The role of lighting type in dental photography for tooth shade assessment

The role of lighting type in dental photography for tooth shade assessment | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search The role of lighting type in dental photography for tooth shade assessment View ORCID Profile Leszek Szalewski , View ORCID Profile Dorota Wójcik , View ORCID Profile Oskar Tokarczuk , Tuana Ozdas , Gabriela Durlej doi: https://doi.org/10.1101/2025.05.14.25327573 Leszek Szalewski 1 Medical University of Lublin Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Leszek Szalewski For correspondence: leszek.szalewski{at}umlub.pl Dorota Wójcik 1 Medical University of Lublin Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Dorota Wójcik Oskar Tokarczuk 1 Medical University of Lublin Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Oskar Tokarczuk Tuana Ozdas 1 Medical University of Lublin Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gabriela Durlej 1 Medical University of Lublin Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Introduction Accurate tooth shade assessment is fundamental for achieving optimal aesthetic outcomes in restorative dentistry. This study aims to evaluate the impact of different lighting setups on the reliability and consistency of dental photography for shade selection. Materials and Methods Three distinct lighting conditions were employed to capture standardized dental photographs: bare twin flash, twin flash with softbox, and twin flash with a polarizing filter. Shade assessments were performed through digital analysis of the photographs and were compared with reference measurements obtained using a spectrophotometer. Result Preliminary findings indicate that the twin flash with a polarizing filter provides the most accurate tooth color reproduction. However, statistically significant differences were observed when compared to the spectrophotometer measurements. Notably, the closest match to the spectrophotometric values was achieved with light shades (A1, B1) photographed against a black background. Conclusions The choice of lighting setup significantly affects the accuracy of tooth shade assessment in dental photography. Among the tested configurations, the use of a polarizing filter combined with a black background demonstrates the highest fidelity in color reproduction, particularly for lighter shades. Introduction The color of teeth plays a pivotal role for both patients aspiring to enhance the aesthetic appeal of their smiles and dental professionals striving for precise color matching in restorative procedures [ 1 ]. A thorough understanding of color-related phenomena enables clinicians to achieve superior therapeutic outcomes. Historically, the concept of color in dentistry was viewed as complex, challenging to interpret, evaluate, communicate, and replicate. This perspective shifted in 1904 when Munsell introduced a systematic color notation framework, establishing a three-dimensional color space characterised by lightness (value, the brightness of a color), hue (the perceived color, such as red or green), and chroma (the color’s saturation or purity) [ 2 ]. Munsell’s model assigned numerical values to these attributes and used physical reference samples to assess the color of objects [ 3 ]. Subsequently, in 1931, the International Commission on Illumination (CIE) established universal standards for color measurement [ 4 ]. These standards utilized three coordinates to define colour within a color space, based on systematic consideration of the light source, the observed object, and the observer [ 5 ]. The human eye, capable of discerning subtle differences between colors in side-by-side comparisons [ 6 ], achieves color perception through three types of cone cells, each tuned to specific wavelength ranges of visible light: short wavelengths (380–500 nm, blue), medium wavelengths (430–600 nm, green), and long wavelengths (530–700 nm, red). The CIE introduced the concept of a “standard observer,” representing an average human’s sensitivity to light across the visible spectrum, effectively linking color perception to specific wavelength responses (XYZ is as an analogue of RGB). The tristimulus values (XYZ) of an object are calculated by multiplying the object’s spectral reflectance (representing reflected light wavelengths) by the spectral distribution of a standard illuminant (derived from the light source’s relative spectral power) and the standard observer’s tristimulus values (XYZ) [ 4 ]. This approach has led to the development of instruments such as tristimulus cameras and colorimeters, which utilize transmittance spectra to replicate color coordinates. These devices employ photodetectors to analyze the visible spectrum in digital imaging systems [ 7 ]. In 1940, Beckman et al. developed a spectrophotometer capable of analyzing the reflection and transmission properties of objects as a function of wavelength. This advancement provided precise data from the spectral curves, allowing for accurate quantification of CIE color attributes [ 8 ]. Over the decades, spectrophotometers have become integral tools for colour evaluation in dentistry, offering high accuracy and reliability [ 9 ]. These instruments measure the intensity of light reflected from an object at intervals ranging from 1 to 25 nanometers across the visible spectrum [ 10 ]. A spectrophotometer includes a light source, a dispersion mechanism, an optical measuring system, a detector, and a system to convert light data into analyzable signals. In clinical dentistry, spectrophotometric data is processed and interpreted to align with the shade guide standards, facilitating conversion to the equivalent shade tabs [ 11 ]. Compared to subjective visual assessments or traditional methods, spectrophotometers have been shown to improve color match accuracy by 33%, achieving objective matches in 93.3% of cases [ 12 ]. In contemporary dental practice, digital photography has become an indispensable tool, extensively used for case documentation and communication [ 13 ]. Consumer-grade digital and video cameras capture visual data using red, green, and blue light sensors, which are then processed to create a full-color image. Although digital cameras offer a simplified means of electronic shade matching, they still rely on subjective interpretation by the human eye to some extent [ 14 ]. High-quality digital images enable precise shade matching by allowing evaluation of multiple areas of a tooth, particularly when paired with appropriate imaging software. This approach is advantageous due to its objectivity, affordability, convenience, ability to evaluate multiple points, streamlined patient record management, and enhanced communication with dental laboratories [ 13 ]. To capture optimal images of teeth, smiles, and surrounding soft tissues, facilitating more accurate diagnoses, better color matching, and identification of fine diagnostic details, dental photography often incorporates accessories such as specialized lamps, contrastors, polarizing filters, retractors, and tripods [ 15 ]. Furthermore, digital images are easily stored and transmitted electronically, streamlining consultations and communication. Despite these benefits, dental photography does have limitations. The quality of images is highly influenced by the specifications of the camera and lens, while challenges such as color casting must be meticulously managed to ensure precision [ 13 ]. Aim of the study The main objective of the study was to evaluate the accuracy of reproducing the actual color of the anterior teeth using dental photography, compared to the results obtained with a spectrophotometer. The secondary objective of the study was to evaluate whether there are specific conditions for dental photography that improve the likelihood of achieving clinically acceptable tooth color reproduction, as assessed by the ΔE 2000 parameter, in comparison to the actual color. Materials and methods The study used a Nikon D750 camera equipped with a Nikkor FX 105mm micro 2.8 lens and Godox MF12 K2 twin flash units. All photographs were captured in a darkened room with ambient lighting turned off, employing modelling lamps integrated into flash units for focussing purposes. A constant magnification ratio of 1:1.4 was maintained, corresponding to a distance of 19 cm from the sample. White balance calibration was achieved using a grey card. Exposure settings remained consistent across all images: aperture f/25, ISO 100, and shutter speed of 1/125 s. The flash power output was adjusted according to the light modifiers used: 1/8 power for flashes without modifiers, 1/4 power with FixLite softboxes, and full power when polarizing filters (Intra.Photos) were applied. Half of the photos were taken with a tripod, and the other half were taken handheld by the operator. Color measurements of the captured images were performed in the CIELab color space using ColorChecker software on a MacBook Pro. A standard VITA shade guide containing 10 shades (A1, A2, A3, A4, B1, B2, C3, C4, D3 and D4) was used in the study. The selected shades represent the full spectrum of human tooth colouration, ranging from very light (B1) to very dark (C4, D4). Reference measurements of the standard samples were conducted using a VITA MASTER spectrophotometer in the CIELab color space. The values obtained in this system were subsequently converted to their corresponding dental shades (A1, A2, A3, etc.). Reference values in the CIELab system were established as follows: L = 81.0; a = 2.3; b = 20.0. Subsequently, dental photographs of each tooth shade were taken under various combinations of lighting conditions, backgrounds, and photographic techniques, resulting in a total of 1,200 measurements. The lighting variable was assessed under three conditions: twin flash units without light modifiers (TF group), twin flash units with FixLite softboxes (SB group), and twin flash units with polarizing filters (FP group). Background variations included two types: black and white, while the exposure variable encompassed two photography techniques: tripod-mounted and handheld shots. Each shade from the VITA shade guide was photographed 10 times under each combination of lighting, background, and exposure method, yielding 120 measurements for each shade. Color difference assessments (ΔE) between reference measurements and the values obtained from dental photographs were conducted based on formulas (1) and (2), reflecting older and more contemporary standards [ 16 ]. where: ( L ref − L ) represents the difference in lightness between e reference color and the measured value, Δ L ′ ( a ref − a ) denotes the difference on the red-green axis, ( b ref − b ) denotes the difference on the yellow-blue axis. Formula 2 represents a more recent standard that incorporates additional parameters that affect the perception of color differences by the human eye, such as scaling functions and rotation coefficients. Due to its complexity, this formula requires the use of computer algorithms for calculations: where: Δ L ′-lightness difference between the reference color and the measured color Δ C ′-chroma difference between the reference color and the measured color, Δ H ′-hue difference between the reference color and the measured color, k L , k C , k H – rotation coefficient. S L , S C , S H – scaling coefficient, R T – scaling functions for lightness, chroma and hue, The ΔE results were interpreted based on the scale presented in Table 1 . View this table: View inline View popup Download powerpoint Table 1. Interpretation of ΔE results [ 16 ]. Statistical analysis The significance level of the statistical tests was set at α = 0.05. The distribution of numerical variables was characterized using descriptive statistics. For this purpose, measures of central tendency, represented by the mean, and measures of dispersion, represented by the standard deviation (SD), were employed. Categorical variables were described using frequency counts. Bland Altman analysis To evaluate the agreement between the two methods of measuring color differences (ΔE 2000 and ΔE 1976), the Bland-Altman method was applied [ 17 ]. Mean delta L, delta C, delta H In this study, Formula 2 was applied to evaluate color differences, incorporating additional standardization of the results for assessing color discrepancies. Perceptual thresholds for the Formula 2 parameters were adopted according to Ghinea et al. [ 18 ], defined as the “threshold 50/50,” representing the boundary values at which 50% of observers perceive a difference between two colors. The threshold values were established as follows: L′ < |2.44| for lightness, ΔC′ < |3.15| for chroma, and ΔH′ < |3.24| for hue. Values below these thresholds were considered clinically acceptable. A desired clinical outcome was defined as achieving a total color difference of ΔE 2000 < 2, indicating a difference that could only be perceived by an experienced observer. Firth Logistic regression To estimate the magnitude and direction of the effects of individual parameters involved in dental photography on achieving the desired clinical outcome (ΔE 2000 < 2), the Firth logistic regression model [ 19 ] was applied. We selected Firth’s logistic regression model due to the low frequency of the target outcome, which occurred in only 17 out of 1,200 observations, representing a prevalence of approximately 1.4%. The full model specification is provided in the Supplement. Model fit was assessed using the likelihood ratio test (LRT) and the Wald z-test, while the explained variance was determined by McFadden’s R 2 coefficient. Effect sizes were expressed as odds ratios (OR) along with 95% confidence intervals, which were estimated using the profile likelihood method. The 95% CI and p-values were evaluated using the asymptotic approximation of the Wald z-test statistics. Statistical environment Analyses were conducted using the R Statistical language (version 4.4.1; R Core Team, 2024) on Windows 11 pro 64 bit, using the packages: ggstatsplot (version 0.9.3)[ 20 ], ggplot2 (version 3.4.0) [ 21 ], readxl (version 1.3.1) [ 22 ] and dplyr (version 1.1.2) [ 23 ], effectsize (version 0.8.3) [ 24 ] and psych (version 2.1.6) [ 25 ], sjPlot [ 26 ], logistf [ 27 ], viridis [ 28 ], ggrepel [ 29 ], colorspace [ 30 ]. Results Descriptive Statistics Reference Values The reference values were obtained as the average of four measurements taken with a spectrophotometer for each shade, and their summary is presented in Table 2 . View this table: View inline View popup Download powerpoint Table 2. Reference values for L, a, b obtained from spectrophotometric measurements of VITA shade guide samples. Mean and Standard Deviation of Delta E 2000 The lowest Delta E 1976 values (0.62) were observed for the A1 shade photographed with a polarizing filter on a tripod against a black background. All clinically significant results (ΔE 2000 < 2) were achieved with a polarizing filter on a tripod against a black background (0.49 - 1.17) and handheld (1.67 - 1.84). For the ΔE 2000 results, the lowest values were obtained for the A1 shade photographed on a black background with a polarizing filter on a tripod (0.42 - 0.91). Clinically significant results were obtained for the A1 shade with a polarizing filter on a black background (1.73 - 1.87). The lowest mean value of ΔE 2000 was observed for the A1 shade and photographs taken on a black background with a polarizing filter on a tripod (0.55) ( Table 3 ). View this table: View inline View popup Table 3. Mean and SD for ΔE 2000 categorized by photographic method, background color, and type of flash. Mean ΔL, ΔC, and ΔH The aim of the analysis was to assess the parameters of lightness (|ΔL′| < 2.44), chroma (|ΔC′| < 3.15), and hue (|ΔH′| < 3.24). Clinically significant cut-off points were based on the results obtained by Ghinea (threshold 50/50), which define the boundary for the acceptability of color differences. Lightness Difference (ΔL′): Positive values indicate a lighter sample compared to the reference, while negative values indicate a darker sample. Chroma Difference (ΔC′): Positive values signify a higher colour saturation of the sample relative to the reference, while negative values indicate lower saturation. Hue Difference (ΔH′): Positive values indicate a hue shift in the clockwise direction in the ab plane of the CIELAB color space, while negative values indicate a shift in the counterclockwise direction. Delta L The analysis revealed that only a subset of the photographic parameter combinations met the clinical criterion for acceptability regarding lightness (|ΔL′| < 2.44). It was observed that better lightness reproduction was achieved in photos taken against a black background compared to a white background, both when using a tripod and handheld (detailed results are provided in Table 3 in the supplement). The highest values of |ΔL′|, exceeding the threshold of 2.44, were observed for photos taken against a white background using a tripod with a polarizing filter (A4, C3, D3, D4 colors) and a softbox (D3, D4 colors). Among the tested colors, only A4 was lighter than the reference, while the others exhibited a tendency to darken. The largest deviations in ΔL′ towards darkening were observed on a white background for dark colors with dual flash (A4, C3, C4, D3, D4) and polarizing filter (C4, D3, D4). Delta C Clinically acceptable differences in color saturation were more frequently observed in photographs taken against a black background, regardless of whether a tripod was used or the camera was handheld. The worst color saturation reproduction occurred for the B1 color photographed with dual-plane lighting on a white background with a tripod and the D4 color photographed using a polarizing filter on a white background handheld. Delta H The analysis of hue reproduction indicated that ΔH′ values fell below the clinical acceptability threshold (|ΔH′| < 3.24) only for the A1 shade, regardless of the combination of lighting, background, and photographic method. The largest ΔH′ deviations were observed for handheld photos on a white background with dual flash (A2, A3, B2, C3-C4, D3-D4 colors) and softbox (A2-D4 colors), as well as on a black background with handheld softbox (A4, C4, D4 colors). For tripod-based photography, significant ΔH′ deviations occurred on a white background with dual flash (A2, A3, B2-D4 colors) and softbox (A2-A3, C3, C4, D4 colors), and on a black background with softbox (A2-A3, C3-D4 colors). All observed ΔH′ values were positive, indicating a hue shift in the clockwise direction on the color wheel relative to the reference color. The results suggest that clinically acceptable colour reproduction in terms of lightness, saturation, and hue is achievable, particularly for the A1 color when using a polarizing filter, regardless of the background color or camera stabilization method employed. The Bland Altman analysis results in Figure 1 revealed a systematic bias, indicating that ΔE1976 consistently produces higher values than ΔE2000. Bias 1.73, UL – 0,28 and LL -3,74. ( Table 4 ). Most points fall within the agreement range. For high ΔE 2000 values, starting from approximately 9, the differences between the methods exceed the lower limit of agreement, indicating larger discrepancies for the shades C4, D3, and D4. Both methods, except for the colours C4, D3, and D4, can be used interchangeably. View this table: View inline View popup Download powerpoint Table 4. Summary of Bland-Altman analysis for ΔE1976 vs ΔE2000 comparison Parameter Value 95% CI Download figure Open in new tab Figure 1. Average ΔL′, values under different photographic conditions for analyzed shades. Download figure Open in new tab Figure 2. Average ΔC′ values under different photographic conditions for analyzed shades. Download figure Open in new tab Figure 3. Average ΔH′ values under different photographic conditions for analyzed shades. Download figure Open in new tab Figure 4. Bland-Altman Plot: agreement between ΔE 2000 and ΔE 1976. Download figure Open in new tab Figure 5. ΔE 2000 values by photographic conditions on black backdrop - tripod mounted camera. Download figure Open in new tab Figure 6. ΔE 2000 values by photographic conditions on black backdrop - handheld camera. Download figure Open in new tab Figure 7. ΔE 2000 values by shade and photographic conditions on white backdrop - tripod mounted camera. Download figure Open in new tab Figure 8. ΔE 2000 values by shade and photographic conditions on white backdrop - handheld camera. Analysis of ΔE 2000 values by shade and type of photograph The analysis of the ΔE 2000 results, categorized by background type and photographic conditions, reveals noticeable differences in the distribution of values. Clinically acceptable color differences (ΔE 2000 < 2), distinguishable only by a trained observer, were observed exclusively on a black background, both when using a tripod and when the camera was handheld, with the application of a polarizing filter. The differences in the obtained results may be attributed to variations in the angle of the photographs, depending on how the operator holds the camera. Slightly greater differences were observed in images taken handheld, compared to those taken with a tripod, due to varying angles of camera positioning. For the black background, the desired effect was achieved. However, for a white background, no target outcome was achieved for any combination of lighting and photographic method, indicating that a white background may significantly reduce the chances of obtaining the desired color reproduction in photographs. The target effect (ΔE 2000 < 2) was achieved in 17 measurements (1.42%). Clearly distinguishable color differences (ΔE 2000 ≥ 5) were observed in 1005 cases (83.75%). The remaining measurements (178, 14.83%) fell within the range of 2 ≤ ΔE 2000 < 5. ΔE 2000 values ≥ 5 were significantly more frequent on a white background, especially when using a softbox or dual-plane lighting, suggesting greater challenges in achieving accurate color reproduction under these conditions. For the analysis, a binary variable ΔE binary was created, where the desired outcome (favorable outcome) was defined as ΔE < 2. This coding is based on the ΔE classification, according to which the values below 2 indicate that the color difference between the spectrophotometric measurement and the photograph is imperceptible to the average observer: Code 1: ΔE 2000 < 2 (color difference perceptible only to a trained observer) Code 0: ΔE 2000 ≥ 2 Model The reference category for the model is dual plane as the light type, handheld as the photo type, and white as the backdrop. The odds ratio (OR = 0.0021, 95% CI 0.0001–0.033, p < 0.001) indicates that, under these conditions, the probability of achieving an acceptable color difference is very low. According to the results in Table 5 , the use of a polarizing filter increases the likelihood of obtaining an acceptable color difference by more than 38 times (OR = 38.15, 95% CI [2.03, 488.22], p < 0.001). A black background also increases these odds by 38 times (OR = 0.03, 95% CI [2.54 – 572.97], p < 0.001). The impact of tripod stabilization, although suggesting a positive trend (OR = 1.45, 95% CI [0.57, 3.70]), did not reach statistical significance (p = 0.460). The use of a soft box did not show a significant impact on achieving an acceptable color difference compared to the dual flash lamp (OR = 1.00, 95% CI [0.02, 45.71], p = 1.000). A McFadden’s R 2 of 0.315 indicated that the model exhibited a satisfactory fit to the data. Both the polarizing filter and the black background contributed equally to achieving clinically acceptable ΔE values, essential for accurate color reproduction in dental photography. The magnitude of the effects in both cases is large, but they should be interpreted cautiously due to the wide confidence intervals. The results underscore the importance of optimal lighting and background selection for precise photographic documentation in dentistry. View this table: View inline View popup Table 5. Logistic regression results using Firth’s method The model explains approximately 31.5% of the variability in the analyzed data, suggesting that there are many other factors that may affect the variability of the results, including those that cannot be controlled. Future studies should consider additional factors that could impact measurement quality. A critical element is the angle of photographing the object, which is directly related to the distance from the photographed object. Equally important could be the angle of light incidence or the intensity of lighting used during the photography. The results obtained support the main hypothesis. Discussion The results obtained indicate that the colors achieved through color measurement according to the CIELAB scale differ from those obtained using spectrophotometric measurements, regardless of the lighting conditions or the background used. Analysis of the individual color components, lightness, hue, and chroma—revealed that the best color reproduction was achieved for the lightness component. Considering the complexity of the colour of human tooth color, the study utilized only a small fraction of the possible color combinations. Hein et al. conducted a color analysis of 8,153 natural teeth and identified as many as 1,173 unique tooth shades [ 31 ]. Thus, relying solely on traditional dental color systems, such as the Vita Master scale with its 26 shades, may be insufficient, and alternative methods should be explored. One potential solution is the use of dental photography with polarising filters, a concept introduced by Hein et al. [ 32 ]. However, studies have shown that the application of polarizing filters and a black background did not allow for perfect color reproduction in photographs when compared to spectrophotometer measurements. Mahn et al. obtained similar findings in their research comparing color matching using a spectrophotometer and photography with polarizing filters [ 33 ]. Consistent with our findings, the most accurate results were obtained for the L (lightness) component. Nevertheless, compared to visual color assessment with a shade guide, significantly more accurate results were achieved with photographic methods. Consequently, the authors suggest that photography could be a superior alternative to visual evaluation. Additionally, Sirintawat et al. demonstrated that photography, regardless of the camera type - DSLR or smartphone, and irrespective of the use of polarizing filters, did not replicate colors as accurately as a spectrophotometer [ 38 ]. Delta E values ranged from 6 for intraoral scanners to 20 for photographs taken with a polarizing filter. However, these findings were limited by the absence of information on whether a gray card was used for post-production. Without a grey card, photography with a polarizing filter is unlikely to be effective. These results are in line with a systematic review from 2012 [ 34 ], which analyzed 26 studies and concluded that the most repeatable results are obtained with a spectrophotometer, followed by photography with polarizing filters, and finally, visual assessment. Visual color evaluation proved to be the most subjective method, potentially explaining its lower accuracy. Similar conclusions were drawn in a more recent systematic review from 2023 [ 36 ], which analyzed 85 studies and reaffirmed that the spectrophotometer remains the gold standard. In some studies, dental photography achieved a Delta E below the acceptable threshold (<2) when compared to spectrophotometer results; however, in most studies, the differences exceeded this threshold. The results obtained by visual assessment showed the greatest discrepancies compared to the spectrophotometer measurements. Patamar et al. reported findings similar to ours, demonstrating greater accuracy for lighter shades (A1, B1) compared to darker ones (A3, B3) [ 37 ]. Regarding color matching using photography, there is a wide range of available equipment, including DSLRs, mirrorless cameras, as well as full-frame and crop-sensor cameras. Jasim et al. compared color reproduction using the same polarizing filters with both full-frame and crop-sensor cameras [ 39 ]. Their findings clearly suggest that full-frame cameras reproduce colors more accurately. In the case of full-frame cameras, the results remained within the acceptable threshold (Delta E <2), while for crop-sensor cameras, the Delta E value was 3.5, exceeding the threshold. In contrast, Farah et al. demonstrated that the type of lighting (LED, fluorescent, or natural light) in the environment where color matching is performed does not significantly affect the results. The findings by Akl et al. challenge the results obtained with dental spectrophotometers when compared with technical spectrophotometers [ 35 ]. The differences were statistically significant, highlighting the need to search for alternative methods that could replace the current ones, which present several limitations. Both factors, the use of a polarizing filter as a light modifier and the application of a black background, play significant and equivalent roles in achieving the target Delta E difference, which allows for accurate color reproduction in dental photography. The effect sizes in both cases are substantial, although they should be interpreted with caution due to wide confidence intervals. These findings underscore the importance of optimizing lighting and background conditions for precise photographic documentation in dentistry. Further research is necessary to identify optimal conditions and accessories for dental photography that may eventually enable its use for ideal tooth color matching. Conclusion Based on the obtained results, it can be concluded that accurate color reproduction of teeth in photographs remains unachievable regardless of the applied lighting conditions or background color. However, the use of polarizing filters combined with a black background enhances color fidelity, particularly in the case of lighter shades. Data Availability All data produced in the present study are available upon reasonable request to the authors References 1. ↵ Tabatabaian F , Beyabanaki E , Alirezaei P , Epakchi S. 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Share The role of lighting type in dental photography for tooth shade assessment Leszek Szalewski , Dorota Wójcik , Oskar Tokarczuk , Tuana Ozdas , Gabriela Durlej medRxiv 2025.05.14.25327573; doi: https://doi.org/10.1101/2025.05.14.25327573 Share This Article: Copy Citation Tools The role of lighting type in dental photography for tooth shade assessment Leszek Szalewski , Dorota Wójcik , Oskar Tokarczuk , Tuana Ozdas , Gabriela Durlej medRxiv 2025.05.14.25327573; doi: https://doi.org/10.1101/2025.05.14.25327573 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Dentistry and Oral Medicine Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (300) Cardiovascular Medicine (4435) Dentistry and Oral Medicine (444) Dermatology (382) Emergency Medicine (608) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1509) Epidemiology (15227) Forensic Medicine (30) Gastroenterology (1124) Genetic and Genomic Medicine (6597) Geriatric Medicine (668) Health Economics (997) Health Informatics (4534) Health Policy (1368) Health Systems and Quality Improvement (1613) Hematology (540) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15916) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (146) Nephrology (667) Neurology (6599) Nursing (346) Nutrition (998) Obstetrics and Gynecology (1144) Occupational and Environmental Health (957) Oncology (3332) Ophthalmology (974) Orthopedics (369) Otolaryngology (420) Pain Medicine (436) Palliative Medicine (130) Pathology (663) Pediatrics (1693) Pharmacology and Therapeutics (691) Primary Care Research (711) Psychiatry and Clinical Psychology (5447) Public and Global Health (9230) Radiology and Imaging (2198) Rehabilitation Medicine and Physical Therapy (1370) Respiratory Medicine (1196) Rheumatology (593) Sexual and Reproductive Health (712) Sports Medicine (530) Surgery (712) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a002d1e3be88aa64',t:'MTc3OTUyNjI5OA=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();