tabs with the corresponding shade guide. The shade matching score for each participant was computed as a sum of color differences (SigmaDeltaE(ab)(*) score) between target tabs and selected tabs. Lower scores corresponded to better shade matching results and vice versa. Means and standard deviations were calculated. Mann-Whitney U test was used for statistical analysis of the data (alpha=0.05). RESULTS: The mean shade matching score (S.D.) for all participants was 41 (21). The score for female and male participants was 38 (20) and 44 (21), respectfully (p<0.001). The difference in scores between dental students, 42 (20), and dental professionals, 39 (21), was not statistically significant. CONCLUSION: Within the limitations of this study, females achieved significantly better shade matching results than males, indicating that gender plays an important role in shade matching. The level of experience was not found to be significant factor in shade matching.