A guide for selecting the intraoral scan extension when fabricating tooth- and implant-supported fixed dental prostheses.

OBJECTIVES: To describe a new classification for intraoral scans based on the scan extension and to introduce a decision guideline to choose the scan extension for fabricating tooth- and implant-supported fixed dental prostheses (FDPs). OVERVIEW: Multiple operator- and patient-related factors have been identified that can decrease the scanning accuracy of intraoral scanners (IOSs), including scan extension. However, the decision criteria for selecting scan extension for fabricating tooth- and implant-supported restorations is unclear. Based on the extension of the intraoral digital scans, three types of scans can be defined: half-arch (anterior or posterior), extended half-arch, and complete-arch scan. Variables to consider when choosing the scan extension include the number and location of units being restored, as well as the extension and location of edentulous areas. Additionally, the accuracy of the virtual definitive cast and the accuracy of the maxillomandibular relationship captured by using IOSs should be differentiated. CONCLUSIONS: A decision tree for selecting the scan extension is presented. The decision is based on the number and location of units being restored, and the extension and location of edentulous areas. Intraoral scans with reduced scan extension are indicated when fabricating tooth- and implant-supported crowns or short-span fixed prostheses, when the patient does not have more than one missing tooth in the area of the dental arch included in the scan. For the remaining clinical conditions, complete-arch intraoral scans are recommended. CLINICAL SIGNIFICANCE: Scan extension is a clinician's decision that should be based on the number and location of units being restored and the extension and location of edentulous areas. Intraoral scans with a reduced scan extension is recommended, when possible.

A biologically oriented preparation technique (BOPT) for immediate posterior implant placement, immediate provisionalization, and definitive implant crown fabrication: A complete digital workflow.

OBJECTIVE: Immediate implants and immediate alveolar sealing have been a widely utilized treatment with high predictability and biological advantages. The improvement in technology has made it possible to simplify clinical processes. The aim of the present report was to describe the complete digital workflow of the Biologically oriented preparation technique for immediate posterior implant, immediate provisionalization and fabrication of definitive implant crowns. CLINICAL CONSIDERATIONS: The surgical process and prosthetic management to preserve the gingival contours of the extracted natural tooth during immediate implant placement and provisionalization are described. Additionally, during the same clinical intervention, the definitive intraoral digital implant scans for capturing the implant position, peri-implant tissue contours, adjacent and antagonist dentition, and profile emergence of the interim implant crown are captured for the fabrication of the definitive crown. CONCLUSIONS: Based on the technique described, the immediate implant placement and provisionalization in the posterior area provides biological and clinical advantages, reducing the number of abutment-implant disconnections and the number of clinical appointments, as well as increases patient comfort. CLINICAL SIGNIFICANCE: The present article describes a technique for an immediate implant placement and provisionalization in the posterior region for maintaining the gingival architecture of the extracted tooth. During the same appointment, the implant position, peri-implant tissue contours, and adjacent and antagonist dentition, and profile emergence of the interim implant crown are captured by using an intraoral scanner and used for the fabrication of the definitive crown. This technique aims to reduce the number of abutment-implant disconnections and clinical appointments.

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.

Influence of the ambient color lighting on the accuracy of complete arch implant scans recorded by using two intraoral scanners.

STATEMENT OF PROBLEM: The influence of different ambient factors including lighting has been previously studied. However, the influence of ambient color lighting settings on intraoral scanning accuracy remains uncertain. PURPOSE: The purpose of this in vitro study was to assess the influence of ambient color lighting on the accuracy of complete arch implant scans recorded by using 2 intraoral scanners (IOSs). MATERIAL AND METHODS: An edentulous maxillary cast with 6 implant scan bodies was digitized by using a laboratory scanner (DW-7-140) to obtain a reference file. Two groups were created based on the IOS tested: TRIOS 4 (IOS-1) and i700 (IOS-2). Seven subgroups were developed depending on the ambient color lighting (red, green, blue, yellow, cyan, magenta, and white) (n=15). Scanning accuracy was analyzed by using a metrology software program (Geomagic Control X). The Kruskal-Wallis, 1-way ANOVA, and pairwise comparisons were used to analyze the data (α=.05). RESULTS: Significant trueness and precision values were found across the groups (P<.05) and subgroups (P<.05). For IOS-1, blue ambient lighting obtained the best trueness (19.8 ±1.8 µm) (P<.05); in precision, white light (20.8 ±7.3 µm) and blue light (22.1 ±13.5) showed the best results (P<.05). For IOS-2, white light showed the best trueness (51.9 ±16.7 µm); the best precision was obtained under magenta (38.6 ±10.4 µm) and yellow light (52.6 ±24.0 µm) (P<.05). CONCLUSIONS: The optimal ambient color lighting varied between the IOSs assessed. As the best condition for maximizing accuracy was not found, ambient color lighting must be individualized for the IOS system used.

Accuracy of an artificial intelligence-based program for locating the maxillomandibular relationship of scans acquired by using intraoral scanners.

STATEMENT OF PROBLEM: An artificial-intelligence (AI) based program can be used to articulate scans in maximum intercuspal position (MIP) or correct occlusal collisions of articulated scans at MIP; however, the accuracy of the AI program determining the MIP relationship is unknown. PURPOSE: The purpose of the present clinical study was to assess the influence of intraoral scanner (IOS) (TRIOS 5 or i700) and program (IOS or AI-based program) on the accuracy of the MIP relationship. MATERIAL AND METHODS: Casts of a participant mounted on an articulator were digitized (T710). A maxillary and a mandibular scan of the participant were recorded by using 2 IOSs: TRIOS 5 and i700. The scans were duplicated 15 times. Then, each duplicated pair of scans was articulated in MIP using a bilateral occlusal record. Articulated scans were duplicated and allocated into 2 groups based on the automatic occlusal collisions' correction completed by using the corresponding IOS program: IOS-corrected and IOS-noncorrected group. Three subgroups were created based on the AI-based program (Bite Finder) method: AI-articulated, AI-IOS-corrected, and AI-IOS-noncorrected (n=15). In the AI-articulated subgroup, the nonarticulated scans were imported and articulated. In the AI-IOS-corrected subgroup, the articulated scans obtained in the IOS-corrected group were imported, and the occlusal collisions were corrected. In the AI-IOS-corrected subgroup, the articulated scans obtained in the IOS-noncorrected subgroup were imported, and the occlusal collisions were corrected. A total of 36 interlandmark measurements were calculated on each articulated scan (Geomagic Wrap). The distances computed on the reference scan were used as a reference to calculate the discrepancies with each experimental scan. Nonparametric 2-way ANOVA and pairwise multiple comparison Dwass-Steel-Critchlow-Fligner tests were used to analyze trueness. The general linear model procedure was used to analyze precision (α=.05). RESULTS: Significant maxillomandibular trueness (P=.003) and precision (P<.001) differences were found among the subgroups. The IOS-corrected and IOS-noncorrected (P<.001) and AI-articulated and IOS-noncorrected subgroups (P=.011) were significantly different from each other. The IOS-corrected and AI-articulated subgroups obtained significantly better maxillomandibular trueness and precision than the IOS-noncorrected subgroups. CONCLUSIONS: The IOSs tested obtained similar MIP accuracy; however, the program used to articulate or correct occlusal collusions impacted the accuracy of the MIP relationship.

Influence of scan extension and starting quadrant on the accuracy of four intraoral scanners for fabricating tooth-supported crowns.

STATEMENT OF PROBLEM: Multiple factors can influence the accuracy of intraoral scanners (IOSs). However, the impact of scan extension and starting quadrant on the accuracy of IOSs for fabricating tooth-supported crowns remains uncertain. PURPOSE: The purpose of the present in vitro study was to measure the influence of scan extension (half or complete arch scan) and the starting quadrant (same quadrant or contralateral quadrant of the location of the crown preparation) on the accuracy of four IOSs. MATERIAL AND METHODS: A typodont with a crown preparation on the left first molar was digitized (T710) to obtain a reference scan. Four scanner groups were created: TRIOS 5, PrimeScan, i700, and iTero. Then, 3 subgroups were defined based on the scan extension and starting quadrant: half arch (HA subgroup), complete arch scan starting on the left quadrant (CA-same subgroup), and complete arch scan starting on the right quadrant (CA-contralateral subgroup), (n=15). The reference scan was used as a control to measure the root mean square (RMS) error discrepancies with each experimental scan on the tooth preparation, margin of the tooth preparation, and adjacent tooth areas. Two-way ANOVA and pairwise multiple comparisons were used to analyze trueness (α=.05). The Levene and pairwise comparisons using the Wilcoxon Rank sum tests were used to analyze precision (α=.05). RESULTS: For the tooth preparation analysis, significant trueness and precision differences were found among the groups (P<.001) and subgroups (P<.001), with a significant interaction group×subgroup (P=.002). The iTero and TRIOS5 groups obtained better trueness than the PrimeScan and i700 groups (P<.001). Moreover, half arch scans obtained the best trueness, while the CA-contralateral scans obtained the worst trueness (P<.001). The iTero group showed the worst precision among the IOSs tested. For the margin of the tooth preparation evaluation, significant trueness and precision differences were found among the groups (P<.001) and subgroups (P<.001), with a significant interaction group×subgroup (P=.005). The iTero group obtained best trueness (P<.001), but the worst precision (P<.001) among the IOSs tested. Half arch scans obtained the best trueness and precision values. For the adjacent tooth analysis, trueness and precision differences were found among the groups (P<.001) and subgroups tested (P<.001), with a significant interaction group×subgroup (P=.005). The TRIOS 5 obtained the best trueness and precision. Half arch scans obtained the best accuracy. CONCLUSIONS: Scan extension and the starting quadrant impacted the scanning trueness and precision of the IOSs tested. Additionally, the IOSs showed varying scanning discrepancies depending on the scanning area assessed. Half arch scans presented the highest trueness and precision, and the complete arch scans in which the scan started in the contralateral quadrant of where the crown preparation was obtained the worst trueness and precision.

Influence of implant reference on the scanning accuracy of complete arch implant scans captured by using a photogrammetry system.

STATEMENT OF PROBLEM: Photogrammetry has been reported to be a reliable digital alternative for recording implant positions; however, the factors that may impact the accuracy of photogrammetry techniques remain unknown. PURPOSE: The purpose of this in vitro study was to assess the influence of the implant reference on the accuracy of complete arch implant scans acquired by using a photogrammetry system. MATERIAL AND METHODS: An edentulous cast with 6 implant abutment analogs (MultiUnit Abutment Plus Replica) was obtained and digitized by using a laboratory scanner (T710; Medit). A photogrammetry system (PIC System) was selected to obtain complete arch implant scans. An optical marker (PIC Transfer, HC MUA Metal; PIC Dental) was positioned on each implant abutment of the reference cast. Each optical marker code and position was determined in the photogrammetry software program. Three groups were created based on the implant reference selected before acquiring the photogrammetry scans: right first molar (IPR-3 group), left canine (IPR-11 group), and left first molar (IPR-14 group) (n=30). Euclidean linear and angular measurements were obtained on the digitized reference cast and used to compare the discrepancies with the same measurements obtained on each experimental scan. One-way ANOVA and the Tukey tests were used to analyze the trueness data. The Levene test was used to analyze the precision values (α=.05 for all tests). RESULTS: One-way ANOVA revealed significant linear (P=.003) and angular (P=.009) trueness differences among the groups tested. Additionally, the Tukey test showed that the IPR-11 and IPR-14 groups had significantly different linear (P<.001) and angular trueness (P<.001). The Levene test showed no significant precision linear (P=.197) and angular (P=.235) discrepancies among the groups tested. The IPR-3 group obtained the highest trueness (P<.001) and precision (P<.001) values among the groups tested. CONCLUSIONS: Implant reference impacted the accuracy of complete arch implant scans obtained by using the photogrammetry system tested. However, a trueness ±precision linear discrepancy of 6 ±3 µm and an angular discrepancy of 0.01 ±0.01 degrees were measured among the groups tested; therefore, the impact of the discrepancy measured should not be clinically significant.

Comparison of surface roughness of additively manufactured implant-supported interim crowns fabricated with different print orientations.

PURPOSE: To assess the influence of print orientation on the surface roughness of implant-supported interim crowns manufactured by using digital light processing (DLP) 3D printing procedures. MATERIALS AND METHODS: An implant-supported maxillary right premolar full-contour crown was obtained. The interim restoration design was used to fabricate 30 specimens with 3 print orientations (0, 45, and 90 degrees) using an interim resin material (GC Temp PRINT) and a DLP printer (Asiga MAX UV) (n = 10). The specimens were manufactured, and each was cemented to an implant abutment with autopolymerizing composite resin cement (Multilink Hybrid Abutment). Surface roughness was assessed on the buccal surface of the premolar specimen by using an optical measurement system (InfiniteFocusG5 plus). The data were analyzed with a Shapiro-Wilk test, resulting in a normal distribution. One-way ANOVA and the Tukey HSD tests were selected (α = 0.05). RESULTS: Statistically significant discrepancies were found in the surface roughness mean values among the groups tested (p < 0.001). The lowest mean ± standard deviation surface roughness was found with the 90-degree group (1.2 ± 0.36 µm), followed by the 0-degree orientation (2.23 ± 0.18 µm) and the 45-degree group (3.18 ± 0.31 µm). CONCLUSIONS: Print orientation parameter significantly impacted the surface roughness of the implant-supported interim crowns manufactured by using the additive procedures tested.

Impact of color temperature and illuminance of ambient light conditions on the accuracy of complete-arch digital implant scans.

OBJECTIVE: The purpose of the present study was to assess the influence of color temperature and illuminance of ambient light on the accuracy of different intraoral scanners (IOSs) in complete-arch implant scans. METHODS: An edentulous model with six implants and scan bodies was digitized by using a laboratory scanner (DW-7-140; Dental Wings) to obtain a reference mesh. Fifteen scans were performed employing two intraoral scanners (Trios 4;3Shape A/S and i700; Medit Co) at two illuminances (500 and 1000 lux) and three color temperatures (3200, 4400, and 5600 K). Scanning accuracy was measured by using a 3D metrology software program (Geomagic Control X). Kruskal-Wallis, one-way ANOVA, and pairwise comparison tests were used to analyze the data (α = .05). RESULTS: Significant differences in trueness and precision values were found among the different IOSs under the same ambient lighting condition and among the different lighting conditions for a given IOS (p < .05) except for trueness in i700 groups (p > .05). CONCLUSIONS: The influence on the accuracy of color temperature and illuminance varied depending on the intraoral scanner. An optimal ambient scanning light condition was not found; this should be adjusted based on the specific IOS system used. 3200 K of ambient light influences the precision of i700 when performed at 1000 lux, decreasing the accuracy. The variation of color temperature at the same illuminance does not affect the scanning accuracy of TRIOS 4, which obtained better accuracy in all scans at 1000 lux.

Influence of arch location and scanning pattern on the scanning accuracy, scanning time, and number of photograms of complete-arch intraoral digital implant scans.

OBJECTIVES: To measure the influence of arch location and scanning pattern on the accuracy, scanning time, and number of photograms of complete-arch implant scans acquired using an intraoral scanner (IOS). MATERIALS AND METHODS: A maxillary (maxillary group) and mandibular (mandibular group) model with 6 implant abutments on each cast was digitized using a desktop scanner (control scans). Six subgroups were created based on the scanning pattern used to acquire the scans using an IOS (Trios 4): occluso-buccal-lingual (OBL subgroup), occluso-linguo-buccal (OLB subgroup), bucco-linguo-occlusal (BLO subgroup), linguo-buccal-occlusal (LBO subgroup), zigzag (ZZ subgroup), and circumferential (C subgroup). The control scans were used as a reference to measure the discrepancy with the experimental scans calculating the root mean square error. Two-way ANOVA and the pairwise comparison Tukey tests were used to analyze the data (α = .05). RESULTS: Significant discrepancies in trueness (p < .001), precision (p < .001), scanning time (p < .001), and number of photograms (p < .001) were found. The maxillary group obtained poorer trueness and precision values, higher scanning times, and a larger number of photograms than the mandibular group. The C subgroup obtained the best trueness and precision values, but was not significantly different from the OLB, BLO, and LBO subgroups. The ZZ subgroup obtained the worst trueness and precision values (p < .05). The C subgroup obtained the lowest scanning time and number of photograms (p < .05). CONCLUSIONS: Arch location and scanning pattern influenced scanning accuracy, scanning time, and number of photograms of complete-arch implant scans.

Study of attached gingiva space color according to gender and age in Caucasian population.

OBJECTIVE: To describe the color of the healthy attached gingiva adjacent to the maxillary incisors and to evaluate the effect of age and gender on CIELAB color coordinates. MATERIALS AND METHODS: The study included 216 Caucasian individuals (129 females and 87 males) divided into three age groups. A SpectroShade Micro spectrophotometer was used to register color coordinates at 2.5 mm apical of the zenith of the upper central incisors. Descriptive and inferential statistical analysis was performed. RESULTS: The minimum and maximum coordinates in which the CIELAB natural gingival space is delimited are: L* minimum 40.4 - L* maximum 61.2; a* minimum 17.0 - a* maximum 30.2; b* minimum 9.8 - and b* maximum 21.9. There are statistically significant differences between males and females for coordinates L*, a* and b* in the attached gingival area selected. Age had a significant effect on coordinate b* (p = 0.000). CONCLUSIONS: Statistically significant differences were found between the L*, a* and b* color coordinates of the attached gingiva between men and women, although the color difference is below the clinical acceptance threshold. The attached gingiva takes on a bluish color as the patients get older, which means that the b* coordinate decreases. CLINICAL SIGNIFICANCE: With a prosthodontic approach, knowledge of the CIELAB natural attached gingival coordinates based on the patient's age and gender will facilitate the clinician's work in selecting the appropriate color. The CIELAB system values found can be used as a gingival shade guide reference.

Techniques to improve the accuracy of complete arch implant intraoral digital scans: A systematic review.

STATEMENT OF PROBLEM: The best method of optimizing the accuracy of complete arch intraoral digital scans is still unclear. For instance, the location of the scan bodies can be significantly distorted with respect to their actual positions, which would lead to a nonpassive fit of the definitive prosthesis. PURPOSE: The purpose of this systematic review was to analyze available techniques for improving the accuracy of digital scans in implant-supported complete arch fixed prostheses. MATERIAL AND METHODS: Three databases (Medline, Embase, and Google Scholar) were searched, and the results obtained were supplemented by a hand search. Specific descriptors identified techniques whose objective were to increase the accuracy of digital scans in implant-supported complete arch fixed prostheses. Titles and abstracts were screened by 2 independent reviewers, and unclear results were discussed with a third independent reviewer. A qualitative analysis based on procedural parameters was used. The interexaminer agreements of both were assessed by the Cohen kappa statistic, and the Risk of Bias Tool was used to assess the risk of bias across the studies. RESULTS: A total of 17 techniques matching the inclusion criteria were evaluated. Higher accuracy but also differences regarding the need for supplementary devices, number of intraoral scans, and time consumption of clinical and software program steps were observed compared with the conventional digital scanning protocol. The use of a splinting device was common to most of the studies. The outcome variables for the evaluation of the effectiveness of these protocols were heterogeneous. CONCLUSIONS: The use of additional techniques during intraoral scanning can improve accuracy in implant-supported complete arch fixed prostheses. However, higher complexity for those procedures should be expected.

Techniques for locating the screw access hole in cement-retained implant-supported prostheses: A systematic review.

STATEMENT OF PROBLEM: Different techniques for retrieving cement-retained implant-supported prostheses have been described to minimize damage to the prostheses. Nevertheless, a classification of the described techniques remains ambiguous. PURPOSE: The purpose of this systematic review was to review and classify the described techniques for recording and locating the screw access hole in cement-retained implant-supported prostheses. MATERIAL AND METHODS: A bibliographic search was completed on MEDLINE/PubMed, Web of Science, Scopus, and Cochrane databases. A manual search was also conducted. The articles that described or evaluated techniques for recording and locating the screw access hole of cement-retained implant-supported prostheses were included. Two investigators independently assessed the quality assessment of the studies using the Revised Cochrane risk of bias tool for randomized trials. A third examiner was consulted to resolve the lack of consensus. RESULTS: A total of 30 articles were included. The different methods were classified according to whether the screw access hole location was registered before or after cementation. The precementation techniques were classified into 4 subgroups: identification marks, photographic records, digital files, and precementation screw access hole location guides. The postcementation techniques were subdivided into 2 subgroups: radiographic records and postcementation screw access hole location guides. CONCLUSIONS: Different techniques have been proposed to facilitate the location of the screw access hole in cement-retained implant-supported restorations. Although the evidence is scarce, studies seem to ascertain that some techniques, such as the use of drilling guides, orientation with cone beam computed tomography images, or holes made in the metal framework, can increase the retrievability of cement-retained implant-supported prostheses and decrease complications in the location of the screw access hole. The proposed classification summarizes precementation and postcementation techniques and provides a tool to decide the most suitable for each specific clinical situation.

Clinical evaluation of the effects of cutting off, overlapping, and rescanning procedures on intraoral scanning accuracy.

STATEMENT OF PROBLEM: Cutting off and rescanning procedures have been shown to affect the accuracy of intraoral scanning; however, the clinical impact of tooth cutting off and rescanning of mesh holes on accuracy remains unclear. PURPOSE: The purpose of this clinical study was to evaluate the influence of the tooth location of the rescanned mesh holes (with or without modifying the preexisting intraoral digital mesh with the rescanning procedures) on intraoral scanning accuracy. MATERIAL AND METHODS: A maxillary right quadrant digital scan was acquired (control scan) on a dentate participant by using an intraoral scanner (TRIOS 4). The control scan was duplicated 240 times and distributed among 4 groups depending on the location of the rescanned mesh hole: first molar (M group), second premolar (PM group), canine (C group), and central incisor (I group). Each group was divided into 2 subgroups: one subgroup contained overlapping rescanning modifications (WO subgroup), and the other blocked the preexisting digital scan to avoid further modifications when rescanning (NO subgroup) (n=30). A software program (Geomagic) was used to assess the discrepancy between the control and the experimental meshes by using the root mean square (RMS) error calculation. The Shapiro-Wilk test showed that data were not normally distributed. The Kruskal-Wallis test and post hoc Dunn test with Bonferroni correction were used to analyze the RMS mean discrepancies (α=.05). The Levene test was used to analyze the equality of the variances. RESULTS: Trueness ranged from 15 to 17 µm with a precision of 4 µm among the subgroups in which the existing digital scan was blocked, but the trueness ranged from 42 to 72 µm and the precision ranged from 15 to 47 µm among the subgroups in which the rescanning procedures allowed the modification of the existing digital scan. Significant trueness differences were found among the groups tested (P<.05). Significant differences in the RMS values were computed between the WO and NO subgroups for each group (M (P<.001): PM (P<.001); C (P<.001), and I (P<.001) groups), but the effect of the tooth mesh hole location demonstrated no significant difference either among the WO (P>.999) or NO subgroups (P>.999). Furthermore, the NO groups showed markedly better precision than the WO groups for each tooth location. The I-WO group showed better precision than the groups C-WO, PM-WO, and M-WO. However, when no overlapping was allowed, no difference was found in precision between the different tooth locations tested. CONCLUSIONS: Rescanning procedures influenced intraoral scanning accuracy. Allowing further modification of the preexisting intraoral digital scan demonstrated a significantly decreased scanning accuracy. However, tooth location of the rescanned mesh hole did not impact scanning accuracy.

Effect of splinting scan bodies on the trueness of complete-arch digital implant scans with 5 different intraoral scanners.

STATEMENT OF PROBLEM: The absence of fixed reference points can affect the trueness of complete-arch intraoral digital implant scans. The effect of splinting intraoral scan bodies (ISBs) or the inclusion of artificial landmarks (AL) on the trueness of complete-arch digital implant scans is still unclear. PURPOSE: The purpose of this study was to analyze the effect of splinting ISBs or the inclusion of AL on the trueness of complete-arch digital implant scans with 5 intraoral scanners (IOSs). MATERIAL AND METHODS: Six tissue-level dental implants (Straumann Tissue Level) were placed in an edentulous patient, and the correspondent definitive cast was digitized with a desktop scanner (IScan4D LS3i) to obtain the reference digital cast. Digital scans (n=10) were performed with 5 IOSs: TRIOS 4, Virtuo Vivo, Medit i700, iTero Element 5D, and Cerec Primescan. Three different scanning techniques were evaluated: conventional (cIOSs), splinted (sIOSs), and AL (AL-IOSs). The scan data obtained were imported into a metrology software program and superimposed to the reference digital cast by using a best-fit algorithm. The overall deviations of the positions of the ISBs were evaluated by using the root-mean-square (RMS) error (α=.05). RESULTS: The mean ±standard deviation trueness values for the cIOSs, sIOSs, and AL-IOSs groups were 48 ±8 µm, 53 ±7 µm, and 49 ±11 µm, respectively, with no statistically significant differences (P=.06). Significant differences were found for the IOSs used with each technique (P<.001). Primescan (27 ±4 µm cIOSs; 28 ±3 µm sIOSs; 31 ±3 µm AL-IOSs) showed significantly higher trueness than iTero 5D (47 ±5 µm cIOSs; 47 ±4 µm sIOSs; 50 ±6 µm AL-IOSs) (P=.002) and TRIOS 4 (93 ±18 µm cIOSs; 76 ±18 µm sIOSs; 107 ±13 µm AL-IOSs) (P=.001) for all techniques. In addition, no significant differences were found between the techniques by using iTero 5D or Primescan (P=.348 and P=.059, respectively). CONCLUSIONS: The cIOSs, sIOSs, and AL-IOSs techniques showed similar trueness. The IOS used influenced the trueness of complete-arch digital implant scans.

Discrepancies in the occlusal devices designed by an experienced dental laboratory technician and by 2 artificial intelligence-based automatic programs.

STATEMENT OF PROBLEM: Artificial intelligence (AI) models have been developed for different applications, including the automatic design of occlusal devices; however, the design discrepancies of an experienced dental laboratory technician and these AI automatic programs remain unknown. PURPOSE: The purpose of this in vitro study was to compare the overall, intaglio, and occlusal surface discrepancies of the occlusal device designs completed by an experienced dental laboratory technician and two AI automatic design programs. MATERIAL AND METHODS: Virtually articulated maxillary and mandibular diagnostic casts were obtained in a standard tessellation language (STL) file format. Three groups were created depending on the operator or program used to design the occlusal devices: an experienced dental laboratory technician (control group) and two AI programs, namely Medit Splints from Medit (Medit group) and Automate from 3Shape A/S (3Shape group) (n=10). To minimize the discrepancies in the parameter designs among the groups tested, the same printing material and design parameters were selected. In the control group, the dental laboratory technician imported the articulated scans into a dental design program (DentalCAD) and designed a maxillary occlusal device. The occlusal device designs were exported in STL format. In the Medit and 3Shape groups, the diagnostic casts were imported into the respective AI programs. The AI programs automatically designed the occlusal device without any further operator intervention. The occlusal device designs were exported in STL format. Among the 10 occlusal designs of the control group, a random design (shuffle deck of cards) was used as a reference file to calculate the overall, intaglio, and occlusal discrepancies in the specimens of the AI groups by using a program (Medit Design). The root mean square (RMS) error was calculated. Kruskal-Wallis, and post hoc Dwass-Steel-Critchlow-Fligner pairwise comparison tests were used to analyze the trueness of the data. The Levene test was used to assess the precision data (α=.05). RESULTS: Significant overall (P<.001), intaglio (P<.001), and occlusal RMS median value (P<.001) discrepancies were found among the groups. Significant overall RMS median discrepancies were observed between the control and the Medit groups (P<.001) and the control and 3Shape groups (P<.001). Additionally, significant intaglio RMS median discrepancies were found between the control and the Medit groups (P<.001), the Medit and 3Shape groups (P<.001), and the control and 3Shape groups (P=.008). Lastly, significant occlusal RMS median discrepancies were found between the control and the 3Shape groups (P<.001) and the Medit and 3Shape groups (P<.001). The AI-based software programs tested were able to automatically design occlusal devices with less than a 100-µm trueness discrepancy compared with the dental laboratory technician. The Levene test revealed significant overall (P<.001), intaglio (P<.001), and occlusal (P<.001) precision among the groups tested. CONCLUSIONS: The use of a dental laboratory technique influenced the overall, intaglio, and occlusal trueness of the occlusal device designs obtained. No differences were observed in the precision of occlusal device designs acquired among the groups tested.

Coverage error and shade-match accuracy in three ceramic gingival systems.

STATEMENT OF PROBLEM: Research into the coverage error (CE) of gingival systems that have been expanded by using ceramic specimens created by mixing basic colors is lacking. PURPOSE: The purpose of this in vitro study was to compare the CEs of 3 ceramic gingival color systems that have been expanded with basic-color mixtures from a sample of 360 White participants and to classify the participants according to the accuracy of the shade match achieved with each system. MATERIAL AND METHODS: L*a*b* color coordinates were recorded in 3 zones of attached gingiva for 360 White participants with healthy gingival tissue (187 men and 173 women). The CEs were calculated for 3 ceramic gingival systems that had been expanded with specimens obtained by mixing the basic colors in consecutive order, the color percentages in each mixture having been altered by 10% increments. The systems were Heraceram (Kulzer GmbH) (n=51); Vita VM9 (Vita-Zahnfabrik) (n=41); and IPS Style (Ivoclar AG) (n=41). The participants were classified into 3 groups according to how well the selected shade matched their gingival color (excellent, acceptable, or poor). The data were analyzed using a 1-way ANOVA with a randomized block design and the homogeneity of proportions test (α=.05). RESULTS: Statistically significant differences were found between the CEs of the 3 expanded gingival systems in the 3 zones where gingival color was measured (P<.001). The expanded Heraceram system had the smallest CE (ΔE00: minimum 2.66 in the middle zone and maximum 2.95 at the mucogingival line). In the 3 gingival zones, the expanded IPS Style system produced the largest percentage of participants with a poor shade match (ΔE00: minimum 71.4% at the mucogingival line and maximum 75.8% at the free gingival margin), while the expanded Heraceram system had the lowest percentage of participants with a poor shade match (ΔE00: minimum 33.3% in the middle zone and maximum 41.7% at the mucogingival line). CONCLUSIONS: The CEs calculated for the expanded Vita VM9 and IPS Style ceramic gingival color systems exceeded the clinical acceptability thresholds in the 3 zones examined. According to the ΔE00 formula, the gingival color of at least 33% of participants matched poorly with the expanded systems studied.

Artificial intelligence models for tooth-supported fixed and removable prosthodontics: A systematic review.

STATEMENT OF PROBLEM: Artificial intelligence applications are increasing in prosthodontics. Still, the current development and performance of artificial intelligence in prosthodontic applications has not yet been systematically documented and analyzed. PURPOSE: The purpose of this systematic review was to assess the performance of the artificial intelligence models in prosthodontics for tooth shade selection, automation of restoration design, mapping the tooth preparation finishing line, optimizing the manufacturing casting, predicting facial changes in patients with removable prostheses, and designing removable partial dentures. MATERIAL AND METHODS: An electronic systematic review was performed in MEDLINE/PubMed, EMBASE, Web of Science, Cochrane, and Scopus. A manual search was also conducted. Studies with artificial intelligence models were selected based on 6 criteria: tooth shade selection, automated fabrication of dental restorations, mapping the finishing line of tooth preparations, optimizing the manufacturing casting process, predicting facial changes in patients with removable prostheses, and designing removable partial dentures. Two investigators independently evaluated the quality assessment of the studies by applying the Joanna Briggs Institute Critical Appraisal Checklist for Quasi-Experimental Studies (nonrandomized experimental studies). A third investigator was consulted to resolve lack of consensus. RESULTS: A total of 36 articles were reviewed and classified into 6 groups based on the application of the artificial intelligence model. One article reported on the development of an artificial intelligence model for tooth shade selection, reporting better shade matching than with conventional visual selection; 14 articles reported on the feasibility of automated design of dental restorations using different artificial intelligence models; 1 artificial intelligence model was able to mark the margin line without manual interaction with an average accuracy ranging from 90.6% to 97.4%; 2 investigations developed artificial intelligence algorithms for optimizing the manufacturing casting process, reporting an improvement of the design process, minimizing the porosity on the cast metal, and reducing the overall manufacturing time; 1 study proposed an artificial intelligence model that was able to predict facial changes in patients using removable prostheses; and 17 investigations that developed clinical decision support, expert systems for designing removable partial dentures for clinicians and educational purposes, computer-aided learning with video interactive programs for student learning, and automated removable partial denture design. CONCLUSIONS: Artificial intelligence models have shown the potential for providing a reliable diagnostic tool for tooth shade selection, automated restoration design, mapping the preparation finishing line, optimizing the manufacturing casting, predicting facial changes in patients with removable prostheses, and designing removable partial dentures, but they are still in development. Additional studies are needed to further develop and assess their clinical performance.

Influence of ambient temperature changes on intraoral scanning accuracy.

STATEMENT OF PROBLEM: Different variables that decrease the accuracy of intraoral scanners (IOSs) have been identified. Ambient temperature changes can occur in the dental environment, but the impact of ambient temperature changes on intraoral scanning accuracy is unknown. PURPOSE: The purpose of this in vitro study was to assess the impact of ambient temperature changes on the accuracy (trueness and precision) of an IOS. MATERIAL AND METHODS: A complete arch maxillary dentate Type IV stone cast was obtained. Four 6-mm-diameter gauge balls were added to the maxillary cast to aid future evaluation measurements. The maxillary cast was digitized by using an industrial scanner (GOM Atos Q 3D 12M). The manufacturer's recommendations were followed in obtaining a reference scan. Then, the maxillary cast was digitized by using an IOS (TRIOS 4) according to the scanning protocol recommended by the manufacturer. Four groups were created depending on the ambient temperature change assessed: 24 °C or room temperature (24-D or control group), 19 °C or a 5-degree temperature drop (19-D group), 15 °C or a 9-degree temperature drop (15-D group), and 29 °C or a 5-degree temperature rise (29-D group). The Shapiro-Wilk and Kolmogorov-Smirnov tests revealed that the data were not normally distributed (P<.05). For trueness, the nonparametric Kruskal-Wallis followed by the Dwass-Steel-Critchlow-Fligner pairwise comparison tests were used. Precision analysis was obtained by using the Levene test based on the comparison of the standard deviations of the 4 groups with 95% Bonferroni confidence intervals for standard deviations (α=.05). RESULTS: The Kruskal-Wallis test revealed significant differences in the trueness values among all 4 groups (P<.001). Furthermore, significant differences between the linear discrepancy medians between the control and 19-D groups (P<.001), control and 15-D groups (P=.002), control and 29-D groups (P<.001), 19-D and 29-D groups (P=.003), and 15-D and 29-D groups (P<.001) were found. The Levene test for the comparison of the variances among the 4 groups did not detect a significant difference (P>.999), indicating that precision wise the 4 groups were not significantly different from each other. CONCLUSIONS: Ambient temperature changes had a detrimental effect on the accuracy (trueness and precision) of the IOS tested. Ambient temperature changes significantly decreased the scanning accuracy of the IOS system tested. Increasing the ambient temperature has a greater influence on the intraoral scanning accuracy of the IOS selected when compared with decreasing the ambient temperature.

Artificial intelligence models for diagnosing gingivitis and periodontal disease: A systematic review.

STATEMENT OF PROBLEM: Artificial intelligence (AI) models have been developed for periodontal applications, including diagnosing gingivitis and periodontal disease, but their accuracy and maturity of the technology remain unclear. PURPOSE: The purpose of this systematic review was to evaluate the performance of the AI models for detecting dental plaque and diagnosing gingivitis and periodontal disease. MATERIAL AND METHODS: A review was performed in 4 databases: MEDLINE/PubMed, World of Science, Cochrane, and Scopus. A manual search was also conducted. Studies were classified into 4 groups: detecting dental plaque, diagnosis of gingivitis, diagnosis of periodontal disease from intraoral images, and diagnosis of alveolar bone loss from periapical, bitewing, and panoramic radiographs. Two investigators evaluated the studies independently by applying the Joanna Briggs Institute critical appraisal. A third examiner was consulted to resolve any lack of consensus. RESULTS: Twenty-four articles were included: 2 studies developed AI models for detecting plaque, resulting in accuracy ranging from 73.6% to 99%; 7 studies assessed the ability to diagnose gingivitis from intraoral photographs reporting an accuracy between 74% and 78.20%; 1 study used fluorescent intraoral images to diagnose gingivitis reporting 67.7% to 73.72% accuracy; 3 studies assessed the ability to diagnose periodontal disease from intraoral photographs with an accuracy between 47% and 81%, and 11 studies evaluated the performance of AI models for detecting alveolar bone loss from radiographic images reporting an accuracy between 73.4% and 99%. CONCLUSIONS: AI models for periodontology applications are still in development but might provide a powerful diagnostic tool.