Influence of Artificial Surface Markers on the Accuracy of Full-arch Digital Impressions in Edentulous Arches: An In Vitro Study

Abstract

Introduction

This in vitro study assessed the accuracy of full-arch edentulous impressions obtained via intraoral scanning using various scanning strategies. This study aimed to evaluate whether the addition of surface markers would enhance scan precision in various anatomical regions of edentulous models, particularly on low-feature and smooth surfaces.

Methods

Sixty scans (30 maxillary and 30 mandibular) were performed using an intraoral scanner across three scanning protocols: unmodified (control), composite-resin, and drawn-line markers. Reference scans were obtained using a laboratory scanner. Scans were evaluated in four anatomical regions: the vestibule, residual ridge, palate/lingual area, and overall surface. Deviations were measured using the root mean square, mean deviation, and standard deviation. Two-way analysis of variance, with Tukey’s post hoc test, was used for the statistical analysis.

Results

The scanning strategy had no significant effect on the residual ridge area (p = .918). The jaw type significantly affected the scanning accuracy in all areas (p < .001), with greater deviations observed in the mandibular scans. Composite-resin and line marker strategies increased the accuracy in the mandibular vestibular and lingual regions. All root mean square error values remained within the clinically acceptable limits, although the numerical differences between the groups were minimal.

Conclusion

Full-arch intraoral scanning of edentulous models demonstrated clinically acceptable accuracy, within the limitations of this in vitro study. Surface markers did not provide a significant advantage in the maxillary scans but may improve scanning performance in mandibular regions. These findings provide methodological insights into scanning performance on smooth and low-feature surfaces and should be interpreted cautiously when extrapolating to clinical intraoral conditions.

Keywords

Digital impressions edentulous mouth intraoral scanners surface markers

Introduction

The ability of a complete denture to function properly depends on the accurate adaptation of its intaglio surface, peripheral seal, and cohesive forces of saliva contributing to retention. Therefore, accurate impression-taking is fundamental to successful complete denture therapy, requiring precise replication of both hard and soft tissues within the denture-bearing area. Conventional techniques using zinc oxide–eugenol or elastomeric materials following border molding remain the gold standard.¹

Recently, intraoral scanners (IOSs) have been widely adopted for digital impressions in fixed and implant-supported prosthodontics, often demonstrating comparable or superior accuracy.² However, capturing edentulous arches intraorally remains challenging owing to the presence of mobile mucosa, limited anatomical landmarks, and the complex morphology of soft tissues.³

Digital workflows for complete denture fabrication have been proposed to reduce clinical appointments, improve standardization, and enhance efficiency.⁴ In this context, the accuracy of digital impressions is commonly described in terms of trueness and precision.⁵ Several factors influence IOS accuracy, including scanning strategy, operator experience, environmental conditions, and image acquisition parameters.^{6, 7}

A critical limitation of edentulous scanning is the presence of smooth, low-feature surfaces that hinder image stitching and alignment.⁸ To address this, the use of artificial landmarks or surface markers has been suggested to enhance the scan registration.⁹ Although previous studies have evaluated scanning strategies and marker applications, there is limited evidence regarding their effectiveness, specifically on low-feature edentulous surfaces and across various anatomical regions.

Importantly, investigating such variables directly under intraoral conditions introduces confounding factors such as saliva, soft tissue displacement, and patient-related variability. Therefore, standardized experimental models are required to isolate the effect of scanning strategies on surface characteristics independent of intraoral conditions.

In this study, alginate impressions of edentulous models were used as a controlled and reproducible substrate to simulate smooth and low-feature surfaces while minimizing intraoral variability. This approach does not represent a clinical workflow for denture fabrication; rather, it was designed as an experimental model to evaluate the influence of scanning strategies and surface markers on the scan accuracy under standardized conditions.

Therefore, the purpose of this in vitro study was to evaluate the accuracy and precision of intraoral scanning using different strategies applied to a completely edentulous model under controlled conditions. The null hypothesis was that there would be no significant difference in accuracy and precision between the scanning methods. An alternative hypothesis was that scanning accuracy may vary depending on the scanned jaw type and scanning strategy.

Materials and Methods

Study Design

This in vitro study aimed to compare the accuracy and precision of intraoral scanning using three different strategies applied to completely edentulous maxillary and mandibular models under standardized conditions. Three experimental groups were defined: control, composite-resin marker, and line-drawn marker. Each maxillary and mandibular model was scanned 10 times per group, resulting in a total of 60 scans.

Ten repeated scans were performed on the same physical substrate to evaluate the reproducibility and precision of the IOS under different surface conditions. To account for the hierarchical structure of the data and potential nonindependence of repeated measurements, a linear mixed-effects model was employed, treating the physical model as a random effect.

An a priori power analysis was performed using G*Power 3.1 software based on a fixed-effects two-way analysis of variance (ANOVA) design. With a significance level of 0.05, statistical power of 95%, and effect size of f = 0.528 (derived from pilot data and previous studies), a minimum sample size of 10 scans per group was determined to be sufficient.¹⁰

Obtaining Measurements

Impressions were made from completely edentulous maxillary (Group U) and mandibular (Group L) models using alginate (Hydrogum 5, Zhermack, Italy).

The use of alginate impressions in this study was intended to provide a standardized, smooth, and low-feature surface condition, representing a challenging substrate for IOS image acquisition, rather than to replicate a clinical workflow.

Although alginate materials are known to exhibit dimensional instability, all impressions were scanned within 5 min after removal to minimize distortion, in accordance with the manufacturer’s recommendations. Nevertheless, this limitation was acknowledged and standardized across all groups to ensure internal consistency.

The original model was digitized using a desktop scanner (Dental Wings, Montreal, Canada) to generate the reference STL file.

Scanning Protocols

The impressions were evaluated using three scanning strategies:

Control group (Group C): Scanning was performed without any surface modification, following the manufacturer’s recommended protocol (Figure 1a).

Composite marker group (Group K): Small composite resin dots (Filtek Z250, 3M ESPE, USA) were applied to the impression surfaces. These markers were placed in minimal dimensions to ensure detectability while avoiding significant alterations in the surface geometry (Figure 1b).

Line-drawn marker group (Group P): Fine lines were drawn on the impression surface to facilitate image alignment. Care was taken to keep the line thickness minimal and as consistent as possible; however, minor variations in thickness may have occurred and are considered a limitation of this approach (Figure 1c).

Figure 1.

(a) The Measurement Model Used in the Study; (b) Placement of Composite Landmarks Within the Measurement; (c) Drawing Scan Lines Within the Measurement.

Figure 2.

(a) Regional Division of the Scanned Upper Jaw into Vestibule, Residual Ridge, and Palate; (b) Regional Division of the Scanned Lower Jaw into Vestibule, Residual Ridge, and Lingual.

Scanning Procedure

To ensure the reliability of the results and minimize potential bias, the scanning sequence across the three strategies and two jaw types was fully randomized. For each jaw, 10 independent impressions were prepared, and each was assigned to a scanning strategy in a counterbalanced order. This approach was adopted to prevent any learning effects or systematic drift caused by the temporal degradation of the alginate material. All scanning procedures were performed by a single experienced operator under standardized conditions.

All scans were performed by a single experienced operator using an IOS (Trios 5, 3Shape, Denmark) under standardized ambient lighting conditions. A new case was created for each scan using the “Removable” ↓ “Full denture” ↓ “Impression” scanning mode to standardize data acquisition. All impressions were gently dried prior to scanning to reduce surface reflectivity variability.

Data Processing and Analysis

Scan data were exported as STL files and aligned with the reference scans using Geomagic Control X (3D Systems, USA). The reference model was segmented into three anatomical regions: residual ridge, palate/lingual area, and vestibule. These regions were selected based on their clinical relevance in complete denture support, stability, and border seal, as well as their differing surface characteristics (ridge: moderate features; palate/lingual area: broad smooth surface; vestibule: peripheral anatomy) (Figures 2a–2b). Segmentation boundaries were consistently defined across all samples using the same reference model to ensure reproducibility. Alignment was initially performed using “Initial Alignment,” followed by “Best Fit Alignment,” when necessary to improve the local accuracy.

An accuracy analysis was conducted using the “3D Compare” tool. The root mean square error (RMSE), average deviation (AVG), and standard deviation (STD) were calculated for each scan across four regions: the vestibule, residual ridge, palate/lingual area, and overall surface.

Statistical Analysis

Data were analyzed using SPSS software (v25.0; IBM Corp., USA). The normality of distribution was assessed via Shapiro–Wilk tests and Q–Q plots, while Levene’s test was used to confirm the homogeneity of variances. Since the data followed a normal distribution and met parametric assumptions, a two-way ANOVA was performed to evaluate the effects of scanning strategy and jaw type. To mitigate the risk of Type I error inflation due to multiple testing, the overall surface root mean square (RMS) was designated as the primary outcome measure. Secondary analyses on specific subregions and additional metrics were treated as exploratory, emphasizing effect sizes (partial η²) alongside p values. Pairwise comparisons were conducted using Tukey’s post hoc tests with Bonferroni correction where appropriate. Statistical significance was set at p < .05.

Results

Two-way ANOVA was performed to evaluate the effects of scanning strategy and jaw type on the RMS, AVG, STD, and frame count values across different anatomical regions.

Residual Ridge Region

The scanning strategy did not have a statistically significant effect on AVG (p = .918). In contrast, jaw type had a statistically significant effect (F = 226.660, p < .001, η² = 0.808) (Table 1).

Table 1.

Two-way Analysis of Variance (ANOVA) Test for the Effect of Scanning Strategy and Jaw Type on Residual Ridge.

	Dependent Variable	Type III Sum of Squares	df	Mean Square	F	Sig.	Partial η²
Scanning strategy	Residual ridge AVG	1.344E-6	2	6.722E-7	0.086	0.918	0.003
	Residual ridge RMS	0.001	2	0.001	2.564	0.086	0.087
	Residual ridge STD	0.001	2	0.001	2.667	0.079	0.090
Jaw type	Residual ridge AVG	0.002	1	0.002	226.660	<0.001	0.808
	Residual ridge RMS	0.000	1	0.000	0.812	0.372	0.015
	Residual ridge STD	0.000	1	0.000	0.697	0.407	0.013
Scanning strategy * Jaw type	Residual ridge AVG	1.994E-5	2	9.968E-6	1.279	0.287	0.045
	Residual ridge RMS	0.000	2	0.000	0.989	0.379	0.035
	Residual ridge STD	0.000	2	0.000	0.987	0.379	0.035

Notes: a. R² = 0.809 (adjusted R² = 0.792); b. R² = 0.128 (adjusted R² = 0.047); c. R² = 0.129 (adjusted R² = 0.048). AVG: Average deviation; RMS: Root mean square; STD: Standard deviation. Type III Sum of Squares indicates the variance explained by each factor after controlling for other effects in the model; df = degrees of freedom; Mean Square = sum of squares divided by df; F = test statistic; Sig. = significance value; Partial η² = partial eta squared, indicating the effect size. A p-value of < 0.05 was considered statistically significant.

Mean deviation values for the maxillary model (Group U) were consistently low across all scanning strategies (0.04–0.05 mm), whereas mandibular values (Group L) were slightly negative (–0.06 to –0.07 mm). The RMS and STD values were similar among all groups (approximately 0.56–0.63 mm), with no statistically significant differences (p > .05) (Table 2). Despite the statistical significance related to jaw type, the magnitude of deviation differences remained small.

Table 2.

Residual Ridge Descriptive Statistics.

Scanning Strategy	Jaw Type	Residual Ridge AVG	Residual Ridge AVG	Residual Ridge AVG
Group C	Group L	–0.07 ± 0.04^b	0.57 ± 0.15^a	0.57 ± 0.15^a
Group C	Group U	0.05 ± 0.01^a	0.60 ± 0.16^a	0.60 ± 0.16^a
Group K	Group L	–0.06 ± 0.03^b	0.63 ± 0.18^a	0.63 ± 0.18^a
Group K	Group U	0.04 ± 0.02^a	0.61 ± 0.11^a	0.60 ± 0.11^a
Group P	Group L	–0.07 ± 0.04^b	0.57 ± 0.16^a	0.56 ± 0.15^a
Group P	Group U	0.04 ± 0.01^a	0.46 ± 0.12^a	0.46 ± 0.12^a

Notes: ^a,bThere is no meaningful difference in vertical columns with the same letter. AVG: Average deviation.

Vestibular Region

The scanning strategy had a statistically significant effect on the AVG (F = 7.405, p < .001, η² = 0.215). Jaw type also significantly affected the AVG values (F = 203.227, p < .001, η² = 0.790) (Table 3).

Table 3.

Two-way Analysis of Variance (ANOVA) Test for the Effect of Scanning Strategy and Jaw Type on Vestibule.

	Dependent Variable	Type III Sum of Squares	df	Mean Square	F	Sig.	Partial η²
Scanning strategy	Vestibule AVG	0.000	2	7.998E-5	7.405	<0.001	0.215
	Vestibule RMS	0.001	2	0.000	3.106	0.053	0.103
	Vestibule STD	0.001	2	0.000	2.728	0.074	0.092
Jaw type	Vestibule AVG	0.002	1	0.002	203.227	<0.001	0.790
	Vestibule RMS	0.001	1	0.001	9.189	0.004	0.145
	Vestibule STD	0.001	1	0.001	2.859	0.097	0.050
Scanning strategy * Jaw type	Vestibule AVG	0.000	2	6.122E-5	5.669	0.006	0.174
	Vestibule RMS	0.004	2	0.002	12.086	<0.001	0.309
	Vestibule STD	0.001	2	0.001	3.446	0.039	0.113

Notes: a. R² = 0.809 (adjusted R² = 0.792); b. R² = 0.423 (adjusted R² = 0.369); c. R² = 0.220 (adjusted R² = 0.148). AVG: Average deviation; RMS: Root mean square; STD: Standard deviation. Type III Sum of Squares indicates the variance explained by each factor after controlling for other effects in the model; df = degrees of freedom; Mean Square = sum of squares divided by df; F = test statistic; Sig. = significance value; Partial η² = partial eta squared, indicating the effect size. A p-value of < 0.05 was considered statistically significant.

Group K and Group P demonstrated lower mean deviation values compared to Group C, particularly in mandibular scans (–0.06 ± 0.02 mm and –0.11 ± 0.05 mm, respectively). In contrast, maxillary values remained low and consistent across all groups (approximately 0.01–0.02 mm).

The RMS and STD values varied across groups; however, these differences were limited. For example, the lowest RMS value was observed in Group K–L (0.52 ± 0.14 mm), while the highest was in Group K–U (0.83 ± 0.05 mm) (Table 4).

Table 4.

Vestibule Descriptive Statistics.

Scanning Strategy	Jaw Type	Vestibule AVG	Vestibule RMS	Vestibule STD
Group C	Group L	–0.13 ± 0.05^c	0.66 ± 0.15^b,c	0.65 ± 0.14^a,b
Group C	Group U	0.02 ± 0.01^a	0.70 ± 0.13^a,b	0.70 ± 0.13^a
Group K	Group L	–0.06 ± 0.02^b	0.52 ± 0.14^c	0.51 ± 0.14^b
Group K	Group U	0.02 ± 0.01^a	0.83 ± 0.05^a	0.68 ± 0.14^a,b
Group P	Group L	–0.11 ± 0.05^c	0.63 ± 0.13^b,c	0.61 ± 0.12^a,b
Group P	Group U	0.01 ± 0.01^a	0.56 ± 0.11^b,c	0.56 ± 0.11^a,b

Notes: ^a–cThere is no meaningful difference in vertical columns with the same letter. AVG: Average deviation; RMS: Root mean square; STD: Standard deviation.

Palatal (Maxillary) and Lingual (Mandibular) Region

Group U exhibited minimal deviation across all strategies (approximately 0.00–0.01 mm). In contrast, Group L showed slightly higher negative deviations in Groups C and P, while Group K–L demonstrated values close to zero (0.00 ± 0.01 mm) (Table 5).

Table 5.

Two-way Analysis of Variance (ANOVA) Test for the Effect of Scanning Strategy and Jaw Type on Maxillary Palatal and Mandibular Lingual Region.

Dependent Variable		Type III Sum of Squares	df	Mean Square	F	Sig.	Partial η²
Scanning strategy	MPML AVG	0.000	2	5.338E-05	4.352	0.018	0.139
	MPML RMS	0.000	2	0.000	1.031	0.364	0.037
	MPML STD	0.000	2	0.000	1.010	0.371	0.036
Jaw type	MPML AVG	0.000	1	0.000	27.860	<0.001	0.340
	MPML RMS	0.004	1	0.004	28.947	<0.001	0.349
	MPML STD	0.004	1	0.004	29.276	<0.001	0.352
Scanning strategy * Jaw type	MPML AVG	0.000	2	0.000	15.950	<0.001	0.371
	MPML RMS	0.018	2	0.009	64.204	<0.001	0.704
	MPML STD	0.018	2	0.009	65.879	<0.001	0.709

Notes: a. R² = 0.559 (adjusted R² = 0.518); b. R² = 0.747 (adjusted R² = 0.724); c. R² = 0.751 (adjusted R² = 0.728). AVG: Average deviation; MPML: Maxillary palatal and mandibular lingual; RMS: Root mean square; STD: Standard deviation. Type III Sum of Squares indicates the variance explained by each factor after controlling for other effects in the model; df = degrees of freedom; Mean Square = sum of squares divided by df; F = test statistic; Sig. = significance value; Partial η² = partial eta squared, indicating the effect size. A p-value of < 0.05 was considered statistically significant.

Although statistically significant differences were observed in the RMS and STD values between some groups (p < .05), the absolute differences remained small in magnitude (Table 6).

Table 6.

Maxillary Palatal and Mandibular Lingual Region Descriptive Statistics.

Scanning Strategy	Jaw Type	MPML AVG	MPML RMS	MPML STD
Group C	Group L	–0.09 ± 0.06^b	0.61 ± 0.19^a	0.61 ± 0.18^a
Group C	Group U	0.00 ± 0.01^a	0.21 ± 0.04^b	0.21 ± 0.04^b
Group K	Group L	–0.03 ± 0.01^a	0.21 ± 0.04^b	0.21 ± 0.04^b
Group K	Group U	0.00 ± 0.01^a	0.54 ± 0.16^a	0.53 ± 0.16^a
Group P	Group L	–0.08 ± 0.05^b	0.64 ± 0.13^a	0.63 ± 0.13^a
Group P	Group U	0.00 ± 0.01^a	0.22 ± 0.05^b	0.22 ± 0.05^b

Notes: ^a–bThere is no meaningful difference in vertical columns with the same letter. AVG: Average deviation; MPML: Maxillary palatal and mandibular lingual; RMS: Root mean square; STD: Standard deviation.

Total Area

The scanning strategy had a statistically significant effect on AVG (F = 4.739, p = .013, η² = 0.149) and a strong effect on the number of frames (F = 14.551, p < .001, η² = 0.350). Jaw type had a highly significant effect on AVG (F = 459.416, p < .001, η² = 0.895) and frame count (F = 54.737, p < .001, η² = 0.503) (Table 7).

Table 7.

Two-way Analysis of Variance (ANOVA) Test for the Effect of Scanning Strategy and Jaw Type on Total Area and Frame.

	Dependent Variable	Type III Sum of Squares	df	Mean Square	F	Sig.	Partial η²
Scanning strategy	Total AVG	3.203E-5	2	1.601E-5	4.739	0.013	0.149
	Total RMS	0.001	2	0.001	2.706	0.076	0.091
	Total STD	0.001	2	0.001	2.609	0.083	0.088
	Frame	5,367,445.633	2	2,683,722.817	14.551	<0.001	0.350
Jaw type	Total AVG	0.002	1	0.002	459.416	<0.001	0.895
	Total RMS	0.000	1	0.000	1.411	0.240	0.025
	Total STD	0.000	1	0.000	1.792	0.186	0.032
	Frame	10,095,842.400	1	10,095,842.400	54.737	<0.001	0.503
Scanning strategy * Jaw type	Total AVG	9.701E-5	2	4.851E-5	14.354	<0.001	0.347
	Total RMS	0.001	2	0.000	1.252	0.294	0.044
	Total STD	0.001	2	0.000	1.185	0.314	0.042

Notes: a. R² = 0.902 (adjusted R² = 0.893); b. R² = 0.147 (adjusted R² = 0.068); c. R² = 0.148 (adjusted R² = 0.069); d. R² = 0.610 (adjusted R² = 0.573). AVG: Average deviation; RMS: Root mean square; STD: Standard deviation. Type III Sum of Squares indicates the variance explained by each factor after controlling for other effects in the model; df = degrees of freedom; Mean Square = sum of squares divided by df; F = test statistic; Sig. = significance value; Partial η² = partial eta squared, indicating the effect size. A p-value of < 0.05 was considered statistically significant.

Group U consistently showed small positive deviation values (0.03–0.05 mm), whereas Group L showed slightly negative values (–0.04 to –0.08 mm). The RMS and STD values were comparable across all groups (approximately 0.49–0.68 mm), with no statistically significant differences (p > .05) (Table 8).

Table 8.

Total Region and Frame Descriptive Statistics.

Scanning Strategy	Jaw Type	Total AVG	Total RMS	Total STD	Frame
Group C	Group L	–0.08 ± 0.02^c	0.66 ± 0.16^a	0.65 ± 0.15^a	1,739.008 ± 40.004^c
Group C	Group U	0.04 ± 0.01^a	0.66 ± 0.16^a	0.66 ± 0.16^a	2,601.903 ± 85.775^a
Group K	Group L	–0.04 ± 0.01^b	0.50 ± 0.10^a	0.49 ± 0.10^a	1,631.408 ± 18.765^b
Group K	Group U	0.03 ± 0.02^a	0.64 ± 0.19^a	0.64 ± 0.19^a	2,518.304 ± 37.952^a
Group P	Group L	–0.07 ± 0.02^c	0.68 ± 0.16^a	0.67 ± 0.16^a	1,137.905 ± 15.910^c
Group P	Group U	0.05 ± 0.01^a	0.68 ± 0.17^a	0.68 ± 0.17^a	1,849.306 ± 14.433^a

Notes: ^a–cThere is no meaningful difference in vertical columns with the same letter. AVG: Average deviation; RMS: Root mean square; STD: Standard deviation.

Frame counts varied considerably between scanning strategies, with the highest number observed in Group C (2,601 ± 85) and the lowest in Group P (1,137 ± 15).

Discussion

The present in vitro study evaluated the effect of different scanning strategies on the digital acquisition of alginate impressions derived from completely edentulous maxillary and mandibular models. Under standardized experimental conditions, statistically significant differences were observed between the scanning strategies and jaw types. However, the absolute magnitude of these differences was generally small, and most deviation values remained within clinically acceptable limits (<100 µm) reported in the literature.^{11, 12} Therefore, the findings should be interpreted primarily as a methodological evaluation of scan performance on smooth and low-feature surfaces rather than as evidence of direct clinical applicability.

A principal finding of this study was that jaw type had a greater influence on scan deviation than scanning strategy. Mandibular scans consistently demonstrated greater deviations than maxillary scans. This may be explained by morphological differences between arches, as the maxilla provides broader and more continuous anatomical surfaces—such as the palate, which may facilitate image stitching and alignment.^{13, 14} Similar trends have been reported in previous studies evaluating edentulous scanning, although not always with consistent statistical significance.¹⁴

The influence of the scanning strategy was limited and region-dependent. No significant differences were observed between the scanning strategies in the residual ridge region, which may be attributed to its relatively uniform surface morphology. In contrast, the application of composite or line markers showed some improvement in the deviation values in the mandibular vestibular and lingual regions. These areas are known to present greater scanning difficulty owing to limited geometric features and complex peripheral anatomy.^{15, 16} However, the magnitude of these improvements was small, and the findings do not support the generalized conclusion that marker application significantly enhances the scanning accuracy across all regions.

The use of artificial markers to improve the scan alignment has been previously suggested. Kim et al.¹⁷ reported improved accuracy with marker use in long-span edentulous regions, whereas Chang et al.¹⁸ demonstrated that the effectiveness of markers may vary depending on the scanning system and anatomical conditions. Similarly, Albdour et al.¹⁹ emphasized the role of surface characteristics, such as reflectivity, in scan accuracy. In the present study, marker application showed localized and inconsistent effects, suggesting that its benefit may be limited to specific surface conditions rather than being universally applicable.

It is important to emphasize that the present study did not evaluate the clinical workflow of denture fabrication. Unlike studies assessing intraoral scanning or cast digitization, this study involved scanning the alginate impression surfaces. This methodological distinction is critical. Scanning an impression does not replicate intraoral conditions, as saliva, mucosal mobility, tissue displacement, and patient-related factors significantly affect scan acquisition.²⁰ Therefore, extrapolation of these findings to clinical edentulous scanning should be performed with caution.

The use of alginate impressions as an experimental substrate presents an additional limitation. Alginate undergoes dimensional changes over time, even under controlled conditions. Although all impressions were scanned within 5 min to minimize distortion and the protocol was standardized across all groups, dimensional instability could not be completely eliminated. Consequently, part of the measured deviation may reflect the material behavior rather than solely the performance of the scanning strategy. This limitation differentiates the present findings from studies based on stone casts or direct intraoral scanning.^{21, 22}

Another limitation is related to the segmentation of anatomical regions. The vestibule, residual ridge, and palate/lingual areas were selected because of their clinical relevance in denture support, stability, and border seal, as well as their differing surface characteristics. However, these regions represent analytical divisions rather than discrete anatomical boundaries, and their interpretation should be considered within the context of software-based segmentation approaches.^{19, 23}

Therefore, the marker application protocol should be cautiously interpreted. Although efforts have been made to minimize the marker dimensions, any addition to the surface has the potential to alter the local geometry. This is particularly relevant for the line-marker group, where variations in line thickness may have influenced the scan results independently of their intended role in improving the image registration.^{24, 25}

An additional consideration is the distinction between statistical and clinical significance. Although several comparisons showed statistically significant differences, the numerical magnitude of these differences was small. This suggests that the scanning strategy may influence measured deviation values without producing clinically meaningful differences. The dominant effect of jaw type further supports the notion that anatomical factors play a greater role than surface modification strategies under the conditions tested.²⁶

Existing literature on edentulous scanning includes studies of intraoral scanning, cast digitization, and scanning strategies. However, these workflows differed fundamentally from the present experimental design. Therefore, previous studies should be interpreted as providing contextual support regarding scanning challenges in edentulous conditions, rather than as direct comparators.^{15, 27, 28}

In the present study, several significant p values were observed, particularly regarding the interaction between jaw type and scanning strategy. However, these findings should be interpreted with caution. While the use of surface markers showed statistically significant improvements in certain regions, the absolute numerical differences remained minimal and stayed well within the clinically acceptable threshold of 0.07 mm. The high statistical power of our study, combined with the multiplicity of tests, may have highlighted differences that are statistically detectable but have limited clinical impact. Therefore, our results support the use of markers as a supplementary aid rather than a strictly necessary requirement for full-arch edentulous scanning.

Within the limitations of this in vitro study, full-arch scanning of alginate impressions from edentulous models resulted in small deviations across all the groups. Surface markers did not provide a consistent advantage in maxillary scans but showed limited regional benefits of certain mandibular areas. These findings contribute to the understanding of the scan behavior on smooth and low-feature surfaces under controlled conditions. However, they should not be interpreted as direct evidence of a clinical denture impression workflow. Further studies comparing intraoral scanning, impression scanning, and conventional techniques are necessary to determine the clinical relevance of these observations.

Conclusion

Within the limitations of this in vitro study, all three scanning strategies demonstrated comparable performances when applied to alginate impressions of completely edentulous models. Although some statistically significant differences were observed, the magnitude of these differences was small and remained within the clinically acceptable limits.

Surface modifications did not influence scan accuracy in maxillary impressions, whereas limited improvements were observed in specific mandibular regions with the use of composite markers. However, these effects were localized and inconsistent across all measurements.

These findings suggest that scanning strategy and surface modification may have a minor influence on the measurement outcomes under controlled experimental conditions. However, the present results are limited to impression-surface scanning and cannot be directly extrapolated to intraoral edentulous scanning or clinical denture workflows. Further studies are required to evaluate the clinical relevance of these findings under intraoral conditions and with different impression and scanning protocols.

Footnotes

Authors’ Contributions

Conceived and designed the analysis: EA, HY. Data collection or data entry: EA. Contributed data/analysis tools: EA, HY. Performed the analysis: EA. Writing: EA, HY. All authors read and approved the final version of the manuscript.

Data Availability Statement

The datasets generated and/or analyzed during the current study are not publicly available as additional analyses are currently being carried out but are available from the corresponding author on reasonable request.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethical Approval Institutional Statement and Informed Consent

This study was conducted in vitro only. It does not involve animals or personal data from which individuals can be identified. Consequently, approval from an ethics committee and informed consent were not required.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Emel Arslan

Hatice Yemenoglu

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