The effect of color vision resolution on intra-oral shade-selection accuracy

Abstract

BACKGROUND:

Dentists must be able to identify subtle color changes as shade-matching is crucial in aesthetic dentistry.

OBJECTIVE:

To determine whether color discrimination ability is related to shade-matching accuracy among dentists.

METHODS:

The sensitivity of the normal-color vision population to different colors was investigated using Farnsworth Munsell 100 Hue (FM-100) test results. The FM-100 test was administered to 37 dentists at the Hospital of Stomatology, Jilin University. Sensitivity of dentists with normal-color vision to different colors was investigated using the FM-100 test. Participants were instructed to arrange color caps of various colors according to the gradual change in colors and the results were scored. Visual shade matching test using Vita 3D-MASTER shade guide was performed to determine shade-matching accuracy. The relationship between color discrimination ability and shade-matching accuracy was analyzed. The number of misplaced color caps in the FM-100 test was also calculated.

RESULTS:

The FM-100 test revealed that 16 and 21 participants had excellent and average color discrimination ability, respectively, and their shade-matching accuracies were 68.75% and 66.67%, respectively. No significant difference was observed in the shade-matching accuracy between the two groups. No significant correlation was observed between the color discrimination ability and shade-matching accuracy. In addition, the number of wrong color caps on the 43–63 color tray with the transition from blue-green to blue-purple was the highest according to Friedman’s test.

CONCLUSION:

Color discrimination ability of dentists does not affect their visual shade-matching accuracy. Additionally, people with normal color vision are not sensitive to the transition from blue-green to blue-purple.

Keywords

Color vision cosmetic dentistry tooth preparation color perception tests

1. Introduction

Shade matching is an essential skill for every dentist and is also the key to dental aesthetic restoration. Errors during shade matching may require prosthesis repair, thereby affecting patient satisfaction [1]. Several researchers have developed different shade selection techniques using digital photography. However, these shade-matching approaches are sophisticated and expensive. Therefore, visual shade matching remains the most frequently used method in clinic [2, 3]. Many factors influence visual shade selection, such as natural tooth color, lighting conditions, shade guidance, surrounding environment and the observer [2, 4, 5]. Thus, when performing visual shade selection, it is necessary to use a standardized shade guide in certain settings. Standardized shade guides provide a reliable reference for dentists. Vita Classical and Vita 3D-MASTER shade guides are two of the most commonly used shade guides in clinics. The Vita Classical shade guide is divided into four categories, A, B, C and D, and contains 16 labels. Standard tooth colors are mostly seen in categories A and B, with colors in categories C and D being uncommon [6]. Few colors are available for clinical shade matching in the Vita Classical shade guide.

The Vita 3D-MASTER shade guide has 26 primary color labels in addition to three bleach color labels. Compared to the Vita Classical shade guide, it covers a wider and more uniform color range [7, 8, 9]. In addition, its application is more scientific. While using the shade guide, the brightness is determined first, followed by the chroma, and finally, the saturation is selected [1]. This matching procedure satisfies the clinical shade selection requirements.

It is not difficult for ordinary people with normal color perception to distinguish colors with large differences. However, subtle color differentiation is important for dentists as the color difference between the teeth is very small, and these small differences can have a significant impact on the results of a dental restoration. To meet the prosthetic and restorative requirements of dental patients, it is necessary to match the tooth color using the shade guide. Dentists need to choose the most suitable color from a shade guide that contains many similar colors. Therefore, we aim to determine whether the color discrimination ability could affect the results of dental visual shade matching.

The Farnsworth Munsell 100 Hue (FM-100) test can check the color discrimination ability more accurately than other detection methods. This accurate testing and scoring method is used to detect color perception of individuals. And it can also be used to test the color discrimination ability of the subject. The FM-100 Test device contains several movable color caps that can be divided into four main areas [10]. These areas are red to yellow-green, yellow-green to blue-green, blue-green to blue-purple, and blue-purple to red. The FM-100 test is designed to distinguish between people with normal color vision and those with color vision defects. However, its use in detecting the ability of distinguishing different colors among people with normal color vision has not been studied extensively.

Therefore, we want to know whether dentists with different color discrimination ability have different shade-matching ability. At the same time, we want to study whether individuals have the same ability to distinguish all colors.

We screened dental practitioners with excellent and average color discrimination abilities using the FM-100 test. Then a visual shade-matching test with Vita 3D-MASTER shade guide was carried out in the two groups. On this basis, we analyzed whether the color discrimination ability of the dentists affected their shade-matching accuracy.

In this study, we aimed to determine whether color discrimination ability could affect the results of dental visual shade matching among dentists. On this basis, analyze whether the individual has the same color resolution.

2. Materials and methods

2.1 Participants

A total of 37 dental practitioners were randomly selected from the Hospital of Stomatology, Jilin University. Simple color vision tests were performed to exclude color-blind participants. The study was approved by the ethics review committee.

2.2 Equipment and materials

2.2.1 The FM-100 test and supporting scoring system (Fig. 1a–c)

The FM-100 test instrument contains four color trays, and 93 color caps, of which 85 color caps are removable. Each color cap has a unique numerical sequence number. There are fixed color caps at each end of each color tray as references, and the other movable color caps are arranged in each color tray according to the change in color hue. The color tray containing 85–21 color caps transitions from red to yellow-green, while the one containing 22–42 color caps transitions from yellow-green to blue-green, the one containing 43–63 color caps transitions from blue-green to blue-purple, and the one containing 64–84 color caps transitions from blue-purple to red. The participants were asked to place the color caps on a color tray within a certain time period. The results of participants’ color cap arrangement were entered into the support scoring system to calculate the score and generate an analysis chart. Color discrimination ability was deduced based on the score (Table 1).

Table 1
FM-100 test color discrimination ability evaluation score standard

Evaluation of color discrimination ability	Score range
Excellent color discrimination ability	0–16
Average color discrimination ability	20–100
Poor color discrimination ability	$>$ 100

Figure 1.

FM-100 test and supporting scoring system (USA) and Vita 3D Master shade guide (Vita, Germany). a. The FM-100 test equipment outsourcing; b. The four color trays and 93 color caps of FM-100 test; c. The FM-100 test supporting scoring system interface; d. Vita 3D Master shade guide.

2.2.2 Vita 3D-MASTER shade guide (Vita, Germany) (Fig. 1d)

The Vita 3D-MASTER shade guide contains 26 main color labels and three bleach color labels. Each shade guide is divided into five groups. The lightness values of all the color labels in each group are consistent. The lightness between groups increases gradually from 1 to 5. Group 1 is the brightest and group 5 is the darkest. Each group has color labels named M, L and R, which represent the middle chroma(M), yellow chroma (L) and red chroma (R), respectively. In the middle color column, color saturation gradually increases from top to bottom. Its value ranges from 1 to 3, where 1 is the lightest and 3 is the densest. In Groups 2, 3 and 4, there are two color labels in R or L column, with values of 1.5 and 2.5, respectively. The recording format is “lightness chroma saturation”, for example, “1M2”.

2.3 Researching methods

This study included three experimental parts: the FM-100 test, shade selection test, and resolution analysis of each color region (Fig. 2).

Figure 2.

Experimental flowchart.

2.3.1 Color discrimination ability analysis by FM-100 test

The experiment was conducted from 10 a.m. to 3 p.m. The experimental environment was a natural light source. The color tray of the FM-100 test was selected randomly. The tester scrambled the removable color caps on the color tray and asked the participants to rearrange them. A reminder was provided after 2 minutes if the participants did not complete the arrangement. However, the participants would not be interrupted. Upon completion of the arrangement, the remaining three trays were operated, as described above. After the participants completed the entire arrangement of the four trays, the transparent cover was paced on each color tray. The color trays were then flipped to expose the number at the bottom of each color tray. The numbers were then sequentially entered into the FM-100 hue test matching scoring system. A score of no more than 16 indicates excellent color discrimination ability, 20–100 indicates average color discrimination ability, and more than 100 indicates poor color discrimination ability. After the initial color vision screening, the final scoring results showed that all participants had normal vision and all scores were below 100. Finally, they were divided into two groups: those with excellent color discrimination ability and those with average color discrimination ability. Shade matching tests were performed for the two groups.

2.3.2 Shade selection test

Shade matching tests were conducted for the groups with average and excellent color discrimination ability. Two sets of Vita 3D-MASTER shade guides, named A and B, were used for shade matching. The names of the color labels on shade guide A were blocked using opaque adhesive tapes. Simultaneously, the 2M1 label was removed from shade guide B. The participants were asked to choose a color label in shade guide A with the same color as that of the 2M1 label from shade guide B. Then, the opaque tape was peeled off. The names of the selected color labels were checked. If the colorimetric result was 2M1, it was recorded it as correct; otherwise, we recorded it as incorrect.

2.3.3 Resolution analysis of each color region

In the FM-100 test, the number of false color caps per person arranged in each color tray was calculated and averaged. Finally, the Friedman experiment examined was used to examine whether there was a statistically significant difference in the number of false color caps identification in the four trays.

2.4 Statistical analysis

Use the analysis soft IBM SPSS Statistics 22. Inc., Chicago, IL, USA.

3. Results

3.1 Relationship between color discrimination ability and shade-selection accuracy

A total of 37 participants were included in the test. According to the FM-100 test results, the participants were divided into excellent ( $n=$ 16) and average ( $n=$ 21) color discrimination ability groups. The shade-selection accuracy rate of the excellent color discrimination ability group was 68.75%, and while that of the average color discrimination ability group was 66.67% (Fig. 3). According to Fisher’s exact probability test ( $P>$ 0.05), there was no statistical difference in the vision shade matching accuracy between the excellent and average color discriminators under normal color vision conditions. Pearson’s correlation test ( $P>$ 0.05) indicated that under normal color vision, there was no significant correlation between the accuracy of color discrimination ability and visual shade selection among dentists.

Figure 3.

Distribution diagram of colorimetric results of the excellent color discrimination group and the general color discrimination group. a. There were 16 persons in the excellent color discrimination ability group, and the accuracy rate of shade matching results was 68.75%; b. 21 persons in the ordinary color discrimination ability group. The accuracy rate of shade matching results was 66.67%.

Figure 4.

The report charts of FM-100 test. a. The chart of excellent color discriminator has no error color caps is a complete circle; b. The chart of excellent color discriminator has error color caps. At the point where the blue arrow points, there is a regular peak with a platform. c. The chart of average color discriminator has error color caps. At the point where the red arrow is, there are uneven peaks with a large base area and ups and downs.

Table 2

The number of wrong color caps in each color trays of FM-100 test

Subject	85–21 wrong color caps	22–42 wrong color caps	43–63 wrong color caps	64–84 wrong color caps
1	3	5	6	2
2	2	2	8	6
3	0	0	3	0
4	0	8	2	4
5	0	2	4	4
6	0	2	2	9
7	0	2	2	0
8	2	5	9	2
9	0	4	7	0
10	0	6	7	0
11	2	5	5	0
12	2	6	4	2
13	0	0	2	0
14	0	2	2	0
15	2	5	6	0
16	0	0	2	0
17	2	4	4	4
18	4	2	4	6
19	0	0	2	0
20	9	2	4	2
21	2	2	2	2
22	0	0	0	0
23	0	0	2	0
24	0	4	3	0
25	2	4	2	0
26	2	2	3	4
27	4	10	8	4
28	2	0	6	0
29	6	5	10	2
30	0	6	6	2
31	2	2	4	0
32	0	5	7	4
33	4	9	10	2
34	2	2	4	2
35	0	2	4	2
36	0	2	2	0
37	0	2	4	2
Average number of	1.46	3.22	4.38	1.81
wrong color caps

Table 3

Friedman M test results of the number of color caps errors in the four-color trays

Grouping	Significance	Results
85–21 and 22–42	0.001	$p<$ 0.05
85–21 and 43–63	0.000	$p<$ 0.05
85–21 and 64–84	0.357	$p>$ 0.05
22–42 and 43–63	0.002	$p<$ 0.05
22–42 and 64–84	0.011	$p<$ 0.05
43–63 and 64–84	0.000	$p<$ 0.05

3.2 Discrimination analysis of color regions corresponding for each color tray

According to the analysis and test chart of the FM-100 test, we can see that the report chart of excellent color discriminators with all color caps arranged correctly was a complete circle (Fig. 4a). The report chart of an excellent color discriminator with incorrect color caps showed peaks of different heights. A low peak indicates misplacement of two adjacent caps, while a high peak indicates misplacement of two color caps that were wide apart. In the report chart of average color discriminators, peaks of continuous fluctuations could be seen in the model base, indicating misalignment of continuous color caps. This suggests that, the better the color discrimination ability, the more regular the report chart and the lower the volatility (Fig. 4b and c). Through the analysis chart, the specific position of the participant’s wrong color cap can be observed more intuitively, and it is more convenient to analyze his/her discrimination ability for different colors.

Four color trays are tested in the FM-100 test. The number of error color caps made by each person in each color tray is counted (Table 2). According to the Friedman test, there was no significant difference in the number of wrong color caps between 85–21 and 64–84 color trays, and a significant difference was noted in the number of wrong color caps between the other color trays (Table 3). In addition, 43–63 had the highest number of false color caps, suggesting that the normal population had the weakest resolution to the color transition from blue-green to blue-purple.

4. Discussion

Shade matching has long been clinical challenge for dentists. Correct shade matching is required to obtain an acceptable restoration outcome. Currently, visual shade matching is the most widely used method. It is affected by many factors, such as the environment, light source, shade guide, and psychological state and color perception of the dentists [2, 4, 5, 11]. Researchers are constantly exploring innovative shade selection methods to overcome the influence of changing environmental impact factors. Recent shade selection methods include spectrophotometry, digital photography shade selection, and intraoral scanner shade selection [2, 23, 24]. Spectrophotometers are highly accurate, however, their use is inconvenient [13]. The digital photography shade selection technology method photographs the actual tooth color, but it is easily influenced by illumination and photographing distance during the shade matching procedure [32]. Furthermore, optical shade matching is required for intraoral scanners [30]. In general, when compared to the currently available shade-matching technologies, oral visual shade selection is easy to operate, less expensive, and more stable. Consequently, visual shade selection remains the most widely utilized shade-matching method in clinics. Therefore, in this study, we used the visual shade matching method to assess the shade selection abilities of dental practitioners. Shade guide is an essential tool for visual shade matching. Color segregation of the Vita 3D-MASTER shade guide is more thorough and orderly than that of the Vita Classical shade guide, and has a uniform color distribution [2, 7]. Furthermore, the use of the Vita 3D-MASTER shade guide is more scientific. Therefore, the Vita 3D-MASTER shade guide was chosen for the experiment.

Several studies have shown that sex has no effect on shade matching results [1, 4, 15, 27]. In addition, according to previous studies [16, 26], experience has little effect on the accuracy of shade matching. Therefore, we did not select participants based on sex or age. However, studies have found that oral professionals have greater oral visual shade selection ability than non-oral professionals [28]. According to Capa’s research [17], prosthodontists are more accurate in shade matching than dentists from the other departments. To avoid the influence of these factors, all participants in this study were selected from the Department of Dental and Endodontic Diseases. In addition, light source had a significant impact on shade matching. According to Wee et al. [25], the ambient light of most dental clinics cannot meet the color temperature requirements of shade selection, reducing the consistency and repeatability of the clinicians’ shade selection results. Moreover, the daily consultation room’s window position, dental chair position, weather and patient visiting time may vary. Consequently, ensuring the stability of the natural light source is difficult. To reduce the unpredictability of ambient light sources during the investigation, we ensured that the weather was clear, patient visit time was regulated, and the position was stable. The natural light source used in this experiment was consistent with a clinical visual shade selection light source [12].

After excluding these interfering elements, we focused on the effects of color vision on visual shade selection. It is not difficult for ordinary people with normal color perception to distinguish colors with large differences. However, dentists must have a fine color discrimination ability because the color difference they distinguish throughout the shade-matching procedure is more subtle.

The FM-100 test can also be used in the color-related industries [29]. When compared to other color vision tests, the FM-100 test can not only evaluate color vision defects, but also grade persons with normal color vision’s color discriminating abilities. The FM-100 test can be used under a natural light source, in accordance with the standards of oral visual shade selection. Therefore, we determined the dental practitioners’ color discrimination ability using the FM-100 test and subsequently examined their visual shade-matching accuracy.

In experimental results, the oral shade-selection accuracy rate of the excellent color discrimination ability group was 68.75%, and that of the average color discrimination ability group was 66.67%. According to our results, there was no correlation between color discrimination ability and oral visual shade matching accuracy under normal color vision. Therefore, dentists with average color discrimination ability do not need to spend more time on specific shade selection training. This finding has certain clinical significance. However, some studies have reported that training can improve the accuracy of shade selection to some extent [18, 19, 20, 21], implying that dentists can improve their colorimetric skills through regular training. However, even without this systematic training, dentists would not make significant errors in shade selection. In addition, according to the two sets of data, the accuracy of visual shade selection was relatively low. A study has shown that shade matching devices are more repeatable than visual shade selection [5], therefore visual shade matching and shade matching devices can be combined to further improve the accuracy of shade selection.

According to our study results, there was a significant difference in the number of wrong color caps between the blue-green to blue-purple color tray and the other three trays in the FM-100 test. This indicates that people with normal color perception have poor resolution for the transition from blue-green to blue-purple. This finding has a certain guiding significance for clinical practice. Blue and purple colors indicate the short-wavelength visible light. Therefore, these light sources should be avoided during tooth shade matching. Furthermore, a previous found that [31], with a blue background, the shade selection quality is much lower. This suggests that a blue background should be avoided when selecting visual shades in clinical practice.

The number of participants in this study is limited. During the experiment, we have tried our best to keep the light source consistent. The next study will expand the number of subjects, and use a standard light source box to simulate sunlight and eliminate interference.

5. Conclusion

There was no evident relationship between color discrimination ability and visual shade-matching accuracy. Furthermore, individuals with normal color perception have poor resolution for the transition from blue-green to blue-purple colors.

Ethics approval

The study was approved by the local ethics committee (no. 202046).

Informed consent

Informed consent was obtained from all individual participants included in the study.

Funding

The study was funded by the Health Commission of Jilin Province (no. 2021TC028).

Footnotes

Conflict of interest

The authors declare that they have no competing interests.

References

Alnusayri

Sghaireen

Mathew

Alzarea

Bandela

. Shade selection in esthetic dentistry: A review. Cureus. 2022; 14(3): e23331.

Liberato

Barreto

Costa

de Almeida

Pimentel

Tiossi

. A comparison between visual, intraoral scanner, and spectrophotometer shade matching: A clinical study. Journal of Prosthetic Dentistry. 2019; 121(2): 271-5.

Chu

Trushkowsky

Paravina

. Dental color matching instruments and systems. Review of clinical and research aspects. J Dent. 2010; 38(Suppl 2): e2-16.

Imbery

Killough

Baechle

Hankle

Janus

. An evaluation of factors affecting dental shade matching in first-year dental students. The Journal of Prosthetic Dentistry. 2021.

Reyes

Acosta

Ventura

. Repeatability of the human eye compared to an intraoral scanner in dental shade matching. Heliyon. 2019; 5(7): e02100.

da Costa

Fox

Ferracane

. Comparison of various resin composite shades and layering technique with a shade guide. J Esthet Restor Dent. 2010; 22(2): 114-24.

Hassel

Zenthöfer

Corcodel

Hildenbrandt

Reinelt

Wiesberg

. Determination of VITA Classical shades with the 3D-Master shade guide. Acta Odontol Scand. 2013; 71(3-4): 721-6.

Gómez-Polo

Quispe López

Portillo Muñoz

Montero

. 3D master toothguide is adequate to subjective shade selection? Medicina (Kaunas). 2022; 58(3).

Ahn

Lee

. Color distribution of a shade guide in the value, chroma, and hue scale. J Prosthet Dent. 2008; 100(1): 18-28.

10.

Thyagarajan

Moradi

Membrey

Alistair

Laidlaw

. Technical note: The effect of refractive blur on colour vision evaluated using the Cambridge Colour Test, the Ishihara Pseudoisochromatic Plates and the Farnsworth Munsell 100 Hue Test. Ophthalmic Physiol Opt. 2007; 27(3): 315-9.

11.

Clary

Ontiveros

Cron

Paravina

. Influence of light source, polarization, education, and training on shade matching quality. Journal of Prosthetic Dentistry. 2016; 116(1): 91-7.

12.

Liu

Chen

Pointer

Liu

Khanh

. Extending the color discrimination metric with consideration of illuminance level. Opt Lett. 2022; 47(7): 1851-4.

13.

Kalantari

Ghoraishian

Mohaghegh

. Evaluation of accuracy of shade selection using two spectrophotometer systems: Vita easyshade and degudent shadepilot. Eur J Dent. 2017; 11(2): 196-200.

14.

Garg

Raura

Arora

Shenoy

Thomas

. Effect of ocular dominance, clinical experience, and sex on the accuracy of shade selection. Quintessence Int. 2022; 53(4): 320-7.

15.

Aswini

Ramanarayanan

Rejithan

Sajeev

Suresh

. The effect of gender and clinical experience on shade perception. J Esthet Restor Dent. 2019; 31(6): 608-12.

16.

Nakhaei

Ghanbarzadeh

Amirinejad

Alavi

Rajatihaghi

. The influence of dental shade guides and experience on the accuracy of shade matching. The Journal of Contemporary Dental Practice. 2016; 17(1): 22-6.

17.

Capa

Malkondu

Kazazoglu

Calikkocaoglu

. Evaluating factors that affect the shade-matching ability of dentists, dental staff members and laypeople. J Am Dent Assoc. 2010; 141(1): 71-6.

18.

Samra

APB

Moro

Mazur

Vieira

De Souza

Freire

, et al. Performance of dental students in shade matching: Impact of training. J Esthet Restor Dent. 2017; 29(2): E24-e32.

19.

Olms

Jakstat

. Learning shade differentiation using toothguide trainer and toothguide training box: A longitudinal study with dental students. Journal of Dental Education. 2016; 80(2): 183-90.

20.

Ristic

Stankovic

Paravina

. Influence of color education and training on shade matching skills. J Esthet Restor Dent. 2016; 28(5): 287-94.

21.

Chakrabortty

Wadkar

Mustaffa

Gupta

. An analysis of an educational method to improve the shade-matching ability of dental students. J Prosthet Dent. 2021; 125(5): 815.e1-.e9.

22.

Özat

Tuncel

Eroğlu

. Repeatability and reliability of human eye in visual shade selection. J Oral Rehabil. 2013; 40(12): 958-64.

23.

Ferreira Dias

Lourenço Silveira

Nunes Pereira

Cardoso

Duarte Sola Pereira da Mata

da Silva Marques

. CIEL*a*b* values in vita classical and vita 3d master by two dental spectrophotometers. Int J Prosthodont. 2021.

24.

Jorquera

Atria

Galán

Feureisen

Imbarak

Kernitsky

, et al. A comparison of ceramic crown color difference between different shade selection methods: Visual, digital camera, and smartphone. J Prosthet Dent. 2021.

25.

Wee

Meyer

Wichman

. Lighting conditions used during visual shade matching in private dental offices. J Prosthet Dent. 2016; 115(4): 469-74.

26.

Garg

Raura

Arora

Shenoy

Thomas

. Effect of ocular dominance, clinical experience, and sex on the accuracy of shade selection. Quintessence Int. 2022; 53(4): 320-7.

27.

Garg

Raura

Arora

Shenoy

Thomas

. Effect of ocular dominance, clinical experience, and sex on the accuracy of shade selection. Quintessence Int. 2022; 53(4): 320-7.

28.

Simionato

Pecho

Della Bona

. Efficacy of color discrimination tests used in dentistry. J Esthet Restor Dent. 2021; 33(6): 865-73.

29.

Lovell

Rabin

. Hue discrimination in jewellery appraisers exceeds normal performance. Ophthalmic Physiol Opt. 2021; 41(5): 1119-24.

30.

Akl

Mansour

Zheng

. The Role of Intraoral Scanners in the Shade Matching Process: A Systematic Review. J Prosthodont. 2022.

31.

Dudea

Gasparik

Botos

Alb

Irimie

Paravina

. Influence of background/surrounding area on accuracy of visual color matching. Clin Oral Investig. 2016; 20(6): 1167-73.

32.

Tabatabaian

Beyabanaki

Alirezaei

Epakchi

. Visual and digital tooth shade selection methods, related effective factors and conditions, and their accuracy and precision: A literature review. J Esthet Restor Dent. 2021; 33(8): 1084-104.

The effect of color vision resolution on intra-oral shade-selection accuracy

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSION:

Keywords

1. Introduction

2. Materials and methods

2.1 Participants

2.2 Equipment and materials

2.2.1 The FM-100 test and supporting scoring system (Fig. 1a–c)

Table 1 FM-100 test color discrimination ability evaluation score standard

2.3 Researching methods

2.3.2 Shade selection test

2.3.3 Resolution analysis of each color region

2.4 Statistical analysis

3. Results

3.1 Relationship between color discrimination ability and shade-selection accuracy

4. Discussion

5. Conclusion

Ethics approval

Informed consent

Funding

Footnotes

Conflict of interest

References

Table 1
FM-100 test color discrimination ability evaluation score standard