Dental Scanner Research Articles

Clinical validation of the virtual splint registration workflow for craniomaxillofacial surgery.

Accurate registration is vital to transfer the virtual surgical plan during surgery. This study's goal was to present and clinically validate a virtual splint registration workflow. Ten dentate patients requiring revision surgery were included. Specific inclusion criterion for this study was the presence of at least two osteosynthesis screws on the orbital rim from a previous surgery. Dedicated orthognathic surgery software was used to fuse the maxillary dental scan with the computed tomography and generate a dental splint, which was imported into the navigation software and augmented with fiducial markers. Registration points were indicated virtually and the augmented splint was three-dimensionally printed. Intraoperatively, the splint was fitted on the maxillary dentition and the fiducial markers were used for registration. Accuracy of the registration procedure was quantified by calculating the difference between the landmarks acquired by indicating the pre-existing osteosynthesis material with the navigation pointer and in the virtual planning software. After acquisition of the landmarks, the screws were removed and surgery proceeded according to plan. A median target registration error of 1.53 mm was found. The advantages of the virtual splint registration workflow are that it does not require extensive computer-aided design skills or repeated preoperative imaging, and is non-invasive.

Feasibility and accuracy of single maxillary molar designed by an implicit neural network (INN)-based model: A comparative study.

This feasibility study aimed to develop and evaluate a novel implicit neural network (INN)-based model for the automatic reconstruction of biomimetic maxillary molar. A total of 500 sets of full dental scans containing intact right and left maxillary first molars (#3 and #14) and adjacent teeth were included in this study. The digital maxillary casts were duplicated: one set served as the original, while the other had one maxillary first molar removed. Two INN-based models were developed, including the point convolution for surface reconstruction (POCO-only) model and the POCO with point multilayer perceptron (POCO-PointMLP) model. Each model was trained with either 12,000 or 50,000 sampling points from the training dataset. Chamfer distance (CD), F-score, and Volumetric Intersection over Union (volumetric IoU) were used to evaluate the INN-based models and training outcomes. The best INN-based model was selected to generate 20 digital crown designs (GCs) automatically from randomly selected test datasets and compared with the digital crowns designed by experienced human laboratory technicians (TCs). Both GCs and TCs were compared with the original clinical crowns (OCs) using rootmean square(RMS) and color maps. Comparisons of RMS values were analyzed utilizing paired t-tests with statistical significance set at α=0.05. The performance of 2 INN-based models and training outcomes were evaluated and POCO-PointMLP trained with 50,000 sample points showed the best outcomes, with CD of 0.000119403, F-score of 0.919740566, and volumetric IoU of 0.943382978. This INN-based model was then selected to generate 20 digital crown designs (GCs) and compared with TCs for the RMS outcomes. The GCs exhibited numerically lower mean RMS compared to TCs without significant difference (0.2839±0.0307 versus 0.3026±0.0587mm, p=0.202). GCs and TCs both closely matched to the original clinical crowns (OCs). This study demonstrated the feasibility and accuracy of the proposed INN-based POCO-PointMLP model for automating the digital crown designs of maxillary first molars. The INN-based model-generated digital crown designs showed comparable 3D deviations to the ones designed by experienced human laboratory technicians, presenting a novel and efficient artificial neural network architecture for tooth digital design tasks with the expansion of training datasets, refined training hyperparameters, and integrated comprehensive assessments.

TSegLab: Multi-stage 3D dental scan segmentation and labeling.

This study introduces a novel deep learning approach for 3D teeth scan segmentation and labeling, designed to enhance accuracy in computer-aided design (CAD) systems. Our method is organized into three key stages: coarse localization, fine teeth segmentation, and labeling. In the teeth localization stage, we employ a Mask-RCNN model to detect teeth in a rendered three-channel 2D representation of the input scan. For fine teeth segmentation, each detected tooth mesh is isomorphically mapped to a 2D harmonic parameter space and segmented with a Mask-RCNN model for precise crown delineation. Finally, for labeling, we propose a graph neural network that captures both the 3D shape and spatial distribution of the teeth, along with a new data augmentation technique to simulate missing teeth and teeth position variation during training. The method is evaluated using three key metrics: Teeth Localization Accuracy (TLA), Teeth Segmentation Accuracy (TSA), and Teeth Identification Rate (TIR). We tested our approach on the Teeth3DS dataset, consisting of 1800 intraoral 3D scans, and achieved a TLA of 98.45%, TSA of 98.17%, and TIR of 97.61%, outperforming existing state-of-the-art techniques. These results suggest that our approach significantly enhances the precision and reliability of automatic teeth segmentation and labeling in dental CAD applications. Link to the project page: https://crns-smartvision.github.io/tseglab.

Evaluation of Root Canal Diameters and Radicular Wall Thickness of the Human Primary Molars by using Multidetector Computed Tomography: A Cross-sectional Study

Introduction: Paediatric endodontics is critical for preserving the primary tooth until its physiologic exfoliation and ensuring the child’s quality of life. Endodontic treatment of human primary molars with varying internal geometry of the root canal necessitates extensive knowledge and skills. Aim: To evaluate the diameters of the root canals and the radicular wall thickness of human primary molars using Multidetector Computed Tomography (MDCT). Materials and Methods: This cross-sectional study study was performed in the Department of Paediatric and Preventive Dentistry in collaboration with the Department of Oral and Maxillofacial Surgery, Department of Antaomy of Guru Nanak Institute, Kolkata, West Bengal,India on selected 64 human primary maxillary and mandibular molars through inclusion and exclusion criteria and grouped them (Group 1, maxillary 1st molars; Group 2, maxillary 2nd molars; Group 3, mandibular 1st molars; and Group 4, mandibular 2nd molars). After proper sterilisation, the teeth were mounted on a wax platform, and the mounted teeth block was scanned by a computed tomography scanner. Analysis of these Computed Tomography (CT) scan images was done through Denta Scan (GE Healthcare, USA) software. In each of the corresponding cross-sections, the diameters of the root and the root canals were measured at their greatest diameter. The radicular wall thickness of the roots was derived by subtracting the measured diameter of the root canals from that of the roots in their respective cross-sections. Descriptive statistical analysis (Student’s t-test) was performed. A p-value less than equal to 0.05 was considered statistically significant. Results: The maximum mean diameter of the canal was found in the mesiobuccal root canal of both primary mandibular first molars (1.16±0.22 mm at cervical third) and second molars (1.12±0.16 mm at cervical third); and the minimum diameter was found in the distolingual root canal of both primary mandibular first molars (0.87±0.12 mm at cervical third) and second molars (0.89±0.16 mm at cervical third). The mean radicular wall thickness of the roots gradually increased from the apical third to the cervical third of both primary molars. Conclusion: The maximum root canal diameter and radicular wall thickness were found in the cervical third of the palatal root of maxillary second molars. The study evaluates the mean maximum and minimum diameters of each canal of human primary molars and radicular wall thickness in different crosssections, which enables paediatric dental practitioners to establish effective paediatric endodontic treatment.

Advancing 3D dental scanning: The use of photogrammetry with light detection and ranging for edentulous arches

The advent of computer-aided design and computer-aided manufacturing (CAD-CAM) has necessitated the acquisition of digital scans. However, there are limitations and problems with acquiring accurate 3-dimensional (3D) casts from edentulous patients, especially in the presence of saliva. The purpose of this in vitro study was to develop a novel approach for obtaining 3D casts of edentulous arches by using 2-dimensional (2D) images as an alternative to traditional 3D scanners with and without light detection and ranging (LiDAR). This study comprised 6 groups, each consisting of 10 specimens. For the control group, 3D casts were generated by scanning edentulous mandibular molds using a dental laboratory scanner. Experimental groups included photogrammetry with and without LiDAR under various conditions (Groups PG360, PG120, LPG120, PG360S, LPG120S). For Groups PG120, LPG120, and LPG120S, a custom-made manikin was used. In all photogrammetry groups, images of each mold were captured with a mobile phone (iPhone 14 Pro Max). The casts from the experimental groups were superimposed onto those from the control group using the Blender Foundation software program (Version 3.6.1). The mean distances were calculated and statistically analyzed using 1-way ANOVA followed by the post hoc Tukey test (α=.05). The mean distances between the experimental groups and the control group varied significantly. The PG360 and PG120 groups showed a statistically significant difference from the control group (P<.001, 95% CI), with mean distances of 1.54 ±0.31 mm and 4.54 ±1.65 mm, respectively. The LPG120S group, which combined photogrammetry with LiDAR in the presence of artificial saliva, achieved a mean distance of 2.03 ±0.46mm, which was not significantly different from the control group (P=.501, 95% CI). The successful scanning of edentulous mandibular molds using a mobile phone was achieved through a combination of 2D images and LiDAR, covering a limited access angle of 120 degrees. Compared with other techniques, the method developed the most accurate 3D casts and was less susceptible to interference from saliva, a significant issue for intraoral scanners.

Tooth point cloud resampling method based on divergence index and improved Euclidean clustering rule.

In endodontic therapy, 3D Cone Beam Computerized Tomography (CBCT) and oral scan fusion models allow exact root canal channels and guidance. However, the point cloud model from CBCT has few data points and poor model features, limiting 3D fusion with oral scan data. Our aim to build a sub-regional point cloud resampling method and evaluate the precision of merging it with three-dimensional oral scan data.&#xD;Approach: Two molars and four incisors were resampled for this investigation. Based on point cloud density and curvature, the rebuilt model was separated into the crown and cervical cavities. Using crown surface morphology, Divergence Index (DI) was employed to determine resampling points based on point dispersion. Improved Euclidean Clustering Rule (IECR) downsamples each point using its weight and joins the two halves using Iterative Nearest Neighbour (ICP) to create a complete resampled point cloud. After aligning with the oral scanning model, the maximum error, maximum distance, average distance, and other characteristics are calculated to assess resampling. Additionally, a cross-entropy kernel-based point cloud reconstruction depth selection method is given to determine the appropriate reconstruction depth.&#xD;Main results: Applying the DI-IECR technique reduces the average distance between the resampled tooth point cloud and the point cloud generated by the dental scanner by around 20%. The maximum error remains same to that of the widely used method. This study also demonstrates that the use of the DI-IECR approach guarantees the complete representation of the coronal characteristics of the resampled reconstructed 3D model, rather than excessively focusing processing resources on pertinent but insignificant areas.&#xD;Significance: Point cloud data and crown features are balanced using DI-IECR. When registered with the oral scan model, CBCT-generated point clouds are more accurate and timely, making them a better intraoperative navigation model.

Influence of implant depth and scanning rod length on accuracy of digital impression for mandibular posterior single-tooth implant restoration

To study the influence of implant depth and scanning rod lengths on the accuracy of digital impression for single-tooth implant restoration of the mandibular posterior teeth. Five standard dental cast models with missing right mandibular first molar (46) were prepared with the subgingival implant depths of 0, 1, 3, 5 and 7 mm. ITI RC and ITI RC H11 scanning rods were connected to the replacement body and placed into the seating tract for scanning. The reference data were obtained using a 3D dental scanner, and the experimental data were obtained by 10 scans of each model using a digitized intraoral scanner. Geomagic Wrap 2021 was used to analyze the model data to test the trueness and precision of the models. The trueness did not differ significantly among the groups (P > 0.05). The implant depth of 1 mm achieved the highest impression precision (66.81±2.45 μm), and the depth of 0 mm resulted in a significantly lower precision (95.60±3.04 μm) than the depth of 1 and 3 mm. Starting from the subgingival depth of 1 mm, the precision of the scan decreased progressively with the increase of the implant depth. At the subgingival implant depth of 5 or 7 mm, the use of an extended rod significantly improved the scan precision. For single-tooth implant restoration of the mandibular posterior teeth, the implant depth can substantially affect the accuracy of digital impression, which decreases as the implant depth increases. For a deep implant, the use of a longer scanning rod can improve the scanning accuracy.

Source-detector trajectory optimization for FOV extension in dental CBCT imaging

In dental imaging, Cone Beam Computed Tomography (CBCT) is a widely used imaging modality for diagnosis and treatment planning. Small dental scanning units are the most popular due to their cost-effectiveness. However, these small systems have the limitation of a small field of view (FOV) as the source and detector move at a limited angle in a circular path. This often limits the FOV size. In this study, we addressed this issue by modifying the source-detector trajectory of the small dental device. The main goal of this study was to extend the FOV algorithmically by acquiring projection data with optimal projection angulation and isocenter location rather than upgrading any physical parts of the device. A novel algorithm to implement a Volume of Interest (VOI) guided trajectory is developed in this study based on the small dental imaging device's geometry. In addition, this algorithm is fused with a previously developed off-axis scanning method which uses an elliptical trajectory, to compensate for the existing constraints and to further extend the FOV. A comparison with standard circular trajectory is performed. The FOV of such a standard trajectory is a circle of 11 cm diameter in the axial plane. The proposed novel trajectory extends the FOV significantly and a maximum FOV of 19.5 cm is achieved with the Structural Similarity Index Measure (SSIM) score ranging between (≈98-99%) in different VOIs. The study results indicate that the proposed source-detector trajectory can extend dental imaging FOV and increase imaging performance, which ultimately results in more precise diagnosis and enhanced patient outcomes.

Comparison of the Accuracy and Precision of Digital Scans for Implant-Supported Maxillary Hybrid Prosthesis: An in vitro Study.

Numerous intraoral scanners (IOS) devices are currently used for intraoral impressions in prosthodontics. It is crucial to assess the accuracy and precision of these devices for clinical use. This in vitro study aimed to assess and compare the accuracy and precision of six IOS in the all-on-4 treatment concept. Trueness evaluation involved aligning the data from six dental scanners with the reference model. Precision analysis encompassed aligning the data from all six scanners within their respective groups. An edentulous maxillary model was utilized, with four implants placed at 12, 15, 22, and 25 teeth positions, simulating the all-on-4 approach using resin acrylic. Following the placement of scanbodies, each of the six IOSs (Primescan, Trios 3, Trios 4, Trios 5, Virtuo Vivo, and Medit i 700) performed eight scans of the model. An industrial scanner was employed for the control group. Data alignment and comparison were executed using the CloudCompare software (v2.11.3, General Public License of Telecom ParisTech, Paris, France). Statistical scrutiny encompassed the Shapiro-Wilk, Levene's, and Games-Howell tests. Among the scanners, Primescan exhibited the highest trueness (35.75 ± 26.08 μm), whereas the Medit i700 demonstrated superior precision (0.163 μm). IOS can be used to make dental impressions within the all-on-4 concept. More comprehensive and clinical studies are needed on this subject.

LMVSegRNN and Poseidon3D: Addressing Challenging Teeth Segmentation Cases in 3D Dental Surface Orthodontic Scans.

The segmentation of teeth in 3D dental scans is difficult due to variations in teeth shapes, misalignments, occlusions, or the present dental appliances. Existing methods consistently adhere to geometric representations, omitting the perceptual aspects of the inputs. In addition, current works often lack evaluation on anatomically complex cases due to the unavailability of such datasets. We present a projection-based approach towards accurate teeth segmentation that operates in a detect-and-segment manner locally on each tooth in a multi-view fashion. Information is spatially correlated via recurrent units. We show that a projection-based framework can precisely segment teeth in cases with anatomical anomalies with negligible information loss. It outperforms point-based, edge-based, and Graph Cut-based geometric approaches, achieving an average weighted IoU score of 0.97122±0.038 and a Hausdorff distance at 95 percentile of 0.49012±0.571 mm. We also release Poseidon's Teeth 3D (Poseidon3D), a novel dataset of real orthodontic cases with various dental anomalies like teeth crowding and missing teeth.

The Adoption and Impact of Dental Scanners in Dental Clinics across Punjab, India: Need of the Hour - A Questionnaire Study

Background: The aim of this article was to determine the adoption and impact of dental scanners in dental clinics across Punjab, India. Materials and Methods: A questionnaire was distributed via Google Forms to 450 dental clinics to gather data on the usage, benefits, and challenges associated with dental scanners. The findings highlight the growing trend of digital dentistry and its implications for clinical practice. Results: The survey conducted across 450 clinics in Punjab revealed that 60% of clinics do not use dental scanners, highlighting a significant gap in the adoption of this technology. Among the 40% of clinics that do use dental scanners, 25% utilize intraoral scanners, 10% use extraoral scanners, and 5% employ both types. The majority of these clinics have adopted dental scanners relatively recently, with only a small percentage using them for more than five years. Conclusion: Intra Oral Scanner (IOS) system is less time consuming when compared with other conventional methods. In terms of patient benefits, it reduces pain and discomfort. Ease of communicating with patients and educate them by providing video presentations before delivering any appliance or prostheses. Advanced technology is reshaping the future of digital dentistry, not only in orthodontics but also in other fields of dentistry as well.

Radiation protection in dental imaging: Evaluating the impact of the SABA thyroid shield during panoramic and cone beam computed tomography

Radiation protection in dental imaging: Evaluating the impact of the SABA thyroid shield during panoramic and cone beam computed tomography

DIGITAL IMPRESSION VERSUS CONVENTIONAL IMPRESSION FROM THE POINT OF VIEW OF DENTIST PREFERENCES

Nowadays digital impression represents a new breakthrough for dental offices, since more and more dentists use dental scanners. From the point of view of the dentist's preferences, there are not many studies regarding the use of the digital impression in comparison with the conventional impression. (1) Background: The aim of this study was to make a comparison between the digital impression and the classic impression and highlight the advantages and disadvantages of using the two methods, both in terms of the patient and the dentist; (2) Methods: To fulfill the objectives of this study, the Google Forms Platform was used to distribute a questionnaire regarding digital impression and classic impression. The questionnaire included 14 questions with the possibility of choosing only one answer and was addressed to dentists. (3) Results: 42 responses were recorded to the 14 questions contained in the questionnaire; (4) Conclusions: Intraoral scanning dental impression compared to traditional impression has less effect on patient gag effect, breathing difficulties, stress and anxiety, and results in better patient perception and comfort.

Comparison of Dental Arch Measurements on Conventional and Digital Study Model: A Validity, Reliability, and Reproducibility Study in Himachal Pradesh

Aim: The objective of this study was to assess the validity, reliability, and reproducibility of dental arch measurements between conventional study models and digital study models. Materials and Methods: The study sample comprised 50 pretreatment diagnostic conventional study models. Six dental arch measurements were assessed. The conventional study model’s measurements were done with digital vernier caliper (Precision, IP54). For three-dimensional (3D) model measurements, scanning of the conventional study models was done with the Maestro 3D dental scanner (Italy). Results: Measurement on conventional study models shows the mean value of the upper arch length was 72.33 and the mean value of the lower arch length was 64.68 for the conventional study model and the mean value of the upper arch length was 72.41 and the mean value of lower arch length was 63.11 for 3D model. There was a statistical difference between the measurements of overjet and overbite obtained between the study model and the 3D model. There was no statistical difference in intermolar width, intercanine width, and arch length of both the arches and curve of spee between conventional study models and 3D models. The Cronbach’s alpha (internal consistency) ranged from 0.81 to 1, intraclass correlation ranged from 0.70 to 0.99, and the concordance correlation coefficient ranged from 0.81–1, respectively, for various arch parameters. Conclusion: Digital study models were accurate, efficient, and easy-to-use alternatives to conventional study models. Hence, dental arch measurements of digital study models were as valid, reliable, and reproducible compared to conventional study models for all arch parameters except nonvalidity was observed for overjet and overbite measurement in the study.

Evaluating the accuracy of one-piece and three-piece 3D-printed indirect bonding transfer trays: An in vitro study.

Recent developments in computer-aided design/computer-aided manufacturing (CAD/CAM) and 3D printing have enabled the fabrication of digital indirect bonding (IDB) transfer trays. These modern products require thorough investigation. The aim of the study was to determine the accuracy of one-piece and three-piece IDB transfer trays in vitro. An initial dental scan (IDS) of a randomly selected patient with digitally positioned brackets served as the master scan (MS) for designing 16 IDB transfer trays of each type. They were 3D printed and used for bonding 448 brackets to the models. Subsequently, the models were scanned with a TRIOS® 3 Intraoral Scanner (3Shape A/S, Copenhagen, Denmark), producing actual scans (ASs). The accuracy of bracket positioning was measured digitally on both MSs and ASs. The measurements were compared to the Objective Grading System for dental casts provided by the American Board of Orthodontics (ABO). The 2 types of IDB transfer trays showed comparable accuracy. All linear errors were within the clinically acceptable range, whereas the angular measurements demonstrated significant variability, resulting in clinically unacceptable transfer errors that ranged from 3.3% to 90.3%. The study results cannot be unconditionally extrapolated to other types of IDB transfer trays due to the diversity of their properties and features. The study evaluated the in vitro accuracy of IDB transfer trays. The revealed number of errors may be even higher in vivo due to limitations in visibility, salivary flow, interference from the tongue, and difficulties in achieving a proper fit of the IDB transfer tray to the teeth.

Evaluation of the accuracy of automated tooth segmentation of intraoral scans using artificial intelligence-based software packages

The accuracy of tooth segmentation in intraoral scans is crucial for performing virtual setups and appliance fabrication. Hence, the objective of this study was to estimate and compare the accuracy of automated tooth segmentation generated by the artificial intelligence of dentOne software (DIORCO Co, Ltd, Yongin, South Korea) and Medit Ortho Simulation software (Medit Corp, Seoul, South Korea). Twelve maxillary and mandibular pretreatment dental scan sets comprising 286 teeth were collected for this investigation from the archives of the Department of Orthodontics, Faculty of Dentistry, Alexandria University. The scans were imported as standard tessellation language files into both dentOne and Medit Ortho Simulation software. Automatic segmentation was run on each software. The number of successfully segmented teeth vs failed segmentations was recorded to determine the success rate of automated segmentation of each program. Evaluation of success and/or failure was based on the software's identification of the teeth and the quality of the segmentation. The mesiodistal tooth width measurements after segmentation using both tested software programs were compared with those measured on the unsegmented scan using Meshmixer software (Autodesk, San Rafael, Calif). The unsegmented scans served as the reference standard. A total of 288 teeth were examined. Successful identification rates were 99% and 98.3% for Medit and dentOne, respectively. Success rates of segmenting the lingual surfaces of incisors were significantly higher in Medit than in dentOne (93.7% vs 66.7%, respectively; P<0.001). DentOne overestimated the mesiodistal width of canines (0.11 mm, P= 0.032), premolars (0.22 mm, P< 0.001), and molars (0.14 mm, P= 0.043) compared with the reference standard, whereas Medit overestimated the mesiodistal width of premolars only (0.13 mm, P= 0.006). Bland-Altman plots showed that mesiodistal tooth width agreement limits exceeded 0.2 mm between each software and the reference standard. Both artificial intelligence-segmentation software demonstrated acceptable accuracy in tooth segmentation. There is a need for improvement in segmenting incisor lingual tooth surfaces in dentOne. Both software programs tended to overestimate the mesiodistal widths of segmented teeth, particularly the premolars. Artificial intelligence-segmentation needs to be manually adjusted by the operator to ensure accuracy. However, this still does not solve the problem of proximal surface reconstruction by the software.

Automatic removal of soft tissue from 3D dental photo scans; an important step in automating future forensic odontology identification

The potential of intraoral 3D photo scans in forensic odontology identification remains largely unexplored, even though the high degree of detail could allow automated comparison of ante mortem and post mortem dentitions. Differences in soft tissue conditions between ante- and post mortem intraoral 3D photo scans may cause ambiguous variation, burdening the potential automation of the matching process and underlining the need for limiting inclusion of soft tissue in dental comparison. The soft tissue removal must be able to handle dental arches with missing teeth, and intraoral 3D photo scans not originating from plaster models. To address these challenges, we have developed the grid-cutting method. The method is customisable, allowing fine-grained analysis using a small grid size and adaptation of how much of the soft tissues are excluded from the cropped dental scan. When tested on 66 dental scans, the grid-cutting method was able to limit the amount of soft tissue without removing any teeth in 63/66 dental scans. The remaining 3 dental scans had partly erupted third molars (wisdom teeth) which were removed by the grid-cutting method. Overall, the grid-cutting method represents an important step towards automating the matching process in forensic odontology identification using intraoral 3D photo scans.

Evaluating the influence of palate scanning on the accuracy of complete-arch digital impressions–An in vitro study

To assess the impact of including the palate and the number of images recorded during intraoral digital scanning procedure on the accuracy of complete arch scans. An experienced operator conducted 40 digital scans of a 3D printed maxillary model and divided them into two groups: 20 with inclusion of the palate (PAL) and 20 without (NPAL). Each set of scans was performed using an intraoral scanner (IOS) (Trios 5; 3Shape A/S; Copenhagen, Denmark). The resulting STL files were imported into the Geomagic Control X software (3D Systems, Rock Hill, SC, USA) for accuracy comparison. A reference STL file was created using a 3Shape E3 laboratory scanner (3Shape Scanlt Dental 2.2.1.0; Copenhagen, Denmark). The number of images captured was recorded during the scanning procedure. In the case of the right side no statistically significant difference in trueness was detected (84 µm ± 45.6 for PAL and 80.4 ± 40.4 µm for NPAL). In the case of the left side no significant difference in trueness was observed (215.1 ± 70.2 µm for PAL and 233.9 ± 70.7 µm for NPAL). In the case of the arch distortion a statistically significant difference in trueness was seen between the two types of scans (135.3 ± 71.9 µm for PAL and 380.4 ± 255.1 µm for NPAL). The average number of images was 831.25, and 593.8 for PAL and NPAL, respectively. Scanning of the palatal area can significantly improve the accuracy of dental scans in cases of complete arches. In terms of the number of images, based on the current results, obvious conclusions could not be drawn, and further investigation is required. Scanning the palate may be beneficial for improving the accuracy of intraoral scans in dentate patients. Consequently, this should be linked to an appropriate scanning strategy that predicts palatal scanning.

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.

CBCT and modern intraoral scanners as tools for developing comprehensive, interdisciplinary treatment plans.

The aim of this narrative literature review is to present the possibilities of using cone beam computed tomography (CBCT) and 3D dental scanners to prepare comprehensive, interdisciplinary treatment plans. Scanners are instruments whose usage seems to be a key element of modern digital dentistry. Their importance in orthodontic treatment with overlay appliances, planning modern prosthetic treatments (CAD/CAM) and implantology cannot be overestimated. These scanners allow for accurate imaging of the tooth structures and their positioning independently in the maxilla and mandible as well as in the occlusion. As a result, dentists can plan treatment, e.g., in the case of the need to implant dental implants, prosthetic crowns or orthodontic braces. Dentistry was revolutionized to a similar extent by the introduction of CBCT to everyday diagnostics, which is the most advanced imaging technology that provides even more detailed images in 3 dimensions. Its use has enabled a wider and more precise range of diagnostics, which in turn has improved the quality of multidisciplinary treatment planning. This paper explains how scanners and CBCT can be used in orthodontics and prosthetics based on the articles found in 3 databases: PubMed, Scopus and Embase. The review included 28 articles on the aforementioned topics and was presented with a brief description of the content of each article.