OBJECTIVES
To describe the factors related to the operator skills and decisions that influence the scanning accuracy of intraoral scanners (IOSs). A new classification for these factors is proposed to facilitate dental professionals' decision making when using IOSs and maximize the accuracy and reliability of intraoral digital scans.


OVERVIEW
Each IOS system is limited by the hardware and software characteristics of the selected device. The operator decisions that can influence the accuracy of IOSs include the scanning technology and system selection, scanning head size, calibration, scanning distance, exposure of the IOS to ambient temperature changes, ambient humidity, ambient lighting conditions, operator experience, scanning pattern, extension of the scan, cutting off, rescanning, and overlapping procedures.


CONCLUSIONS
The knowledge and understanding of the operator factors that impact scanning accuracy of IOSs is a fundamental element for maximizing the accuracy of IOSs and for successfully integrating IOSs in daily practices.


CLINICAL SIGNIFICANCE
Operator skills and clinical decisions significantly impact intraoral scanning accuracy. Dental professionals must know and understand these influencing operator factors for maximizing the accuracy of IOSs.

A guide for maximizing the accuracy of intraoral digital scans. Part 1: Operator factors.

OBJECTIVES
Within the development of digital technologies, dental professionals aim to integrate virtual diagnostic articulated casts obtained by using intraoral scanners (IOSs), the mandibular motion of the patient recorded by using an optical jaw tracking system, and the information provided by computerized occlusal analysis systems. This article describes the various digital technologies available for obtaining the digital occlusion of a patient and outlines its challenges and limitations.


OVERVIEW
The factors that influence the accuracy of the maxillomandibular relationship of diagnostic casts obtained by using IOSs are reviewed, as well as the occurrence of occlusal collisions or mesh interpenetrations. Different jaw tracking systems with varying digital technologies including ultrasonic systems, photometric devices, and artificial intelligence algorithms are reviewed. Computerized occlusal analysis systems for detecting occlusal contacts in a time sequential manner with the pressure distribution on the occlusal surfaces are reviewed.


CONCLUSIONS
Digital technologies provide powerful diagnostic and design tools for prosthodontic care. However, the accuracy of these digital technologies for acquiring and analyzing the static and dynamic occlusion need to be further analyzed.


CLINICAL SIGNIFICANCE
Efficiently implementing digital technologies into dental practice requires an understanding of the limitations and state of current development of the digital acquisition methods for digitizing the static and dynamic occlusion of a patient by using IOSs, digital jaw trackers, and computerized occlusal analysis devices.

An overview of the digital occlusion technologies: Intraoral scanners, jaw tracking systems, and computerized occlusal analysis devices.

This network meta-analysis (NMA) aimed to compare the complete-arch scanning accuracy of different intraoral scanners (IOSs) to that of reference standard tessellation language (STL) files.Studies comparing the trueness and precision of IOS STL files with those of reference STL scans for different arch types (dentate, edentulous, completely edentulous with implants, and partially edentulous with implants) were included in this study.An electronic search of five databases restricted to the English Language was conducted in October 2021.A total of 3,815 studies were identified, of which 114 were eligible for inclusion. After study selection and data extraction, pair-wise comparison and NMA were performed to define the accuracy of scanning for four arch subgroups using four outcomes (trueness and precision expressed as mean absolute deviation and root mean square values). Cochrane guidelines and the QUADAS-2 tool were used to assess the risk of bias. GRADE was used for certainty assessment.Fifty-three articles were included in this NMA. Altogether, 26 IOSs were compared directly and indirectly in 10 network systems. The accuracy of IOSs scans were not significantly different from the reference scans for dentate arches (three IOSs), edentulous arches (three IOSs), and completely edentulous arches with implants (one IOS). The accuracy of the IOSs was significantly different from the reference scans for partially edentulous arches with implants. Significant accuracy differences were found between the IOSs, regardless of clinical scenarios.The accuracy of complete-arch scanning by IOSs differs based on clinical scenarios.Different IOSs should be used according to the complete arch type. 

Evaluation of the accuracy of intraoral scanners for complete-arch scanning: A systematic review and network meta-analysis.

The accuracy of intraoral scanners (IOSs) can be affected by operator handling; however, the scanning area and accuracy discrepancies acquired at different scanning distances and angulations among IOSs remain uncertain.The objective of this in vitro study was to compare the scanning area and scanning accuracy of the intraoral digital scans obtained at 3 scanning distances with 4 different scanning angulations among 4 different IOSs.A reference device (reference file) was designed with 4 inclinations (0, 15, 30, and 45 degrees) and printed. Four groups were created based on the IOS: i700, TRIOS4, CS 3800, and iTero scanners. Four subgroups were generated depending on the scanning angulation (0, 15, 30, and 45 degrees). Each subgroup was divided into 3 subgroups based on the scanning distance: 0, 2, and 4 mm (N=720, n=15). The reference devices were positioned in a z-axis calibrated platform for standardizing the scanning distance. In the i700-0-0 subgroup, the 0-degree reference device was positioned in the calibrated platform. The wand of the IOS was positioned in a supporting framework with a 0-mm scanning distance, and the scans were acquired. In the i700-0-2 subgroup, the platform was lowered for a 2-mm scanning distance followed by the specimen acquisition. In the i700-0-4 subgroup, the platform was further lowered for a 4-mm scanning distance, and the scans were obtained. For the i700-15, i700-30, and i700-45 subgroups, the same procedures were carried out as in the i700-0 subgroups respectively, but with the 10-, 15-, 30-, or 45-degree reference device. Similarly, the same procedures were completed for all the groups with the corresponding IOS. The area of each scan was measured. The reference file was used to measure the discrepancy with the experimental scans by using the root mean square (RMS) error. Three-way ANOVA and post hoc Tukey pairwise comparison tests were used to analyze the scanning area data. Kruskal-Wallis and multiple pairwise comparison tests were used to analyze the RMS data (α=.05).IOS (P<.001), scanning distance (P<.001), and scanning angle (P<.001) were significant factors of the scanning area measured among the subgroups tested. A significant group×subgroup interaction was found (P<.001). The iTero and the TRIOS4 groups obtained higher scanning area mean values than the i700 and CS 3800 groups. The CS 3800 obtained the lowest scanning area among the IOS groups tested. The 0-mm subgroups obtained a significantly lower scanning area than the 2- and 4-mm subgroups (P<.001). The 0- and 30-degree subgroups obtained a significantly lower scanning area than the 15- and 45-degree subgroups (P<.001). The Kruskal-Wallis test revealed significant median RMS discrepancies (P<.001). All the IOS groups were significantly different from each other (P<.001), except for the CS 3800 and TRIOS4 groups (P>.999). All the scanning distance groups were different from each other (P<.001).Scanning area and scanning accuracy were influenced by the IOS, scanning distance, and scanning angle selected to acquire the digital scans. 

Scanning accuracy and scanning area discrepancies of intraoral digital scans acquired at varying scanning distances and angulations among 4 different intraoral scanners.

Abstract Background In mucogingival and implant surgery, an autologous soft tissue graft from the palate is the gold standard for reconstructing missing keratinised soft tissue and volume. Previously, presurgical measurements of the graft harvesting site were described with two-dimensional (2D) linear measurements. The present observational clinical study aimed to evaluate a three-dimensional (3D) measurement method for determining the present palatal soft tissue volume for each patient individually. Methods Pre-existing cone beam computed tomography (CBCT) scans of 20 patients were converted into 3D Standard Tessellation Language models of the bone surface. Intraoral impressions of the maxilla were taken and digitised to visualise the gingival surface. The resulting virtual models of bone (reference value) and gingival (actual value) surfaces were merged, with tooth surfaces used for registration. The region between the central incisors and the hard palate was subdivided into 5 regions of interest (ROIs). The distance between palatal bone and gingival surface was analysed both volumetrically and linearly, and the results were statistically evaluated for the ROIs. Results The average gingival surface area on the palate was 19.1 cm 2 , and the mean volume was 58.2 cm 3 (± 16.89). Among the ROIs, the mean linear value was highest in the most distal region, from the second molar to the hard palate (4.0 ± 1.09 mm) and lowest in the canine region (1.9 ± 0.63 mm). For mean distance, significant differences were found for the anterior palate and the most posterior palate in comparison with all other ROIs ( p &lt; 0.01). The volume measurements also declined significantly and steadily between the posterior (1.9 ± 1.0 cm 3 ) and anterior palates (0.4 ± 0.2 cm 3 ). Conclusions By merging digital data, palatal soft tissue could be quantified virtually. The results were reliable and comparable to previous findings with linear measurement methods. This 3D soft tissue volume analysis method fully exploited the diagnostic potential of data that are frequently collected for presurgical planning in oral surgery (i.e., CBCT + surface scans). This evaluation method might be useful for volumetric and linear measurements in other applications in anatomy and for determining palatal soft tissue dimensions in the planning stage before surgical interventions. Trial registration This observational clinical trial was retrospectively registered in the German Clinical Trials Register, reference number: DRKS00023918. 

/pdf/investigation-of-the-palatal-soft-tissue-volume-a-3d-virtual-26fpds5p.pdf

Investigation of the palatal soft tissue volume: a 3D virtual analysis for digital workflows and presurgical planning

Digital versus conventional full-arch impressions in linear and 3D accuracy: a systematic review and meta-analysis of in vivo studies

ABSTRACT Objective To evaluate the responsiveness of three cone-beam computed tomography (CBCT) transverse analyses (University of the Pennsylvania [UPenn] analysis, Boston University analysis and Yonsei University [YU] analysis). Materials and Methods A consecutive cohort sample of patients was retrospectively reviewed for eligibility. CBCT records before treatment (T0) and immediately after maxillary expansion (T1) of 71 patients receiving tooth-supported rapid maxillary expansion (RME) and 57 patients receiving mini-screw-assisted RME (MARME) were finally analyzed. Responsiveness was assessed by comparing changes of measures (T1-T0) to mid-palatal suture opening distance (MSOD) at T1. Correlational responsiveness was assessed by Pearson correlation coefficient (r). Absolute agreement responsiveness was assessed by Bland-Altman analysis. A specialized intraclass correlation coefficient (ICC) was selected to assess responsiveness combining correlation and absolute agreement. Results Changes of all three measures were moderately to strongly correlated to MSOD (r &amp;gt; 0.5). The highest correlation coefficient (0.79) was found between the YU analysis and MSOD. When exploring absolute agreement responsiveness, the smallest deviation (0.14 mm) was observed in the UPenn analysis. For ICC, the highest ICC value (0.63) was observed when the YU analysis was used. In addition, all three measurements were more responsive to MSOD in the MARME group than to those in RME group. Conclusions All three transverse measurements responded well to true changes of maxillary transverse deficiency during both tooth-supported and mini-screw-assisted RME. Deviations of responsive properties of these measurements from true skeletal changes were below a clinically meaningful level (1 mm). 

Responsiveness of three measurements in cone-beam computed tomography transverse analyses during both tooth-supported and mini-screw-assisted rapid maxillary expansion.

Although similar to trunk and limb skeletal muscles, masticatory muscles are believed as unique in both developmental origins and myogenesis. Gαi2 has been demonstrated to promote muscle hypertrophy and muscle satellite cell differentiation in limb muscles. However, the effect of Gαi2 on masticatory muscles is still unexplored. This study aimed to identify the role of Gαi2 in the proliferation and differentiation of masticatory muscle satellite cells, further exploring the metabolic mechanism of masticatory muscles. The proliferation rate, myotube size, fusion index of masticatory muscle satellite cells and Pax7, Myf5, MyoD, Tcf21 and Musculin expressions were significantly decreased by Gαi2 knockdown, while in cells infected with AdV4‐Gαi2, the proliferation rate, myotube size, fusion index and Tbx1 expression were significantly increased. Masticatory muscle satellite cells also displayed phenotype transformation as Gαi2 changed. In addition, Gαi2 altered myosin heavy chain (MyHC) isoforms of myotubes with less MyHC‐2A expression in siGαi2 group and more MyHC‐slow expression in AdV4‐Gαi2 group. In conclusion, Gαi2 could positively affect the adult myogenesis of masticatory muscle satellite cells and maintain the superiority of MyHC‐slow. Masticatory muscle satellite cells may have their unique Gαi2‐regulated myogenic transcriptional networks, although they may share some common characteristics with trunk and limb muscles.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/jcmm.17726

Gαi2 regulates the adult myogenesis of masticatory muscle satellite cells

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Digital versus conventional full-arch impressions in linear and 3D accuracy: a systematic review and meta-analysis of in vivo studies

Responsiveness of three measurements in cone-beam computed tomography transverse analyses during both tooth-supported and mini-screw-assisted rapid maxillary expansion.

Gαi2 regulates the adult myogenesis of masticatory muscle satellite cells