Abstract
Background
The accuracy of intraoral scanning can vary across clinical scenarios. The aim of this study was to evaluate the effects of dentition alignment, scanning strategy, and operator experience on the trueness, precision, and efficiency of intraoral scanning.
Methods
Three maxillary models representing aligned, crowded, and spaced dentition were scanned using an intraoral scanner (Alliedstar AS100). Three experienced operators and three inexperienced dental students performed intraoral scanning using three scanning strategies, each repeated ten times. Trueness and precision were quantified using root mean square (RMS) deviations following best-fit alignment in Geomagic Control X software, and scanning time was recorded. Data were analyzed using generalized linear mixed models.
Results
Scanning strategy and dentition alignment significantly affected trueness and precision (P < 0.05), whereas operator experience showed no significant main effect. Non-linear strategies produced lower RMS deviations than the linear strategy (P < 0.001). Spaced dentition showed the highest deviations (0.16 ± 0.12 mm), while crowded dentition exhibited the lowest (0.13 ± 0.10 mm). Scanning time was significantly influenced by scanning strategy and dentition alignment, with the sweeping non-linear strategy showing the shortest acquisition time (P < 0.05). Tooth-level analysis revealed site-specific differences in scanning accuracy.
Conclusions
Dentition alignment and scanning strategy are primary determinants of intraoral scanning performance, whereas operator experience has a limited influence on full-arch accuracy.
Keywords: Intraoral scanning, Operator experience, Dentition types, Accuracy
Background
The integration of computer-aided design and computer-aided manufacturing (CAD–CAM) technologies has markedly reshaped contemporary prosthodontic practice by improving the accuracy, efficiency, and reproducibility of restorative procedures [1, 2]. Traditionally, intraoral morphology has been recorded using conventional impression materials such as alginate, agar, and elastomeric silicones. Although these materials remain widely used, they are susceptible to dimensional changes caused by syneresis, imbibition, and polymerization shrinkage, which may compromise the fidelity of the resulting casts and the fit of definitive restorations. In addition, conventional impressions are frequently associated with patient discomfort [3, 4] and technique sensitivity [5, 6], which may further affect clinical reliability [7].
Digital intraoral scanning has been introduced as an alternative approach for capturing dental morphology. Through intraoral scanning, intraoral scanners (IOS) can achieve comparable or superior accuracy while improving patient comfort and streamlining clinical workflows [3, 7]. Contemporary IOS systems rely on optical acquisition technologies such as confocal microscopy or structured-light projection, followed by computational surface reconstruction to generate three-dimensional models. Although hardware performance contributes to scanning quality, the overall accuracy of intraoral scanning is strongly influenced by acquisition protocols [8] and software-based stitching algorithms [9], which determine how sequential images are merged into a complete digital model. The ability to visualize scans in real time also enables immediate quality assessment during tooth preparation and impression procedures [6], which may reduce the need for repeated impressions and improve clinical efficiency.
Despite these advantages, the accuracy of intraoral scanning can vary across clinical scenarios. Previous studies have identified several factors that influence scanning outcomes, including scanner type [5, 10–19], acquisition protocol [13, 15, 20–22], and operator-related variables [13, 23–25]. Among these, scanning strategy, defined as the predetermined movement path of the scanner tip to ensure sufficient overlap between adjacent images, has been recognized as a key determinant of scanning trueness and precision, particularly for full-arch scans [26–31]. From a clinical perspective, inaccurate intraoral scanning may lead to cumulative errors during CAD–CAM fabrication, potentially affecting marginal adaptation and occlusal relationships of prosthetic restorations [32]. Operator experience has also been reported to influence scanning performance, with experienced users generally achieving shorter acquisition times and more consistent results [26, 33, 34]. However, the relative contribution of operator experience compared with technical factors remains inconclusive. These findings collectively suggest that both technical factors and anatomical conditions may interact to influence scanning accuracy. Existing evidence remains fragmented, particularly regarding the combined effects of dentition morphology, scanning strategy, and operator experience under standardized conditions.
In contrast, the influence of dentition alignment on intraoral scanning accuracy has received limited systematic investigation. Although some studies have suggested that dental crowding may increase scanning error, the effect of spaced dentition has rarely been examined [35]. Many validation studies have relied on idealized dental models with well-aligned teeth, whereas malocclusion and irregular tooth arrangements are frequently encountered in daily clinical practice [36–39]. Conditions such as dental crowding or spacing may alter surface morphology and reference geometry, potentially affecting feature recognition and image stitching during scanning. These morphological variations could therefore influence the reliability of intraoral scanning and the accuracy of subsequent restorations. Nevertheless, evidence regarding how dentition alignment interacts with scanning strategy and operator experience remains scarce, let alone the scanning accuracy at specific sites.
Therefore, the present study aimed to systematically evaluate the effects of dentition alignment, scanning strategy, and operator experience on the trueness, precision, and efficiency of intraoral scanning. Three maxillary models representing aligned, crowded, and spaced dentition were scanned using different acquisition strategies by experienced and inexperienced operators. Understanding how these factors influence scanning performance may provide clinically relevant guidance for optimizing intraoral scanning workflows and improving the predictability of CAD–CAM restorations. The null hypotheses were that: (I) dentition alignment, scanning strategy, and operator experience would not affect scanning trueness; (II) these factors would not influence scanning precision; (III) there would be no significant differences in scanning time among the experimental conditions.
Materials and methods
Scanning models and equipment
Three custom-made maxillary dentition models fabricated from epoxy resin (Jining Xingxing Medical Instrument Co., Ltd, Shandong, China) were used in this study. The models were fabricated using silicone mold duplication techniques. The models are shown in Fig. 1: an aligned dentition (AD), a crowded dentition (CD), and a spaced dentition (SD). These models were selected to simulate common variations in clinical dentition morphology. All intraoral scans were obtained using a single intraoral scanner (Alliedstar AS100, 1.0.16.2, Ziyang Lianyao Medical Equipment Co., Ltd, Sichuan, China) in order to eliminate potential variability associated with different scanning devices.
Fig. 1.
Three maxillary dental models representing different dentition alignment were included: Model AD, aligned dentition; Model CD, crowded dentition; Model SD, spaced dentition
Six operators participated in the study, including three operators with at least one year of clinical experience using intraoral scanners and three dental students without prior scanning experience. Operators were categorized into experienced and inexperienced groups according to their clinical exposure to IOS systems. To minimize operator-related variability, all participants were right-handed. Before the formal experiment, all operators received standardized instruction regarding scanner handling and the predefined scanning strategies. The purpose of the training was to ensure that all participants were able to perform the scanning procedures according to the study protocol while maintaining the distinction between experienced and inexperienced operators. The inexperienced operators were trained only to achieve basic operational familiarity rather than proficiency equivalent to the experienced group. In addition, the number of captured images per scan was limited to fewer than 1000 frames in accordance with recommended digital acquisition guidelines [25].
Sample size estimation
Three scanning strategies were evaluated, and each protocol was repeated ten times. A priori sample size estimation was conducted using G*Power software, following methodologies reported in recent digital dentistry studies [17, 18, 40]. Based on an assumed effect size of 0.6, a significance level (α) of 0.05, and a desired statistical power of 0.92 (1 − β = 0.92), the minimum required sample size was calculated as 60 scans, corresponding to 10 scans per group. The selected sample size ensured sufficient statistical power to detect differences among experimental conditions based on effect sizes reported in previous intraoral scanning studies [17, 18].
Operators and scanning procedure
All intraoral scans were acquired using AS Connect software (version 1.0.42.5) under standardized manufacturer-recommended settings. The maxillary scanning mode was selected with a scanning depth of 15 mm, and the high-resolution option was disabled to ensure consistency among groups. The scanner was calibrated prior to each scanning session according to the manufacturer’s instructions.
Three scanning strategies were implemented based on previously validated protocols (Fig. 2) [27].
Fig. 2.
Scanning strategies from previous research
Strategy SZ (Straight and zigzag, non-linear): Scanning began on the occlusal surfaces of the maxillary right posterior teeth and progressed anteriorly using alternating buccal–lingual sweeping movements toward the left posterior region. Palatal and buccal surfaces were subsequently scanned to complete the full arch.
Strategy S (Straight, linear): Scanning followed a continuous path starting from the buccal surfaces of the right posterior teeth and moving toward the left side, followed by occlusal surface capture and finally the lingual surfaces.
Strategy Z (Zigzag, non-linear): Scanning proceeded along a right-to-left arch trajectory with continuous buccal–lingual sweeping motions until the entire dental arch was recorded.
Strategies SZ and Z were categorized as non-linear scanning trajectories, whereas Strategy S represented a linear scanning trajectory.
Evaluation of trueness and precision on RMS error and scanning time
Scanning time was recorded immediately upon completion of each acquisition. All STL (Standard Tessellation Language) datasets were imported into Geomagic Control X software (version 2014, 3D Systems, Rock Hill, SC, USA) for three-dimensional analysis. Reference datasets were acquired using the same IOS system by another experienced operator under standardized conditions. A comprehensive scanning approach incorporating elements of the evaluated scanning strategies was adopted to maximize surface coverage and facilitate stable data acquisition. The scanning procedure ensured complete coverage of all dental surfaces prior to STL export and subsequent three-dimensional deviation analysis. Prior to superimposition, gingival regions were removed to ensure that the comparison was restricted to dental structures. Each test scan was aligned with the corresponding reference dataset using a best-fit alignment algorithm, which is widely used for evaluating intraoral scanner accuracy [22, 28]. Trueness and precision were quantified using the root mean square (RMS) deviation according to the following formula:
 |
Where X1,i represented reference data, X2,i represented the test data, and n represented the number of measurement points.
Trueness was assessed by superimposing each intraoral scan onto the corresponding reference dataset using a best-fit alignment algorithm within Geomagic Control X software. The dimensional discrepancies between the experimental and reference datasets were subsequently quantified using RMS deviation values. Additionally, precision was determined through pairwise comparisons of repeated intraoral scans acquired under identical experimental conditions. Following the same alignment protocol, the consistency among repeated scan datasets was evaluated by calculating the RMS deviation values [19, 22, 41].
To further evaluate regional accuracy, regions of interest (ROI) were defined at the anterior teeth (11, 21, 13, 23), premolar (14, 24), and molar (16, 26) sites. These regions were selected because accurate digital capture in these areas is considered clinically important for the fit and long-term stability of fixed prosthetic restorations.
Statistical analysis
Statistical analyses were conducted using SAS software version 9.4 (SAS Institute, Cary, NC, USA). All reported p-values are two-sided and P < 0.05 was used to define statistical significance. Descriptive statistics such as count and frequency were generated for categorical variables and mean and standard deviation (SD) were generated for continuous variables. The effects of dentition alignment, scanning strategies, and operator experience on trueness, precision, and scanning time were evaluated using a generalized linear mixed model. The generalized linear mixed model was selected to account for the repeated-measures design and potential non-independence of observations arising from multiple scans performed by different operators under different scanning conditions. Tukey’s method was used for post hoc pairwise comparisons where significant main or interaction effects were identified.
Results
Effects on full-arch trueness and scanning time
The mean RMS deviations for full-arch trueness across all experimental conditions are presented in Table 1. The largest deviation was observed when inexperienced operators applied scanning strategy S to spaced dentition (0.25 ± 0.18 mm), whereas the smallest RMS error was obtained when experienced operators used strategy Z on crowded dentition (0.08 ± 0.02 mm). Consistent with previous reports of protocol-dependent variability in intraoral scanning accuracy [13, 20, 23, 25–27, 33, 34], the generalized linear mixed model revealed significant main effects of scanning strategy (P < 0.001, Table 2) and dentition alignment on trueness (P < 0.001, Table 2), while operator experience did not reach statistical significance. Among the three dentition alignment, crowded dentition exhibited the lowest overall RMS deviations (0.13 ± 0.10 mm, Fig. 3a), while spaced dentition showed the highest deviations (0.16 ± 0.12 mm, Fig. 3a). With respect to scanning strategy, strategy Z produced the lowest RMS deviations (0.11 ± 0.05 mm), whereas the linear strategy S (0.20 ± 0.15 mm) resulted in significantly greater deviations compared with strategies SZ (0.13 ± 0.08 mm) and Z (P < 0.001, Fig. 3b), in agreement with previous studies [25, 29, 30].
Table 1.
Overall trueness and scanning time (Mean ± SD) for three types of models using three scanning strategies
| Model | Scan | RMS error (mm) | Scanning time (s) | |||
|---|---|---|---|---|---|---|
| Exp | Inexp | Exp | Inexp | |||
| AD | SZ | 0.14 ± 0.08 | 0.14 ± 0.03 | 92.17 ± 33.32 | 152.30 ± 53.49 | |
| S | 0.17 ± 0.10 | 0.24 ± 0.18 | 83.87 ± 28.89 | 129.17 ± 26.45 | ||
| Z | 0.10 ± 0.03 | 0.11 ± 0.03 | 85.13 ± 26.64 | 116.10 ± 24.39 | ||
| CD | SZ | 0.10 ± 0.07 | 0.13 ± 0.08 | 132.03 ± 48.04 | 165.33 ± 56.16 | |
| S | 0.19 ± 0.14 | 0.17 ± 0.11 | 112.87 ± 30.14 | 140.83 ± 34.38 | ||
| Z | 0.08 ± 0.02 | 0.12 ± 0.08 | 97.30 ± 19.21 | 127.73 ± 39.58 | ||
| SD | SZ | 0.14 ± 0.07 | 0.14 ± 0.10 | 123.90 ± 45.55 | 149.50 ± 42.47 | |
| S | 0.18 ± 0.12 | 0.25 ± 0.18 | 97.07 ± 19.42 | 131.83 ± 26.38 | ||
| Z | 0.11 ± 0.04 | 0.12 ± 0.05 | 105.63 ± 19.88 | 117.13 ± 23.62 | ||
Definitions of terms used in the table:
Exp represents experienced operators, Inexp represents inexperienced operators
Model represents dentition alignment. AD aligned dentition, CD crowded dentition, SD spaced dentition
Scan represents scanning strategy. Strategy SZ Straight and zigzag, non-linear, Strategy S Straight, linear; Strategy Z Zigzag, non-linear
Table 2.
Generalized linear mixed model to analyze the impacts of dentition alignment and experience on trueness in three scanning strategies
| Effect | Df | F | P | Comparison |
|---|---|---|---|---|
| Model | 2 | 8.43 | < 0.001 | CD < (AD, SD) |
| Experience | 1 | 1.68 | 0.196 | |
| Scan | 2 | 54.99 | < 0.001 | Z < SZ < S |
| Model *experience | 2 | 0.38 | 0.687 | |
| Model *scan | 4 | 0.56 | 0.689 | |
| Scan*experience | 2 | 0.67 | 0.514 |
Definitions of terms used in the table:
Model represents dentition alignment. AD aligned dentition, CD crowded dentition, SD spaced dentition
Scan represents scanning strategy. Strategy SZ Straight and zigzag, non-linear; Strategy S Straight, linear; Strategy Z = Zigzag, non-linear
Fig. 3.
Generalized linear mixed model to analyze the impacts of dentition alignment and scanning strategy on trueness. Data were shown as mean ± standard deviations. a RMS errors of trueness for different model types. b RMS errors of trueness for different scanning strategy, where * represents significant differences between groups (P < 0.05)
Scanning time was also significantly affected by scanning strategy and dentition alignment (Table 3). The shortest acquisition time was recorded when experienced operators used strategy S on aligned dentition (83.87 ± 28.89 s), whereas the longest time occurred when inexperienced operators applied strategy SZ to crowded dentition (165.33 ± 56.16 s). Across all operators, crowded dentition required the longest scanning duration (129.35 ± 44.67 s), while aligned dentition consistently required the shortest time (109.79 ± 41.74 s, P < 0.05, Fig. 4a). In terms of acquisition protocols, strategy SZ was associated with the greatest time expenditure (135.87 ± 53.20 s), whereas strategy Z showed the shortest scanning duration (108.17 ± 29.62 s, P < 0.001, Fig. 4b). When operator experience was analyzed separately, both experienced (114.07 ± 37.04 s) and inexperienced operators (144.63 ± 46.57 s) required more time to scan crowded dentition. For experienced operators, scanning aligned dentition required significantly less time (87.06 ± 29.64 s, P < 0.001) than scanning crowded dentition (114.07 ± 37.04 s) and spaced dentition (108.87 ± 32.47 s), whereas no significant difference between aligned dentition (132.52 ± 39.76 s) and spaced dentition (132.82 ± 34.25 s) was observed for inexperienced operators (P > 0.05, Fig. 4c). Across both experience groups, strategy SZ consistently resulted in longer acquisition times, while strategy Z was the most time-efficient scanning protocol (Fig. 4d).
Table 3.
Generalized linear mixed model to analyze the impacts of model types and experience on scanning time in three scanning strategies
| Effect | Df | F | P | Comparison |
|---|---|---|---|---|
| Model | 2 | 27.00 | < 0.001 | AD < SD < CD |
| Experience | 1 | 1.91 | 0.168 | |
| Scan | 2 | 57.30 | < 0.001 | Z < S < SZ |
| Model*experience | 2 | 8.52 | < 0.001 |
Exp [AD < (SD, CD)], Inexp [(AD, SD) < CD] |
| Model*scan | 4 | 1.64 | 0.164 | |
| Scan*experience | 2 | 4.53 | 0.011 |
Exp[(Z, S) < SZ], Inexp(Z < S < SZ) |
Definitions of terms used in the table:
Exp represents experienced operators, Inexp represents inexperienced operators
Model represents dentition alignment. AD aligned dentition, CD crowded dentition, SD spaced dentition
Scan represents scanning strategy. Strategy SZ Straight and zigzag, non-linear; Strategy S Straight, linear; Strategy Z Zigzag, non-linear
Fig. 4.
Generalized linear mixed model to analyze the impacts of dentition alignment and scanning strategy on scanning time. Data were shown as mean ± standard deviations. a Scanning time for different model types. b Scanning time for different scanning strategy. c scanning time for model types based on experienced and inexperienced operators. d scanning time for scanning strategy based on experienced and inexperienced operators, where * represents significant differences between groups (P ＜ 0.05), NS represents no significant differences between groups (P > 0.05)
Tooth-level accuracy
The ROI-based tooth-level trueness analysis revealed location-dependent differences in scanning accuracy (Tables 4 and 5). Dentition alignment and scanning strategy significantly affected the trueness of all evaluated teeth except tooth 16, for which only scanning strategy reached statistical significance (P < 0.05). Operator experience showed a significant effect for only one tooth position (tooth 26, P < 0.05).
Table 4.
Generalized linear mixed model to analyze the impacts of model types and experience on trueness of ROI anterior teeth in three scanning strategies
| Effect | Df | ROI teeth | |||||||
|---|---|---|---|---|---|---|---|---|---|
| 11 | 21 | 13 | 23 | ||||||
| F | P | F | P | F | P | F | P | ||
| Model | 2 | 21.76 | < 0.001 | 16.11 | < 0.001 | 57.54 | < 0.001 | 9.59 | < 0.001 |
| Experience | 1 | 0.99 | 0.320 | 0.48 | 0.489 | 0.89 | 0.345 | 0.18 | 0.668 |
| Scan | 2 | 47.08 | < 0.001 | 29.10 | < 0.001 | 48.54 | < 0.001 | 14.85 | < 0.001 |
|
Model* experience |
2 | 3.69 | 0.026 | 2.95 | 0.053 | 0.26 | 0.772 | 3.57 | 0.029 |
|
Model* scan |
4 | 0.55 | 0.696 | 1.57 | 0.181 | 0.53 | 0.716 | 2.50 | 0.042 |
|
Scan* experience |
2 | 1.48 | 0.229 | 0.08 | 0.925 | 0.61 | 0.542 | 2.52 | 0.081 |
Table 5.
Generalized linear mixed model to analyze the impacts of model types and experience on trueness of ROI posterior teeth in three scanning strategies
| Effect | Df | ROI teeth | |||||||
|---|---|---|---|---|---|---|---|---|---|
| 14 | 24 | 16 | 26 | ||||||
| F | P | F | P | F | P | F | P | ||
| Model | 2 | 19.18 | < 0.001 | 36.56 | < 0.001 | 0.23 | 0.791 | 98.93 | < 0.001 |
| Experience | 1 | 0.40 | 0.526 | 0.02 | 0.883 | 1.03 | 0.311 | 4.15 | 0.042 |
| Scan | 2 | 69.05 | < 0.001 | 16.82 | < 0.001 | 48.49 | < 0.001 | 4.31 | 0.014 |
|
Model* experience |
2 | 1.88 | 0.153 | 1.33 | 0.266 | 0.51 | 0.603 | 0.31 | 0.733 |
|
Model* scan |
4 | 3.49 | 0.008 | 2.30 | 0.058 | 4.69 | 0.001 | 0.40 | 0.809 |
|
Scan* experience |
2 | 4.12 | 0.017 | 2.45 | 0.087 | 5.92 | 0.003 | 1.81 | 0.164 |
Precision analysis
Precision values expressed as RMS deviations ranged from 0.08 ± 0.02 mm to 0.26 ± 0.20 mm (Table 6). The generalized linear mixed model demonstrated that reproducibility was significantly affected by dentition alignment and scanning strategy (Table 7). Crowded dentition showed lower RMS deviations (0.13 ± 0.10 mm) than aligned dentition (0.16 ± 0.11 mm, P < 0.001) and spaced dentition (0.15 ± 0.12 mm, P < 0.05), indicating higher scanning precision (Fig. 5a). In contrast, the linear scanning strategy S exhibited lower precision compared with the non-linear strategies SZ and Z ( P < 0.001, Fig. 5b) (Table 7).
Table 6.
Overall precision (Mean ± SD) for three types of models using three scanning strategies
| Model | Scan | RMS error (mm) | |
|---|---|---|---|
| Exp | Inexp | ||
| AD | SZ | 0.13 ± 0.09 | 0.15 ± 0.05 |
| S | 0.19 ± 0.09 | 0.25 ± 0.18 | |
| Z | 0.09 ± 0.04 | 0.11 ± 0.06 | |
| CD | SZ | 0.10 ± 0.07 | 0.11 ± 0.08 |
| S | 0.19 ± 0.15 | 0.17 ± 0.13 | |
| Z | 0.08 ± 0.02 | 0.12 ± 0.07 | |
| SD | SZ | 0.14 ± 0.08 | 0.13 ± 0.11 |
| S | 0.17 ± 0.13 | 0.26 ± 0.20 | |
| Z | 0.10 ± 0.05 | 0.12 ± 0.05 | |
Definitions of terms used in the table:
Exp represents experienced operators, Inexp represents inexperienced operators
Model represents dentition alignment. AD aligned dentition, CD crowded dentition, SD spaced dentition
Scan represents scanning strategy. Strategy SZ Straight and zigzag, non-linear, Strategy S Straight, linear, Strategy Z Zigzag, non-linear
Table 7.
Generalized linear mixed model to analyze the impacts of model types and experience on precision in three scanning strategies
| Effect | Df | F | P | Comparison |
|---|---|---|---|---|
| Model | 2 | 10.44 | < 0.001 | CD < (SD, AD) |
| Experience | 1 | 1.24 | 0.266 | |
| Scan | 2 | 52.59 | < 0.001 | (Z, SZ) < S |
| Model*experience | 2 | 0.05 | 0.955 | |
| Model*scan | 4 | 1.43 | 0.224 | |
| Scan*experience | 2 | 1.09 | 0.337 |
Definitions of terms used in the table:
Model represents dentition alignment. AD aligned dentition, CD crowded dentition, SD spaced dentition
Scan represents scanning strategy. Strategy SZ Straight and zigzag, non-linear, Strategy S Straight, linear, Strategy Z Zigzag, non-linear
Fig. 5.
Generalized linear mixed model to analyze the impacts of dentition alignment and scanning strategy on precision. Data were shown as mean ± standard deviations. a RMS errors of precision for different model types. b RMS errors of precision for different scanning strategy, where * represents significant differences between groups (P ＜ 0.05)
Discussion
This study systematically evaluated the influence of dentition alignment, scanning strategies, and operator experience on the accuracy and efficiency of intraoral scanning. The findings indicate that scanning strategy and dentition alignment are the primary determinants of scanning trueness, precision, and acquisition time, whereas operator experience plays a comparatively minor and localized role. Thus, all null hypotheses were rejected.
Consistent with previous studies highlighting the importance of scan path design [13, 22, 27, 31], the present results demonstrate that non-linear scanning strategies achieve higher trueness than linear trajectories. Strategies SZ and Z consistently produced lower RMS deviations than strategy S, particularly in dentition with irregular alignment. This advantage may be attributed to the greater spatial redundancy of non-linear scanning paths, which enable repeated acquisition of anatomical structures from multiple directions and thereby reduce cumulative stitching errors during image registration [14, 20, 21]. In contrast, linear scanning strategies rely on sequential acquisition along a single direction, which increases susceptibility to error propagation across extended scanning distances.
The present study further revealed that the influence of scanning strategy is strongly dependent on dentition alignment. Both crowded and spaced dentition types amplified the effect of scan path selection, an aspect that has received limited attention in previous investigations that predominantly employed well-aligned dentition or edentulous models [13, 15, 26]. The current findings suggest that variations in dentition morphology alter the availability and distribution of anatomical reference points, thereby affecting the stability of image stitching during scanning. In crowded dentition, steep axial inclinations and undercuts may disrupt surface continuity, making looping or zig-zag trajectories advantageous because they increase data overlap and improve registration stability. Conversely, in spaced dentition, discontinuous geometry reduces the number of stable reference landmarks, making repeated acquisition of anchor regions essential for maintaining alignment. These morphology-dependent characteristics may explain the particularly poor performance of the linear scanning strategy observed in the spaced dentition model.
Although operator experience did not represent a significant main effect for full-arch trueness, which is consistent with previous findings [25], its influence became evident in localized accuracy assessments. Experienced operators achieved lower RMS deviations at specific ROI teeth, supporting earlier reports that operator proficiency contributes to smoother scanner movement, improved angulation control, and reduced unnecessary rescanning [34]. The absence of a strong global experience effect may indicate that contemporary IOS systems incorporate software-based tracking algorithms that partially compensate for user-related variability [13, 30, 33]. Nevertheless, this compensation appears insufficient in anatomically complex regions or under challenging alignment conditions.
Tooth-level analyses further demonstrated that scanning accuracy is not uniformly distributed across the dental arch. Dentition alignment and scanning strategy significantly affected most ROI teeth, whereas operator experience reached statistical significance for only one tooth (26), suggesting that experience-related effects are highly site-specific. These findings support previous observations that local anatomical characteristics, such as curvature, undercuts, and accessibility, can substantially influence scanning accuracy at individual tooth positions [5]. In the present study, the significant deviation observed at tooth 26, but not at tooth 16, may reflect localized variations occurring during full-arch scanning rather than an inherent difference in the ease of scanning between the two teeth. Previous studies have suggested that cumulative stitching errors may increase toward posterior regions during digital acquisition [42]. In addition, operator-related factors, including scanner maneuverability and access angle in the upper posterior region, may also contribute to localized deviations [40, 43]. Since all operators in the present study were right-handed, subtle differences in scanner handling on the left posterior side cannot be excluded. However, the exact mechanism underlying this asymmetric finding remains unclear and warrants further investigation.
From a prosthodontic perspective, these findings suggest that site-specific deviations should be considered in addition to overall full-arch RMS values, as localized variations may not be fully reflected by global accuracy assessments. The observed differences among scanning strategies indicate that scanning pathways may influence the consistency of digital acquisition at specific tooth positions, particularly in posterior regions or areas with complex morphology. Although the localized deviation observed at tooth 26 reached statistical significance, its direct clinical relevance should be interpreted with caution. The present study was conducted under in vitro conditions, and the observed deviation may not necessarily translate into clinically significant inaccuracies during intraoral scanning procedures. Nevertheless, localized deviations in posterior regions could potentially influence the fit and reproducibility of digital workflows in situations requiring high precision, such as long-span restorations or full-arch rehabilitations. Further clinical investigations are required to determine the actual clinical impact of these localized deviations.
Precision analysis further indicated that scan reproducibility was primarily determined by scanning strategy and dentition alignment rather than operator experience. The lower precision associated with the linear scanning strategy suggests that multi-directional data acquisition in non-linear trajectories may enhance scan-to-scan consistency by stabilizing image stitching. These findings indicate that scanning pathways may affect the reproducibility of digital acquisition under standardized in vitro conditions. However, because precision primarily reflects consistency among scan datasets rather than direct clinical accuracy during intraoral scanning, the clinical implications of these findings should be interpreted cautiously and require further validation under clinical conditions.
Several limitations should be acknowledged. First, as an in vitro investigation, intraoral conditions such as saliva, soft tissue movement, and patient motion were not reproduced, which may influence scanning accuracy under clinical conditions [8, 13, 16]. Second, the epoxy resin material used in the supporting structure may have introduced optical refraction during image acquisition, potentially influencing scanning accuracy under experimental conditions. Nevertheless, the standardized experimental design allowed controlled evaluation of the independent effects of dentition morphology, scanning strategy, and operator experience on intraoral scanning performance [42]. Third, the potential influence of different resolution settings on intraoral scanning accuracy was not evaluated in the present study and should be investigated in future research. Future studies should incorporate clinical outcome measures, including restoration fit, occlusal contact accuracy, and adjustment requirements, to complement RMS-based accuracy metrics. Fourth, the reference datasets were obtained using the same intraoral scanning system rather than a laboratory or industrial scanner. Although all reference scans were acquired by an experienced operator under standardized conditions, a higher-accuracy reference system may provide a more robust benchmark for future investigations.
Conclusion
This study provides additional insights beyond previous reports by incorporating different dentition alignment types, including aligned, crowded, and spaced dentition, to better simulate clinically relevant anatomical variations during full-arch intraoral scanning. In addition to conventional full-arch RMS analysis, tooth-level evaluations were performed to identify localized deviations that may not be reflected by global accuracy assessments alone. The findings demonstrated that dentition morphology and scanning strategy had greater influences on scanning performance than operator experience, while non-linear scanning pathways generally improved scan reproducibility in anatomically complex dentition types. Collectively, these results highlight the importance of morphology-oriented scanning protocols and site-specific evaluation during intraoral scanning procedures.
Acknowledgements
We sincerely thank Professor Qianqian Zhao for her assistance with the statistical analysis.
Clinical trial number
Not applicable.
Abbreviations
- CAD–CAM
Computer-aided design and computer-aided manufacturing
- IOS
Intraoral scanner
- ROI
Regions of interest
- RMS
Root mean square
- SD
Standard Deviation
- STL
Standard tessellation language
Authors' contributions
YC, JT, and JZ contributed to conceptualization, data curation, and drafting the original manuscript, and reviewed the final version. YC and YD performed formal analysis and data validation. YC, YT, YW, and TC conducted the experiments and collected the data. All authors read and approved the final manuscript.
Funding
This work was supported by Cross-Disciplinary Research Funding of Ninth People’s Hospital affiliated to Shanghai Jiao Tong University School of Medicine (grant no. JYJC202313), Clinical Research Program of Ninth People’s Hospital affiliated to Shanghai Jiao Tong University School of Medicine (grant no. JYLJ202415).
Data availability
All data generated or analyzed during this study are included in this published article.
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Yuanyuan Chen and Yiyun Dong contributed equally to this work.
Contributor Information
Jiang Tao, Email: taojiang_doctor@sjtu.edu.cn.
Junjun Zhao, Email: 13611967296@163.com.
References
- 1.Maiti N, Mahapatra N, Patel D, Chanchad J, Saurabhbhai Shah A, Mahboob Rahaman SK, Surana P. Application of CAD-CAM in dentistry. Bioinformation. 2024;20(5):547–50. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Yeslam HE, von Freifrau N, Nassar HM. Revolutionizing CAD/CAM-based restorative dental processes and materials with artificial intelligence: a concise narrative review. PeerJ. 2024;12:e17793. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Joda T, Bragger U. Patient-centered outcomes comparing digital and conventional implant impression procedures: a randomized crossover trial. Clin Oral Implants Res. 2016;27(12):e185–9. [DOI] [PubMed] [Google Scholar]
- 4.Kihara H, Hatakeyama W, Komine F, Takafuji K, Takahashi T, Yokota J, Oriso K, Kondo H. Accuracy and practicality of intraoral scanner in dentistry: A literature review. J Prosthodont Res. 2020;64(2):109–13. [DOI] [PubMed] [Google Scholar]
- 5.Mangano F, Gandolfi A, Luongo G, Logozzo S. Intraoral scanners in dentistry: a review of the current literature. BMC Oral Health. 2017;17(1):149. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Zimmermann M, Mehl A, Mormann WH, Reich S. Intraoral scanning systems - a current overview. Int J Comput Dent. 2015;18(2):101–29. [PubMed] [Google Scholar]
- 7.Ma J, Zhang B, Song H, Wu D, Song T. Accuracy of digital implant impressions obtained using intraoral scanners: a systematic review and meta-analysis of in vivo studies. Int J Implant Dent. 2023;9(1):48. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Hardan L, Bourgi R, Lukomska-Szymanska M, Hernandez-Cabanillas JC, Zamarripa-Calderon JE, Jorquera G, Ghishan S, Cuevas-Suarez CE. Effect of scanning strategies on the accuracy of digital intraoral scanners: a meta-analysis of in vitro studies. J Adv Prosthodont. 2023;15(6):315–32. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Haddadi Y, Bahrami G, Isidor F. Effect of software version on the accuracy of an intraoral scanning device. Int J Prosthodont. 2018;31(4):375–6. [DOI] [PubMed] [Google Scholar]
- 10.Vitai V, Nemeth A, Solyom E, Czumbel LM, Szabo B, Fazekas R, Gerber G, Hegyi P, Hermann P, Borbely J. Evaluation of the accuracy of intraoral scanners for complete-arch scanning: A systematic review and network meta-analysis. J Dent. 2023;137:104636. [DOI] [PubMed] [Google Scholar]
- 11.Mangano FG, Hauschild U, Veronesi G, Imburgia M, Mangano C, Admakin O. Trueness and precision of 5 intraoral scanners in the impressions of single and multiple implants: a comparative in vitro study. BMC Oral Health. 2019;19(1):101. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Mangano FG, Admakin O, Bonacina M, Lerner H, Rutkunas V, Mangano C. Trueness of 12 intraoral scanners in the full-arch implant impression: a comparative in vitro study. BMC Oral Health. 2020;20(1):263. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Fratila AM, Saceleanu A, Arcas VC, Fratila N, Earar K. Enhancing intraoral scanning accuracy: From the influencing factors to a procedural guideline. J Clin Med. 2025;14(10):3562. [DOI] [PMC free article] [PubMed]
- 14.Pattamavilai S, Ongthiemsak C. Accuracy of intraoral scanners in different complete arch scan patterns. J Prosthet Dent. 2024;131(1):155–62. [DOI] [PubMed] [Google Scholar]
- 15.Jamjoom FZ, Aldghim A, Aldibasi O, Yilmaz B. Impact of intraoral scanner, scanning strategy, and scanned arch on the scan accuracy of edentulous arches: An in vitro study. J Prosthet Dent. 2024;131(6):1218–25. [DOI] [PubMed] [Google Scholar]
- 16.Revilla-Leon M, Subramanian SG, Ozcan M, Krishnamurthy VR. Clinical study of the influence of ambient light scanning conditions on the accuracy (trueness and precision) of an intraoral scanner. J Prosthodont. 2020;29(2):107–13. [DOI] [PubMed] [Google Scholar]
- 17.Liu CT, Chen JH, Du JK, Hung CC, Lan TH. Accuracy comparison of scan segmental sequential ranges with two intraoral scanners for maxilla and mandible. J Dent Sci. 2024;19(1):466–72. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Aung HMM, Linn TY, Lee WF, Chao JC, Teng NC, Renn TY, Chang WJ. Accuracy of intraoral scanners in maxillary multiple restorations: An in vitro study. J Dent Sci. 2024;19(Suppl 2):S116–21. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Donmez MB, Marques VR, Cakmak G, Yilmaz H, Schimmel M, Yilmaz B. Congruence between the meshes of a combined healing abutment-scan body system acquired with four different intraoral scanners and the corresponding library file: An in vitro analysis. J Dent. 2022;118:103938. [DOI] [PubMed] [Google Scholar]
- 20.Yahya MA, Selleus M, Deyar Jallal Hadi DJH, Braian M, Larsson C. The effect of different scanning protocols on precision and trueness of intraoral scanning: A pilot trial. J Clin Exp Dent. 2024;16(10):e1299–306. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Robert N, Bechet E, Albert A, Lamy M. Influence of the scanning path on the accuracy of intraoral scanners in the implanted edentulous patient: an in vitro study. Int J Comput Dent. 2025;28(2):117–27. [DOI] [PubMed] [Google Scholar]
- 22.Guo J, Lu Y, Li R, Zhou K, Ge R, Zhao D, Xia S, Wang Y. Influence of repeated cut-off and rescanning on the trueness of the intraoral digital scans. J Dent. 2024;150:105153. [DOI] [PubMed] [Google Scholar]
- 23.Thomas AA, Jain RK. Influence of operator experience on scanning time and accuracy with two different intraoral scanners - A prospective clinical trial. Turk J Orthod. 2023;36(1):10–4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Schimmel M, Akino N, Srinivasan M, Wittneben JG, Yilmaz B, Abou-Ayash S. Accuracy of intraoral scanning in completely and partially edentulous maxillary and mandibular jaws: an in vitro analysis. Clin Oral Investig. 2021;25(4):1839–47. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Lin WC, Lee CC, Lee SY, Peng CY, Lin CC. Influence of operator experience on the complete-arch accuracy and time-based efficiency of three intraoral scanners. J Dent Sci. 2025;20(1):620–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Sawangsri K, Leelaluk S, Ellakany P, Wade AB, Eid HW, Zaitseva D, Nassani LM, Hammoudeh HS. Impact of operator experience, scanning pattern, and arch location on the time and trueness of complete denture digitization using an intraoral scanner. J Prosthet Dent. 2025;134(2):462–8. [DOI] [PubMed] [Google Scholar]
- 27.Gavounelis NA, Gogola CC, Halazonetis DJ. The Effect of Scanning Strategy on Intraoral Scanner’s Accuracy. Dent J (Basel). 2022;10(7):123. [DOI] [PMC free article] [PubMed]
- 28.Cordaro M, Sailer I, Zarauz C, Liu X, Karasan D. The accuracy of full-arch intraoral optical impressions (IOS): Clinical pilot study of the influence of the scan strategy, operator, and intraoral scanner. Int J Prosthodont. 2023;36(6):689–96. [DOI] [PubMed] [Google Scholar]
- 29.Mangano FG, Veronesi G, Hauschild U, Mijiritsky E, Mangano C. Trueness and precision of four intraoral scanners in oral implantology: A comparative in vitro study. PLoS ONE. 2016;11(9):e0163107. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Roth I, Czigola A, Joos-Kovacs GL, Dalos M, Hermann P, Borbely J. Learning curve of digital intraoral scanning - an in vivo study. BMC Oral Health. 2020;20(1):287. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Hamilton A, Negreiros WM, Jain S, Finkelman M, Gallucci GO. Influence of scanning protocol on the accuracy of complete-arch digital implant scans: An in vitro study. Clin Oral Implants Res. 2024;35(6):641–51. [DOI] [PubMed] [Google Scholar]
- 32.Chiu A, Chen Y-W, Hayashi J, Sadr A. Accuracy of CAD/CAM digital impressions with different intraoral scanner parameters. Sensors. 2020;20(4):1157. [DOI] [PMC free article] [PubMed]
- 33.Zarauz C, Pradies GJ, Chebib N, Donmez MB, Karasan D, Sailer I. Influence of age, training, intraoral scanner, and software version on the scan accuracy of inexperienced operators. J Prosthodont. 2023;32(S2):135–41. [DOI] [PubMed] [Google Scholar]
- 34.Limones A, Morton D, Sallorenzo A, Lin WS, Sadid-Zadeh R, Phasuk K, Revilla-Leon M, Gomez-Polo M. Impact of operator experience on intraoral digital scanning: A systematic review, meta-analysis, and meta-regression. Report of the Committee on Research in Fixed Prosthodontics of the American Academy of Fixed Prosthodontics. J Prosthet Dent. 2026;135(2):297.e1-297.e13. [DOI] [PubMed]
- 35.Martinez-Rodriguez C, Patricia JP, Ricardo OA, Alejandro IL. Personalized dental medicine: Impact of intraoral and extraoral clinical variables on the precision and efficiency of intraoral scanning. J Pers Med. 2020;10(3):92. [DOI] [PMC free article] [PubMed]
- 36.Alhammadi MS, Halboub E, Fayed MS, Labib A, El-Saaidi C. Global distribution of malocclusion traits: A systematic review. Dent Press J Orthod. 2018;23(6):40. e41-40 e10. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Almeida ER, Narvai PC, Frazao P, Guedes-Pinto AC. Revised criteria for the assessment and interpretation of occlusal deviations in the deciduous dentition: a public health perspective. Cad Saude Publica. 2008;24(4):897–904. [DOI] [PubMed] [Google Scholar]
- 38.Thilander B, Pena L, Infante C, Parada SS, de Mayorga C. Prevalence of malocclusion and orthodontic treatment need in children and adolescents in Bogota, Colombia. An epidemiological study related to different stages of dental development. Eur J Orthod. 2001;23(2):153–67. [DOI] [PubMed] [Google Scholar]
- 39.Shen L, He F, Zhang C, Jiang H, Wang J. Prevalence of malocclusion in primary dentition in mainland China, 1988–2017: a systematic review and meta-analysis. Sci Rep. 2018;8(1):4716. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Feng C-W, Hung C-C, Wang J-C, Lan T-H. Accuracy of different head movements of intraoral scanner in full arch of both maxilla and mandible. Appl Sci. 2021;11(17):8140.
- 41.Ochoa-Lopez G, Cascos R, Antonaya-Martin JL, Revilla-Leon M, Gomez-Polo M. Influence of ambient light conditions on the accuracy and scanning time of seven intraoral scanners in complete-arch implant scans. J Dent. 2022;121:104138. [DOI] [PubMed] [Google Scholar]
- 42.Anh JW, Park JM, Chun YS, Kim M, Kim M. A comparison of the precision of three-dimensional images acquired by 2 digital intraoral scanners: effects of tooth irregularity and scanning direction. Korean J Orthod. 2016;46(1):3–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Kapoor S, Puranik MP, Uma SR. Practice perspectives of left-handed clinical dental students in India. J Clin Diagn Res. 2016;10(10):ZC79–83. [DOI] [PMC free article] [PubMed] [Google Scholar]
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Data Availability Statement
All data generated or analyzed during this study are included in this published article.