3D Intraoral Scanner Research Articles

Effect of crown seating methods on the remnant cement in the subgingival region of a cement-retained implant crown

This study aimed to investigate the effects of crown seating speed, crown seating force, quantity of cement used, and type of implant cement on the amount of remnant cement in the subgingival region (RCS) after cementation. Cement-retained implant crowns were cemented to titanium abutments using the following methods: four types of implant cement (TBN: TEMP BOND NE, NR: NEXUS RMGI, ME: MAXCEM ELITE, and U200: RELYX U200), three quantities of cement (0.02 ml, 0.04 ml, and 0.06 ml), three crown seating speeds (5 mm/s, 10 mm/s, and 15 mm/s), and two crown seating forces (25 N, 50 N). The surface area and length of the RCS were measured using a 3D intraoral scanner. The total RCS weight was measured using an analytical balance. The RCS increased significantly as the seating speed increased, the seating force increased, and the quantity of cement increased (p < 0.05). The RCS values were the highest for TBN, followed by U200, NR, and ME (p < 0.05). The lower seating speed, smaller quantity of cement used, and smaller seating force applied in cement-retained implant restorations minimized the RCS in cement-retained prostheses. The type of cement is a factor that determines the aspects of the RCS.

Accuracy of Tooth Segmentation in the Digital Kesling Setup of Two Different Software Programs: A Retrospective Study.

Introduction Precise virtual setup creation and orthodontic appliance fabrication depend on accurate teeth segmentation from intraoral scans. This accuracy is also fundamental for successful orthodontic treatment, as it ensures correct diagnosis and optimal treatment planning. A number of software packages that facilitate the building of virtual setups have been made available in recent years. The performance of these software packages on automatic tooth segmentation has not been widely studied. Hence, the aim of this study was to evaluate the accuracy of automated teeth segmentation in the digital Keslingsetup of Ortho Studio (Maestro 3D Dental Studio, Bordeaux, France) and OrthoAnalyzer (3Shape, Copenhagen, Denmark) software systems. Materials and methods All the scans were taken from the same intraoral scanner (Runyes 3D intraoral scanner; Runyes Medical, Ningbo, China). The scans were stored and imported as stereolithography (STL) files into the Maestro Ortho Studio and 3Shape OrthoAnalyzer software systems. Subsequently, the digital photos underwent alignment in both software applications, an essential stage in each respective workflow prior to any further processing. The digitized images were automatically segmented in Maestro and 3Shape software by a single researcher. For each software interface, the accuracy of teeth segmentation was assessed. An independent t-test, with a significance level set at p < 0.05, was used to evaluate the statistical significance between the two software segmentations. Results The total number of teeth segmented by both software programs utilizing the 12 intraoral scans was 336 for both groups. Successful identification of the tooth segments was 98.21% (n = 330) for 3Shape software and 98.8% (n = 332) for Maestro software. There was no significant difference in the accuracy of determining the tooth segmentations between anterior and posterior teeth, respectively, between both groups, with a p-value of 0.523. Conclusion There were no statistically significant differences between the two software programs, and both demonstrated high success rates for auto-tooth segmentation. Although both programs had excellent success rates, Maestro 3D performed more accurately than 3Shape OrthoAnalyzer.

The effect of imaging programs and segmentation methods on the accuracy of volume measurements of teeth

ObjectiveThis study investigated whether there are differences between software programs, voxel sizes, segmentation techniques, and intraoral scanners in terms of volume measurement in incisor teeth. Study DesignThirty extracted teeth were scanned using a 3D intraoral scanner. Physical volumes were measured using the water displacement method (WDM) as the gold standard. Cone Beam Computed Tomography (CBCT) images were taken in two voxel sizes (0.3- 0.4 voxel). The volumes of the teeth were calculated using the manual segmentation technique in the 3D Doctor and ImageJ (Fiji) Program, manual and automatic segmentation methods in the ITK-Snap program, and automatic segmentation methods in the 3D Slicer program. The data were analyzed using the Statistical Package for Social Sciences (SPSS, v 20.0). ResultsThere is a significant difference between WDM and 3D Doctor volume measurements at the voxel size of 0.4 (p <0.05). The 3D Doctor program showed more than 13% difference compared to the WDM. There is no significant difference between WDM, intraoral scanner, ITK-Snap, ImageJ (Fiji), 3D Slicer, and 3D-Doctor volume measurements at the voxel size of 0.3. ConclusionMeasurements with manual segmentation in ITK-Snap and ImageJ programs give the closest results to the physical volume measurements of the teeth. The automatic segmentation method in ITK-Snap and 3D Slicer programs may be preferred due to its ease of use and less time consuming. In the 3D Doctor software, volume measurements tend to increase with larger voxel size. Statement of Clinical RelevanceDifferent software programs, voxel size, and segmentation techniques used in CBCT images affect the measurement results of tooth volumes. Very close to physical volume was obtained with the intraoral scanner. Measurements obtained through manual segmentation yield more accurate results.

Comparison of Conventional and Digital Impression Methods for Measuring Mesiodistal Teeth Dimensions: An Analytical Cross-sectional Study

Introduction: We aim to determine and compare the correlation between conventional and digital impression methods for measuring mesiodistal teeth dimensions. Methods: This cross-sectional analytical study was conducted on a total of 120 dental arch samples with less than 5 mm of tooth crowding, complete teeth on the dental arch, no missing teeth, and no fillings on the mesial or distal sides at Can Tho University of Medicine and Pharmacy. Conventional (extra-fast alginate) and digital impressions using a 3D intraoral scanner (CEREC Primescan) were taken from all participants, and the dimensions of the mesiodistal teeth were measured. Using R software, Pearson’s correlation coefficients were used to analyze the correlation between conventional and digital impression methods. Result: The pooled correlation for the maxilla was 0.8062 [95% confidence interval (CI): 0.7751–0.8334] (very strong positive correlation); for the mandibular, it was 0.7645 (95% CI: 0.7165–0.8054) (strong positive correlation), and for both jaws was 0.7863 (95% CI: 0.7581–0.8115) (strong positive correlation). Conclusion: In the Vietnamese population measurement of mesiodistal tooth width using a digital dental scanner can be used instead of conventional plaster models.

Occlusal caries detection and monitoring using a 3D intraoral scanner system. An in vivo assessment

ObjectiveTo assess the agreement in detecting and monitoring occlusal caries over thirty months using conventional visual and radiographic assessment and an intraoral scanner system which supports automated caries scoring. MethodsNinety-one young participants aged 12–19 years were included in the study. All occlusal surfaces were examined visually, radiographically (when indicated), and scanned with the TRIOS 4 intraoral scanner. TRIOS Patient Monitoring software (vers. 2.3, 3Shape TRIOS A/S, Denmark) was used for automated caries detection on the 3D digital models. ResultsFifty-five of the study participants were re-examined after 30-months. Significant differences regarding caries detection were found between the conventional methods and the automated caries scoring system (p < 0.01), with moderate positive percent agreement (49–61%) and high negative percent agreement (87–98%). All methods reported significant caries progression over the follow-up period (p < 0.01). However, the automated system showed significantly more caries progression than the other methods (p < 0.01). ConclusionsThe software for automated caries detection and classification showed moderate positive agreement and strong negative agreement with the conventional methods considering both the baseline and the follow-up assessments. The automated caries scoring system detected significantly fewer caries lesions and tended to underestimate the caries severity. All methods indicated significant caries progression over the follow-up period, while the automated system detected more caries progression. Clinical significanceThe TRIOS system supporting automated occlusal caries detection and classification can assist in detecting and monitoring occlusal caries on permanent teeth as a complementary tool to the conventional methods. However, the operator should be aware that the automated system shows a tendency to underestimate the caries presence and lesion severity.

Bitemarks and 3D scanner: An objective comparison for bitemarks. A pilot study

The marks left by the bite of a subject (bitemark) represent an unequivocal character, being useful in identifying the possible perpetrator of a crime. To date, the analysis of bitemarks is mainly based on the direct vision of photographic finds and on the visual comparison with the teeth of the hypothetically responsible person. However, the bitemark also retains three-dimensional characteristics detectable with a 3D scanner. In this pilot study, an innovative method of bitemark analysis, utilizing a three-dimensional scanner and some software, will be introduced, enabling a quantitative comparison of bitemarks and their corresponding human dentitions. For this purpose, 10 complete plaster models (human dentitions) of 10 adult subjects were used to make 20 experimental bitemarks on dentistry wax. All materials were individually scanned with the iTero® 3D scanner and reworked with MeshMixer software. A visual analysis of the characteristics and a computerized analysis with the CloudCompare software were also performed. Study showed a different trend of the distribution curves of the points which were obtained comparing the human dentition and coincident and non-coincident bitemarks. Current results support that the intraoral 3D scanner allows the fast record and the preservation of the three-dimensional characteristics of the bitemarks, and it allows computerized analyses to be carried out.

A full-set tooth segmentation model based on improved PointNet++

Segmentation of a complete set of teeth from three-dimensional (3D) intra-oral scanner images is a crucial step in tooth identification procedures. In large-scale disasters with many victims, teeth are often the preferred and reliable source for victim identification due to their hard and non-deformable characteristics. In this paper we present a study on the automatic segmentation of a complete set of teeth from intra-oral scanner images. We propose a tooth segmentation method based on an improved PointNet++ architecture. To address the problem of inadequate segmentation capability of the teeth-gingival boundary of PointNet++, we introduce a single-point preliminary feature extraction (SPFE) module to better preserve the subtle details that may be overlooked by the original PointNet++ model. In addition, a weighted-sum local feature aggregation (WSLFA) mechanism is proposed to replace the max pooling in PointNet++ to better perform feature aggregation. The experimental results on 52 testing datasets using the network trained on 160 annotated 3D intra-oral scanner images demonstrate that our improved PointNet++ method achieves a segmentation accuracy of 97.68%, and performs well under different dental conditions.

Pemindai intraoral 3d pada Ortodonti: tinjauan naratif

Background: Nowadays, 3D intraoral scanners have become an ongoing trend in the development of digital orthodontics. Its utility is to minimize patient discomfort during scanning while maximizing the accuracy of the images and shortening the diagnosis time. Objective: To determine the accuracy of 3D intraoral scanners in orthodontics in cases of crowded teeth. Methods: The study was conducted by narrative review by PRISMA guidelines and using electronic databases, PubMed, Google Scholar, and ScienceDirect which were then processed and reviewed. Results: Four journals were found, which were then presented in the extraction table. Each journal discusses the accuracy of 3D intraoral scanners in orthodontics in cases of crowded teeth using their respective research methods. Conclusion: The 3D intraoral scanner showed no significant difference in the accuracy of the study models in the case of crowded teeth. Clinically, the 3D intraoral scanner is acceptable for diagnosis and treatment planning in dentistry.

Comparing the accuracy and precision of digital model transfer methods used in virtual orthognathic planning

Objectives: The hard and soft tissues of the head and neck, dentition, and temporomandibular joint are the determining elements in orthognathic surgery planning. The accuracy and precision of the relationship between the jaws at the beginning of treatment and their final position depend on an accurate dentition record. The aim of this study was to determine the simplest and most feasible virtual dental model transfer method for three-dimensional orthognathic planning according to clinical applicability, technical difficulty, effective costs, accuracy, and precision. Material and Methods: A total of ten spherical porcelain markers were placed in plaster models of the maxilla and mandible of a patient. The models were scanned using an intraoral optical scanner, an extraoral digital model scanner, and cone-beam computerized tomography. To evaluate reliability, each measurement was repeated 10 times at 1-week intervals and the distances between points were measured horizontally and vertically. The findings obtained in the study were evaluated statistically using IBM SPSS Statistics 2.2 program. Results: Measurements obtained with the extraoral model scanner did not differ from the digital caliper method (P &gt; 0.05), while there were significant differences between the digital caliper and the other methods (intraoral 3D scanner P = 0.000; CBCT P = 0.001). Conclusion: Although all of the measurements showed high consistency among all methods, the most accurate results were obtained with the extraoral digital model scanner. (r = 0.99, P = 0.01, P &lt; 0.05).

Characteristic changes of the palatal rugae following orthodontic treatment

BackgroundBecause of the individuality of the palatal rugae, they are applicable for human identification purposes the same as the fingerprints. Therefore, it is important to assess the effect of different factors on rugae characteristics. We assessed the characteristic changes of the palatal rugae following extraction and expansion during orthodontic treatments. Before and after orthodontic treatment dental casts of 30 patients were scanned by 3Shape Trios 3D intraoral scanner, and superimposed in Geomagic X 2018 software. Displacement of 13 reference points was analyzed in three dimensions. The length of the palatal rugae was also measured. SPSS version 22.0 was applied for data analysis, using independent t test, sample t test, and chi-square test.ResultsThe alterations in the mean palatal rugae length were significant in the first right and third right ridges after extraction, and right and left second ridges after expansion treatment (P < 0.05). Changes in coordinates in the X, Y, and Z axes were significant at 8 points on the rugae after extraction, and 5 points after expansion treatment (P < 0.05). The changes in the sum of means of displacements were significant at all 13 points after extraction and expansion treatments (P < 0.05).ConclusionsThe palatal rugae do not remain stable during expansion and extraction orthodontic treatments. Some changes occurred in length, and the most medial and most lateral points on each ridge were displaced. The length of the incisive papilla did not change during treatment; thus, it can serve as a stable landmark. If we consider palatal rugae as an individual landmark, we can refer the patients after orthodontic treatment for scanning of their palatal rugae to document their post treatment pattern; but it should be noted that treatment relapse may change it again.

Occlusal caries detection on 3D models obtained with an intraoral scanner. A validation study

ObjectivesTo evaluate the diagnostic performance of visual caries assessment on 3D dental models obtained using an intraoral scanner and to compare it with the performance of the clinical visual inspection. MethodsFifty-three permanent posterior teeth scheduled for extraction were randomly selected and included in this study. One to three independent examination sites on the occlusal surface of each tooth were clinically inspected using International Caries Detection and Assessment System (ICDAS) criteria. Afterwards, the examined teeth were scanned intraorally with a 3D intraoral scanner (TRIOS 4, 3Shape TRIOS A/S, Copenhagen, Denmark) using white and blue-violet light (415 nm wavelength) to capture the colour and fluorescence signal from the tissues. Six months after the clinical examination, the same examiner conducted the on-screen assessment of the obtained 3D digital dental models at the selected examination sites using modified ICDAS criteria. Both tooth colour and fluorescence texture with high resolution were assessed. Lastly, an independent examiner conducted the histological examination of all teeth after extraction. Using histology as the reference test, Sensitivity (SE), Specificity (SP), Accuracy (ACC), area under the Receiver Operating Characteristic (ROC) curve, and Spearman's correlation coefficient were calculated for the clinical and on-screen ICDAS assessments. ResultsThe ACC values of the evaluated methods varied between 0.59-0.79 for initial caries lesions and 0.77-0.99 for moderate-extensive caries lesions. Apart from SE values corresponding to caries in the inner half of enamel, no significant difference was observed between clinical visual inspection and on-screen assessment. In addition, no difference was found in the assessment of 3D models with tooth colour alone or supplemented with fluorescence for all the evaluated diagnostic measures. ConclusionsOn-screen visual assessment of 3D digital dental models with tooth colour or fluorescence showed a similar diagnostic performance to the clinical visual inspection when detecting and classifying occlusal caries lesions on permanent teeth. Clinical significance3D intraoral scanning can aid the detection and classification of occlusal caries as part of patient screening and can potentially be used in remote caries assessment for clinical and research purposes.

Secondary Caries Detection and Treatment Decision according to Two Criteria and the Impact of a Three-Dimensional Intraoral Scanner on Gap Evaluation

This study aimed to evaluate the in vitro performance in detecting caries around composite restorations in permanent posterior teeth and the impact of treatment decisions of two visual criteria: International Dental Federation (FDI) criteria and the Caries Associated with Restorations and Sealants (CARS) system. The correlation among secondary caries and the presence of gap measured with a three-dimensional (3D) intraoral scanner was also aimed. One hundred sixteen teeth were assessed by a trained and calibrated examiner according to the FDI criteria or CARS system. A second examiner measured the gap on the 3D models using specific software. The reference standard was the histological examination performed by a third examiner blind to the other evaluations. Other 30 extracted permanent teeth were used only to assess the reproducibility of the methods. The same random sample was selected for re-examination by all three methods, and there were 7 days between the examinations. Unweighted and weighted kappa tests were conducted to assess intra-examiner reproducibility. Spearman’s correlation coefficient (Rho) and 95% confidence intervals (95% CI) were calculated between the histological examination and scores obtained with FDI criteria, CARS system, and treatment decisions. Spearman’s correlation between the visual and scanner evaluation with the reference standard was performed. Spearman’s rank correlation analyses were conducted independently between the gap evaluated and measured by the visual inspection with the gap assessed using the scanner. The reproducibility of the visual score systems reached high values. Spearman’s correlation coefficients (Rho; 95% CI) between the following variables versus histology were the FDI presence of caries (0.65; 0.53–0.74); CARS scores (0.65; 0.52–0.74); FDI treatment decision (0.46; 0.31–0.59); and CARS treatment decision (0.62; 0.49–0.72). Rho (95% CI) between histology and the gap assessment by the visual inspection was 0.59 (0.45–0.70), the gap measurement by the visual inspection was 0.49 (0.33–0.62), and the gap measured by the scanner was 0.37 (0.18–0.53). Both visual criteria present similar performance in detecting caries. The correlation among treatment decisions is moderate for the FDI and CARS criteria, and both are moderately correlated with lesion depth, with a slight CARS superiority. However, visual examination presents better performance than the 3D intraoral scanner on gap size assessment.

Accuracy of planned tooth movement with lingual straight wire technique.

To analyze the accuracy of planned tooth movements of torque, tip, rotations, and transverse width values with lingual straight-wire technique. 40 Caucasian subjects with mean age of 23.9 years, consecutively treated in private practice with a lingual straight-wire appliance (STb, Ormco, Glendora, Calif) were evaluated. Maxillary and mandibular dental casts were taken before treatment (T0), in the setup (T1), and at the end of treatment (T2) and scanned with an intraoral 3D scanner Carestream CS 3600 (Carestream Dental, Atlanta, GA). Virtual models at the three time points were superimposed on T1 using 3D software, and the coordinates were exported as a set of x, y, and z values. Angular and linear measurements were analyzed to measure torque, tip, rotation movements, and transverse intra-arch widths. Changes among the three time points were analyzed with Friedman's nonparametric test. A general increase in torque was recorded in the setup and in the final result, except for the maxillary molars. Torque, tip, and rotation movement mean accuracy was ≥84% for incisors, canines, and premolars. A general increase in transverse width was measured in the setup and in the final models, except for the upper second molars, which showed reduced transverse width during treatment. The movements planned in the setup to obtain the ideal torque, tip, and rotations actually occurred, except for second molars, which showed less accuracy. Planned expansion of the arches occurred only partially.

Two-Stream Graph Convolutional Network for Intra-Oral Scanner Image Segmentation.

Precise segmentation of teeth from intra-oral scanner images is an essential task in computer-aided orthodontic surgical planning. The state-of-the-art deep learning-based methods often simply concatenate the raw geometric attributes (i.e., coordinates and normal vectors) of mesh cells to train a single-stream network for automatic intra-oral scanner image segmentation. However, since different raw attributes reveal completely different geometric information, the naive concatenation of different raw attributes at the (low-level) input stage may bring unnecessary confusion in describing and differentiating between mesh cells, thus hampering the learning of high-level geometric representations for the segmentation task. To address this issue, we design a two-stream graph convolutional network (i.e., TSGCN), which can effectively handle inter-view confusion between different raw attributes to more effectively fuse their complementary information and learn discriminative multi-view geometric representations. Specifically, our TSGCN adopts two input-specific graph-learning streams to extract complementary high-level geometric representations from coordinates and normal vectors, respectively. Then, these single-view representations are further fused by a self-attention module to adaptively balance the contributions of different views in learning more discriminative multi-view representations for accurate and fully automatic tooth segmentation. We have evaluated our TSGCN on a real-patient dataset of dental (mesh) models acquired by 3D intraoral scanners. Experimental results show that our TSGCN significantly outperforms state-of-the-art methods in 3D tooth (surface) segmentation.

Investigating the effect of bruxism on maxillary arch length and width in children using three-dimensional digital model analysis

BackgroundBruxism is defined as repetitive jaw-muscle activity characterized by the grinding and clenching of teeth. The prevalence of bruxism in children is extensive, and it can cause irregularities in dental arches. The study aimed to investigate the presence of any effects of bruxism on maxillary arch length and width in children using three-dimensional (3D) digital model analysis.MethodThis study evaluated 30 children with bruxism. For every child with bruxism, a case control without bruxism was selected and matched for gender, age, and dentition. Digital models of the patients’ maxilla were obtained with a 3D intraoral scanner, and width and length measurements between the reference points on the maxilla were obtained on the digital models.ResultsThe mean age of the study group was 9.13 ± 1.27. Insıgnificance differences were found between females and males within and between groups in terms of maxillary width and length. Insignificant difference was found between the control and study groups when the lengths of 3R-3L, 4R-4L, 5R-5L, 6R-6L, and IP-M were compared (p > 0.05).ConclusionBased on the study results, there were no differences in the maxillary arch length and width in patients with bruxism and patients without bruxism.

Automated caries detection in vivo using a 3D intraoral scanner

The use of 3D intraoral scanners (IOS) and software that can support automated detection and objective monitoring of oral diseases such as caries, tooth wear or periodontal diseases, is increasingly receiving attention from researchers and industry. This study clinically validates an automated caries scoring system for occlusal caries detection and classification, previously defined for an IOS system featuring fluorescence (TRIOS 4, 3Shape TRIOS A/S, Denmark). Four algorithms (ALG1, ALG2, ALG3, ALG4) are assessed for the IOS; the first three are based only on fluorescence information, while ALG4 also takes into account the tooth color information. The diagnostic performance of these automated algorithms is compared with the diagnostic performance of the clinical visual examination, while histological assessment is used as reference. Additionally, possible differences between in vitro and in vivo diagnostic performance of the IOS system are investigated. The algorithms show comparable in vivo diagnostic performance to the visual examination with no significant difference in the area under the ROC curves (p>0.05). Only minor differences between their in vitro and in vivo diagnostic performance are noted but no significant differences in the area under the ROC curves, (p>0.05). This novel IOS system exhibits encouraging performance for clinical application on occlusal caries detection and classification. Different approaches can be investigated for possible optimization of the system.

3D Dental model segmentation with graph attentional convolution network

Precisely segmenting teeth from digitized 3D dental models is an essential task in computer-aided orthodontic surgical planning. In recent years, various deep learning-based methods have been proposed to process dental models for teeth segmentation, however, these methods usually ignore or coarsely model the dependency between vertices/mesh cells in local space, which fails to exploit local geometric details that are critical to capture complete teeth structure. In this paper, we propose a specific end-to-end network for teeth segmentation on 3D dental models. By constructing a graph for the raw mesh data, our network adopts a series of graph attentional convolution layers and a global structure branch to extract fine-grained local geometric feature and global feature, respectively. Subsequently, these two features are further fused to learn comprehensive information for cell-wise segmentation tasks. We have evaluated our network on a real-patient dataset of dental models acquired through 3D intraoral scanners, and experimental results show that our method outperforms state-of-the-art deep learning methods for 3D shape segmentation.

Occlusal stresses in beveled versus non-beveled tooth preparation

The purpose of this study is to investigate occlusal stresses in beveled versus non-beveled tooth preparation in class I restoration when using composite restorative material. Completed plastic tooth preparations were scanned using a 3D intra-oral scanner. Then the files were further processed using meshing software. The Finite Element Method (FEM) was used to model the performance of one non-beveled and two different types of beveled restorations which were being subjected to the chewing forces. The performed FEM simulations illustrated the benefits of the beveled restorations for composite Class I restorations. This is an experimental study. Further research is needed to provide more information to determine the benefits of beveling in teeth preparation both in in-vitro and in-vivo studies. The outcome of this study illustrates the advantage of using bevel over no bevel during class I cavity preparation.

Correlation of Lower Third Molar Angulation and Posterior Space Discrepancy with Mandibular Anterior Crowding using 3d Intra Oral Scanner

&#x0D; &#x0D; &#x0D; &#x0D; OBJECTIVES:&#x0D; To objectively determine the correlation of lower third molar angulation and posterior space discrepancy with mandibular anterior crowding digitally using intraoral scanner.&#x0D; METHODOLOGY:&#x0D; Cross sectional analytical study conducted at Rehman College of Dentistry (RCD) Peshawar from15th November 2020 till 15th December 2020. Lower third molar angulation and posterior space discrepancy were measured by IC 3.1 measure software, using digital Orthopantomograms (OPG). Digitally scanned models by (CS 3600 intraoral scanner) were analyzed using (CS 3600 3D software). Sample was divided into Control (N=30; perfect alignment), Experimental (N=30; crowding &gt;1mm) groups, based on Little’s Irregularity Index. Both lower third molar angulation and posterior space discrepancy (Ganss Ratio) were correlated with lower anterior crowding using Pearson’s Correlation. To determine any difference between two groups independent t-test was used.&#x0D; RESULTS:&#x0D; Statistically insignificant difference and a weak correlation were found for Ganss ratio and third molar angulation in both control and experimental groups.&#x0D; CONCLUSION:&#x0D; Third molar angulation and the retro molar space has no direct effect on the lower anterior crowding.&#x0D; &#x0D; &#x0D; &#x0D;

Comparative Assessment of Precision of Prepared Single Tooth using Shining 3D Intraoral Scanner and Conventional Impressions Made using Polyvinyl Siloxane and Polyether Impression Material – An In vitro Study

Comparative Assessment of Precision of Prepared Single Tooth using Shining 3D Intraoral Scanner and Conventional Impressions Made using Polyvinyl Siloxane and Polyether Impression Material – An <i>In vitro</i> Study