Accuracy of the 3-dimensional virtual patient representation obtained by using 4 different techniques: An in vitro study

Techniques and accuracy for aligning facial and intraoral digital scans to integrate a 3-dimensional virtual patient: A systematic review

STATEMENT OF PROBLEM

The optimal method of aligning intraoral scans with facial scans to generate a 3-dimensional (3D) virtual patient remains unclear. Distortions in the alignment of intraoral and facial scans would lead to an inadequate virtual patient representation and, therefore, to inadequate diagnosis and treatment planning.

PURPOSE

The purpose of this systematic review was to evaluate the available techniques for generating a 3D virtual patient by aligning facial and intraoral scans and to assess their accuracy.

MATERIAL AND METHODS

A systematic search was conducted in 3 databases: Medline, Scopus, and Web of Science. A manual search was also conducted. Specific descriptors were used to identify alignment techniques. Two independent reviewers screened titles and abstracts, with a third independent reviewer resolving ambiguities. A qualitative analysis was performed, and interexaminer agreement was assessed using the Cohen kappa statistic.

RESULTS

After screening, 48 of the 2832 identified articles were included for qualitative analysis. They focused on 3 alignment techniques: guided by retracted facial scans, extraoral scan bodies, and perioral intraoral scans. Interexaminer agreement was high (kappa=0.82 to 0.88). Integration techniques guided by extraoral scan bodies, influenced by extraoral scan body design and protocols, showed the best accuracy. The outcome variables for the evaluation of the effectiveness of these protocols were heterogeneous.

CONCLUSIONS

Integrating facial and intraoral scans was found to enhance diagnosis and treatment planning by providing essential esthetic and functional parameters. Integration techniques guided by extraoral scan bodies and combination techniques showed higher accuracy, especially for complex implant-supported prostheses or edentulous patients.

Trueness and precision of a handheld, a desktop and a mobile 3D face scanning system: An in vitro study

OBJECTIVE

This in vitro study investigated the trueness and precision of three different face scanning systems: a handheld, a desktop and a mobile 3D face scanning system.

MATERIAL AND METHODS

Fourteen landmarks were placed on a mannequin head, and sixteen inter-landmark distances were measured using a digital vernier caliper, repeated 20 times over 80 days. Three 3D face scanning systems were evaluated: a handheld (Metismile; Shining 3D Tech Co., Hangzhou, China), a desktop (RAYFace v2.0; Ray Co., Ltd., Gyeonggi-do, Korea), and a mobile application (Heges, Simon Marinek) on a smart device (iPad Pro X, Apple Inc., Cupertino, CA). Sixty facial scans were analyzed using metrology software (Geomagic Control X), and inter-landmark distances were compared to anthropometric measurements. Trueness was determined by absolute linear deviation and analyzed using one-way ANOVA, with Bonferroni and Tamhane tests for significant variance. Precision was compared to anthropometric measurements and analyzed using Kruskall-Wallis test.

RESULTS

3D analysis showed that the handheld scanner had the highest trueness (0.18 ± 0.15 mm) and precision (0.22 ± 0.04 mm). The desktop scanner had a trueness of 0.35 ± 0.26 mm and precision of 0.61 ± 0.18 mm, while the mobile scan application had a trueness of 0.54 ± 0.34 mm and precision of 0.47 ± 0.12 mm. All systems showed the highest trueness for vertical measurements compared to horizontal measurements. In the lower face, the precision was higher than anthropometric measurements for all 3D face scanning systems.

CONCLUSIONS

The handheld scanner demonstrated the highest trueness and its precision surpassed anthropometric measurements. The desktop scanner outperformed the mobile scan application in trueness but not in precision.

CLINICAL SIGNIFICANCE

The handheld, the desktop and the mobile face scanning system showed clinically acceptable trueness (< 0.6 mm) and could be used for virtual facebow transfer. All 3D face scanning systems in the present study demonstrated superior precision in the lower face compared to anthropometric measurements.

Substituting the Conventional Facebow With an Extraoral Scan Body System for Transferring the Maxillary Cast Into the Virtual Articulator: Manufacturing Procedures and Digital Clinical Protocol

CLINICAL CONSIDERATIONS

Conventional facebow records are used to transfer the maxillary cast into the analog articulator. Different reference planes have been described, including the true horizontal or gravity reference plane. A conventional facebow (Kois Dentofacial Analyzer; Panadent) allows the recording of the gravity plane for transferring the maxillary cast into the analog semi-adjustable articulator. Clinical procedures should also allow the maxillary scan transfer into the virtual articulator by using the true horizontal reference plane.

OBJECTIVE

The present manuscript describes the step-by-step protocol to record the gravity plane and to transfer the maxillary scan into the virtual articulator by using an extraoral scan body system.

CONCLUSIONS

The extroral scan body device enables the substitution of the conventional facebow procedure. Additionally, this extraoral scan body is fabricated by using additive manufacturing technologies and eases the complete digital workflow for transferring the maxillary scan into the virtual articulator.

Accuracy of registration between digitized extraoral scan bodies and virtual casts: Effect of the edentulous area, tooth anatomy, and registration method

STATEMENT OF PROBLEM

Digitized analog records have been used for the superimposition of intraoral and facial scans. However, the discrepancy in the registration between the digitized occlusal records contained on extraoral scan bodies and the maxillary virtual cast remains uncertain.

PURPOSE

The purpose of this in vitro study was to evaluate the effect of the registration method, edentulous area, and tooth anatomy on the accuracy of the alignment between the digitized extraoral scan body (ESB) and the maxillary virtual cast.

MATERIAL AND METHODS

A scannable ESB and a set of 8 maxillary casts (2 completely dentate simulating unworn and worn tooth anatomy, 5 partially edentulous, and 1 completely edentulous were printed (Pro 95S; SprintRay). Four zirconia markers were attached to the index of the ESB and each of the evaluated casts. Each cast was positioned into the tray of the ESB using occlusal registration material (O-Bite; DMG). The ESB and each corresponding evaluated cast were digitized by using a calibrated laboratory scanner (T710; Medit). Then, each cast and index of the ESB were scanned separately by using the same scanner (n=10). Using a CAD software program, each virtual cast was attached to the ESB 10 times by using 3 different alignment methods: an analog using an iterative closest points (ICP) algorithm and 2 semimanual alignments using a best-fit algorithm for the entire data set with or without including the edentulous areas. A metrology software program was used to measure the linear distance between the corresponding gauge balls and the angulation between the planes defined by the markers on the cast and the ESB. The measurements from the scans of the casts attached to the tray were used as a reference to calculate the discrepancies in each experimental group. Α P value threshold of <.05 was used to determine statistical significance.

RESULTS

The best-fit algorithm registration method produced better trueness and precision than the manual point-to-point registration (P<.05). When the edentulous areas were not included in the analog surface record, the trueness and precision of the best-fit algorithm were significantly worse (P<.05). In respect of tooth anatomy, no significant difference in trueness and precision was found among the investigated groups (P>.05). The completely dentate groups presented significantly better trueness than the edentulous groups (P<.05).

CONCLUSIONS

The accuracy of the registration between digitized occlusal surface scans and digital casts was influenced by the registration method, as well as by the location and extent of the edentulous areas.

An overview of the different digital facebow methods for transferring the maxillary cast into the virtual articulator

OBJECTIVES

The purposes of this study were to classify the described digital facebow techniques for transferring the maxillary cast into the semi-adjustable virtual articulator based on the digital data acquisition technology used and to review the reported accuracy values of the different digital facebow methods described.

OVERVIEW

Digital data acquisition technologies, including digital photographs, facial scanners, cone beam computed tomography (CBCT) imaging, and jaw tracking systems, can be used to transfer the maxillary cast into the virtual articulator. The reported techniques are reviewed, as well as the reported accuracy values of the different digital facebow methods.

CONCLUSIONS

Digital photographs can be used to transfer the maxillary cast into the virtual articulator using the true horizontal reference plane, but limited studies have assessed the accuracy of this method. Facial scanning and CBCT techniques can be used to transfer the maxillary cast into the virtual articulator, in which the most frequently selected references planes are the Frankfort horizontal, axis orbital, and true horizontal planes. Studies analyzing the accuracy of the maxillary cast transfer by using facial scanning and CBCT techniques are restricted. Lastly, optical jaw trackers can be selected for transferring the maxillary cast into the virtual articulator by using the axis orbital or true horizontal planes, yet the accuracy of these systems is unknown.

CLINICAL IMPLICATIONS

Digital data acquisition technologies, including digital photographs, facial scanning methods, CBCTs, and optical jaw tracking systems, can be used to transfer the maxillary cast into the virtual articulator. Studies are needed to assess the accuracy of these digital data acquisition technologies for transferring the maxillary cast into the virtual articulator.

An overview of artificial intelligence based applications for assisting digital data acquisition and implant planning procedures

OBJECTIVES

To provide an overview of the current artificial intelligence (AI) based applications for assisting digital data acquisition and implant planning procedures.

OVERVIEW

A review of the main AI-based applications integrated into digital data acquisitions technologies (facial scanners (FS), intraoral scanners (IOSs), cone beam computed tomography (CBCT) devices, and jaw trackers) and computer-aided static implant planning programs are provided.

CONCLUSIONS

The main AI-based application integrated in some FS's programs involves the automatic alignment of facial and intraoral scans for virtual patient integration. The AI-based applications integrated into IOSs programs include scan cleaning, assist scanning, and automatic alignment between the implant scan body with its corresponding CAD object while scanning. The more frequently AI-based applications integrated into the programs of CBCT units involve positioning assistant, noise and artifacts reduction, structures identification and segmentation, airway analysis, and alignment of facial, intraoral, and CBCT scans. Some computer-aided static implant planning programs include patient's digital files, identification, labeling, and segmentation of anatomical structures, mandibular nerve tracing, automatic implant placement, and surgical implant guide design.

Accuracy of a facebow record: A comparison between a conventional facebow and a smartphone 3D scanner

STATEMENT OF PROBLEM

Accurately transferring the maxillary cast to the articulator is an essential step in most prosthodontics procedures in both digital and conventional workflows. Recently, the use of a smartphone 3-dimensional (3D) scanner-based virtual facebow record has been reported, but its accuracy is unclear.

PURPOSE

The purpose of this clinical study was to compare the trueness and precision of a virtual facebow record made with a smartphone 3D scanner with that of a conventional facebow technique.

MATERIAL AND METHODS

One hundred facebow records were obtained from 10 participants using a virtual facebow record made with a smartphone 3D scanner (VFR_SP) and with a conventional facebow record (CFR) (n=10). In the VFR_SP group, a printed facebow fork was used to obtain virtual facebow records by using a smartphone-based face scanner. For the CFR group, an analog facebow record was obtained and scanned by using an industrial scanner. A cone beam computed tomography (CBCT) scan with an aligned maxillary arch scan was used as the control for each participant. Three reference points were placed on the maxillary arch scan on the right first molar, left central incisor, and left first molar. Scans from each group were superimposed on the CBCT scan to determine trueness. Scans within each group were also superimposed on each other to determine precision. Linear deviation at the 3 reference points and the angular deviation of occlusal planes were measured using a Python script. The trueness and precision of the 2 groups were compared by using a linear mixed model to account for repeated measures (α=.05).

RESULTS

No significant linear trueness differences between the 2 groups were found (P>.05). However, the VFR_SP group showed significantly less angular deviation: 1.53 degrees for the virtual facebow and 2.03 degrees for the conventional facebow group (P=.046). Regarding precision, the VFR_SP group showed significantly less linear deviation: 1.59 mm for the virtual facebow group and 2.33 mm for the conventional facebow group (P<.001), as well as angular deviation: 1.03 degrees for the virtual facebow group and 2.17 degrees for the conventional facebow (P<.001).

CONCLUSIONS

The VFR_SP group showed better accuracy compared with the CFR group. Further research with a larger sample size is needed.

Creating three-dimensional virtual patients by superimposing intraoral and facial digital scans guided with an aligner system: A dental technique

A technique for creating 3-dimensional virtual patients (3DVPs) by superimposing intraoral and facial digital scans guided with a novel aligner system is described. This aligner system supports design modifications to adapt to different facial scanning methods (FSMs) and reduce the impact of FSMs on the accuracy of 3DVPs. Two different designs of the aligner system are described: one for use with less-accurate FSMs and another for use with more-accurate FSMs. These virtual designs are available for download and use.

Accuracy of the maxillary cast transfer into the virtual semi-adjustable articulator by using analog and digital facebow record methods

STATEMENT OF PROBLEM

Different digital methods have been described for transferring the maxillary cast into a virtual articulator; however, its accuracy remains uncertain.

PURPOSE

The purpose of this in vitro study was to compare the accuracy of the maxillary cast transfer into the virtual semi-adjustable articulator by using analog and digital methods.

MATERIAL AND METHODS

A maxillary typodont with 5 markers was positioned into a mannequin, which was digitized by using an industrial scanner (ATOS Q) and an extraoral scan of the typodont obtained (T710). Three groups were created based on the technique used to transfer the maxillary cast into the virtual articulator (Panadent PCH Articulator): conventional facebow record (CNV group), digital photograph (P group), and facial scanning (FS group) (n=10). In the CNV group, conventional facebow records (Kois Dentofacial analyzer system) were digitized (T710) and used to mount the maxillary scan into the articulator by aligning it with the reference platform (Kois adjustable platform) (DentalCAD). In the P group, photographs with the reference glasses (Kois Reference Glasses 3.0) were positioned in the mannequin. Each photograph was superimposed with the maxillary scan. Then, the maxillary scan was transferred into the virtual articulator by using the true horizontal plane information of the photograph. In the FS group, facial scans with an extraoral scan body (Kois Scan Body) were positioned in the mannequin by using a facial scanner (Instarisa). The extraoral scan body was digitized by using the same extraoral scanner. The digitized extraoral scan body provided the true horizontal plane information that was used to mount the maxillary scan into the articulator, along with the Kois disposable tray of the scan body. On the reference scan and each specimen, 15 linear measurements between the markers of the maxillary scans and the horizontal plane of the virtual articulator and 3 linear measurements between the maxillary dental midline and articulator midline were calculated. The measurements of the reference scan were used as a control to assess trueness and precision. Trueness was analyzed by using 1-way ANOVA followed by the pairwise comparison Tukey tests (α=.05). Precision was evaluated by using the Levene and pairwise comparisons Wilcoxon Rank sum tests.

RESULTS

No significant trueness (P=.996) or precision (P=.430) midline discrepancies were found. Significant posterior right (P<.001), anterior (P=.005), posterior left (P<.001), and overall (P<.001) trueness discrepancies were revealed among the groups. The P group obtained the best posterior right, posterior left, and overall trueness and precision. The P and FS groups demonstrated the best anterior trueness, but no anterior precision discrepancies were found.

CONCLUSIONS

The techniques tested affected the accuracy of the maxillary cast transfer into the virtual semi-adjustable articulator. In the majority of the parameters assessed, the photography method tested showed the best trueness and precision values. However, the maxillary cast transfer accuracy ranged from 137 ±44 µm to 453 ±176 µm among the techniques tested.

Accuracy comparison of the maxillary cast transfer into the virtual semi-adjustable articulator between an analog facebow record and a digital photography technique

STATEMENT OF PROBLEM

Digital photographs can be used for transferring the maxillary cast into the virtual semi-adjustable articulator; however, its accuracy remains unknown.

PURPOSE

The purpose of the present study was to compare the accuracy of the maxillary cast transfer into the virtual semi-adjustable articulator by using an analog and a digital standardized photography technique.

MATERIAL AND METHODS

A maxillary cast was digitized (T710) and positioned into a dental mannequin. The dental midline was not coincident with the facial midline and the maxillary occlusal plane was tilted. A reference scan of the assembled mannequin was obtained by using a facial scanner (Instarisa). Two groups were created based on the technique used to transfer the maxillary cast into the articulator (Panadent PCH): conventional facebow record (CNV group) or digital photograph (Photo group) (n=10). In the CNV group, facebow records (Kois Dentofacial analyzer system) were digitized (T710) and used to transfer the maxillary scan into the articulator by aligning it with the reference platform (Kois adjustable platform). In the Photo group, photographs with a reference glasses (Kois Reference Glasses) positioned into the mannequin were acquired. Each photograph was aligned with the maxillary scan. Then, the maxillary scan was transferred into the articulator by using the true horizontal axis information contained in the photograph. On the reference scan and each specimen, 10 linear measurements between the buccal cusps of the maxillary scan and the horizontal plane of the virtual articulator and a linear measurement between the maxillary dental midline and articulator midline were calculated. The measurements of the reference scan were used as a control to compute trueness and precision. Trueness was analyzed by using 1-way ANOVA followed by the pairwise comparison Tukey test (α=.05). Precision was evaluated by using the Levene and Wilcoxon Rank sum tests (α=.05).

RESULTS

The overall discrepancy measured in the CNV group was 0.620 ±0.396 mm, while in the Photo group it was 1.282 ±0.118 mm. Significant trueness differences were found in the midline (P=.037), anterior (P=.050), posterior right (P<.001), posterior left (P=.012), and overall discrepancy (P<.001) between the CNV and Photo groups. Significant precision discrepancies were found in the midline (P=.012), posterior right (P<.001), anterior (P<.001), posterior left (P=.002), and overall discrepancy (P<.001) between the CNV and Photo groups.

CONCLUSIONS

The facebow record method impacted the accuracy of the maxillary cast transfer. The Photo group obtained better trueness in the midline transfer than the CNV group; however, the CNV group demonstrated better trueness in the anterior, posterior right, posterior left, and overall discrepancy of the maxillary cast transfer compared with the Photo group. Overall, the Photo group obtained better precision than the CNV group.

Maxillomandibular relationship and virtual facebow integration in complete-arch intraoral implant scan: A novel clinical technique

This clinical report introduces a novel clinical technique to create a 3D virtual patient for transferring the edentulous maxillary arch position with maxillomandibular relationship by using a facial scan device and an intraoral scanner and omitting CBCT imaging.

Facially driven guided crown lengthening using a complete digital workflow: A dental technique

A facially driven digital guided crown lengthening method using the virtual smile design approach supplemented with a static 3-dimensional face scan that demonstrates the digital data of extraoral soft tissue is presented. The technique enables the practitioner to virtually design the new smile and surgically plan the crown lengthening procedure.

Recommendations for successful virtual patient-assisted esthetic implant rehabilitation: A guide for optimal function and clinical efficiency

OBJECTIVE

Complete arch implant rehabilitation necessitates meticulous treatment planning and high-level collaboration between surgical and prosthetic dental teams. Emerging virtual technologies hold considerable promise in streamlining this process. The aim of this article is to extend recommendations to clinicians venturing into the virtual patient-assisted esthetic implant rehabilitation workflow.

OVERVIEW

This article summarizes recommendations for virtual patient-assisted esthetic implant rehabilitation in the following five aspects: three-dimensional data handling and superimposition, occlusion and virtual articulator integration in creating virtual patients, streamlined face- and prosthetic-driven surgical planning, reuse of presurgical data ("Copy & Paste"), and final impression for passive fitting of final restoration. To illustrate these principles, a case with complete-mouth implant rehabilitation completed within six visits using this virtual patient workflow is presented.

CONCLUSION

The virtual patient workflow serves as an invaluable tool to perform treatment planning, enhance efficiency, and ensure predictable outcomes in esthetic complete arch implant rehabilitation.

CLINICAL SIGNIFICANCE

Virtual workflows are increasingly prevalent in esthetic implant rehabilitation. Nevertheless, these workflows necessitate a distinct set of knowledge and tools divergent from conventional dentistry practices. This article offers guidelines and recommendations for dental clinicians who are new to this field.

Comparison of intraoral and laboratory scanners to an industrial-grade scanner while analyzing the fabrication trueness of polymer and titanium complete-arch implant-supported frameworks

OBJECTIVES

To compare the scans of different intraoral scanners (IOSs) and laboratory scanners (LBSs) to those of an industrial-grade optical scanner by measuring deviations of complete-arch implant-supported frameworks from their virtual design file.

MATERIAL AND METHODS

Ten polyetheretherketone (PEEK) and 10 titanium (Ti) complete-arch implant-supported frameworks were milled from a master standard tessellation language (STL) file. An industrial-grade blue light scanner (AT), 2 LBSs (MT and E4), and 3 IOSs (PS, T3, and T4) were used to generate STL files of these frameworks. All STLs were imported into an analysis software (Geomagic Control X) and overall root mean square (RMS) values were calculated. Marginal surfaces of all STL files were then virtually isolated (Medit Link v 2.4.4) and marginal RMS values were calculated. Deviations in scans of tested scanners were compared with those in scans of AT by using a linear mixed effects model (α = 0.05).

RESULTS

When the scans of PEEK frameworks were considered, PS and T3 had similar overall RMS to those of AT (p ≥ .076). However, E4 and T4 had higher and MT had lower overall RMS than AT (p ≤ .002) with a maximum estimated mean difference of 13.41 µm. When the scans of Ti frameworks were considered, AT had significantly lower overall RMS than tested scanners (p ≤ .010) with a maximum estimated mean difference of 31.35 µm. Scans of tested scanners led to significantly higher marginal RMS than scans of AT (p ≤ .006) with a maximum estimated mean difference of 53.90 µm for PEEK and 40.50 µm for Ti frameworks.

CONCLUSION

Only the PEEK framework scans of PS and T3 led to similar overall deviations to those of AT. However, scans of all tested scanners resulted in higher marginal deviations than those of AT scans.

CLINICAL SIGNIFICANCE

Scans performed by using PS and T3 may be alternatives to those of tested reference industrial scanner AT, for the overall fabrication trueness analysis of complete-arch implant-supported PEEK frameworks.

Contemporary Role and Applications of Artificial Intelligence in Dentistry

Artificial Intelligence (AI) technologies play a significant role and significantly impact various sectors, including healthcare, engineering, sciences, and smart cities. AI has the potential to improve the quality of patient care and treatment outcomes while minimizing the risk of human error. Artificial Intelligence (AI) is transforming the dental industry, just like it is revolutionizing other sectors. It is used in dentistry to diagnose dental diseases and provide treatment recommendations. Dental professionals are increasingly relying on AI technology to assist in diagnosis, clinical decision-making, treatment planning, and prognosis prediction across ten dental specialties. One of the most significant advantages of AI in dentistry is its ability to analyze vast amounts of data quickly and accurately, providing dental professionals with valuable insights to enhance their decision-making processes. The purpose of this paper is to identify the advancement of artificial intelligence algorithms that have been frequently used in dentistry and assess how well they perform in terms of diagnosis, clinical decision-making, treatment, and prognosis prediction in ten dental specialties; dental public health, endodontics, oral and maxillofacial surgery, oral medicine and pathology, oral & maxillofacial radiology, orthodontics and dentofacial orthopedics, pediatric dentistry, periodontics, prosthodontics, and digital dentistry in general. We will also show the pros and cons of using AI in all dental specialties in different ways. Finally, we will present the limitations of using AI in dentistry, which made it incapable of replacing dental personnel, and dentists, who should consider AI a complimentary benefit and not a threat.

Prevention of peri-implant disease in edentulous patients with fixed implant rehabilitations

OBJECTIVES

To provide an overview about the current approaches to prevent peri-implant diseases in edentulous patients with complete-arch implant-supported prostheses, and to review the clinical applications of the latest digital technologies for implant prosthodontics.

METHODS

A review of the guidelines to prevent peri-implant diseases in patient's receiving complete-arch implant-supported prostheses including facially driven treatment planning procedures using either conventional or digital methods, computer-aided implant planning procedures, and prosthodontic design variables including the optimal number and distribution of dental implants, implant to abutment connection type, implant or abutment level design, screw- or cement-retained alternatives, prostheses contours, and material selection is provided. Furthermore, an outline of the current therapeutic management approaches to address peri-implant diseases is reviewed.

CONCLUSIONS

Clinicians should understand and know different planning and design-related variables that can affect biological and mechanical complication rates of complete-arch implant-supported prostheses. Maintenance protocols are fundamental for minimizing biological and mechanical complications.

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

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.