Accuracy of artificial intelligence-based segmentation of the mandibular canal in CBCT.

OBJECTIVES: To investigate the accuracy of artificial intelligence (AI)-based segmentation of the mandibular canal, compared to the conventional manual tracing, implementing implant planning software. MATERIALS AND METHODS: Localization of the mandibular canals was performed for 104 randomly selected patients. A localization was performed by three experienced clinicians in order to serve as control. Five tracings were performed: One from a clinician with a moderate experience with a manual tracing (I1), followed by the implementation of an automatic refinement (I2), one manual from a dental student (S1), and one from the experienced clinician, followed by an automatic refinement (E). Subsequently, two fully automatic AI-driven segmentations were performed (A1,A2). The accuracy between each method was measured using root mean square error calculation. RESULTS: The discrepancy among the models of the mandibular canals, between the experienced clinicians and each investigated method ranged from 0.21 to 7.65 mm with a mean of 3.5 mm RMS error. The analysis of each separate mandibular canal's section revealed that mean RMS error was higher in the posterior and anterior loop compared to the middle section. Regarding time efficiency, tracing by experienced users required more time compared to AI-driven segmentation. CONCLUSIONS: The experience of the clinician had a significant influence on the accuracy of mandibular canal's localization. An AI-driven segmentation of the mandibular canal constitutes a time-efficient and reliable procedure for pre-operative implant planning. Nevertheless, AI-based segmentation results should always be verified, as a subsequent manual refinement of the initial segmentation may be required to avoid clinical significant errors.

Accuracy of manual and artificial intelligence-based superimposition of cone-beam computed tomography with digital scan data, utilizing an implant planning software: A randomized clinical study.

OBJECTIVES: To investigate the accuracy of conventional and automatic artificial intelligence (AI)-based registration of cone-beam computed tomography (CBCT) with intraoral scans and to evaluate the impact of user's experience, restoration artifact, number of missing teeth, and free-ended edentulous area. MATERIALS AND METHODS: Three initial registrations were performed for each of the 150 randomly selected patients, in an implant planning software: one from an experienced user, one from an inexperienced operator, and one from a randomly selected post-graduate student of implant dentistry. Six more registrations were performed for each dataset by the experienced clinician: implementing a manual or an automatic refinement, selecting 3 small or 3 large in-diameter surface areas and using multiple small or multiple large in-diameter surface areas. Finally, an automatic AI-driven registration was performed, using the AI tools that were integrated into the utilized implant planning software. The accuracy between each type of registration was measured using linear measurements between anatomical landmarks in metrology software. RESULTS: Fully automatic-based AI registration was not significantly different from the conventional methods tested for patients without restorations. In the presence of multiple restoration artifacts, user's experience was important for an accurate registration. Registrations' accuracy was affected by the number of free-ended edentulous areas, but not by the absolute number of missing teeth (p < .0083). CONCLUSIONS: In the absence of imaging artifacts, automated AI-based registration of CBCT data and model scan data can be as accurate as conventional superimposition methods. The number and size of selected superimposition areas should be individually chosen depending on each clinical situation.

What comes first: teeth or face? Recommendations for an interdisciplinary collaboration between facial esthetic surgery and dentistry.

OBJECTIVE: The aim of the present study was to provide recommendations in order to facilitate communication between dental professionals and surgeons who are collaborating in the field of dentofacial esthetics. CLINICAL CONSIDERATIONS: Smile esthetics are beyond the scope, both of the surgeons who are collaborating with facial esthetics and of the dentists, as a wide range of treatment options from both sides is available. It can be difficult for the surgeon or the dentist that first comes in contact with the patient to conduct an individualized global treatment plan, in order to find out how the various phases of the treatment can be sequenced, as a workflow for an efficient interaction between facial surgery and dentistry still does not exist in the scientific literature. CONCLUSIONS: Facial cosmetic procedures and dental treatment have to be planned as individual elements of the whole dentofacial esthetic rehabilitation. The treatment has to be initiated with the design of the smile and the intraoral mock-up, followed by the required surgical interventions, and to be finished with the delivery of the definitive dental restoration. CLINICAL SIGNIFICANCE: Dentofacial esthetics require comprehensive communication between surgeons and dentists. Following the proposed recommendations, an individualized interdisciplinary treatment plan can be conducted, defining the role of each specialty.

Cephalometric analysis performance discrepancy between orthodontists and an artificial intelligence model using lateral cephalometric radiographs.

PURPOSE: The purpose of the present clinical study was to compare the Ricketts and Steiner cephalometric analysis obtained by two experienced orthodontists and artificial intelligence (AI)-based software program and measure the orthodontist variability. MATERIALS AND METHODS: A total of 50 lateral cephalometric radiographs from 50 patients were obtained. Two groups were created depending on the operator performing the cephalometric analysis: orthodontists (Orthod group) and an AI software program (AI group). In the Orthod group, two independent experienced orthodontists performed the measurements by performing a manual identification of the cephalometric landmarks and a software program (NemoCeph; Nemotec) to calculate the measurements. In the AI group, an AI software program (CephX; ORCA Dental AI) was selected for both the automatic landmark identification and cephalometric measurements. The Ricketts and Steiner cephalometric analyses were assessed in both groups including a total of 24 measurements. The Shapiro-Wilk test showed that the data was normally distributed. The t-test was used to analyze the data (α = 0.05). RESULTS: The t-test analysis showed significant measurement discrepancies between the Orthod and AI group in seven of the 24 cephalometric parameters tested, namely the corpus length (p = 0.003), mandibular arc (p < 0.001), lower face height (p = 0.005), overjet (p = 0.019), and overbite (p = 0.022) in the Ricketts cephalometric analysis and occlusal to SN (p = 0.002) and GoGn-SN (p < 0.001) in the Steiner cephalometric analysis. The intraclass correlation coefficient (ICC) between both orthodontists of the Orthod group for each cephalometric measurement was calculated. CONCLUSIONS: Significant discrepancies were found in seven of the 24 cephalometric measurements tested between the orthodontists and the AI-based program assessed. The intra-operator reliability analysis showed reproducible measurements between both orthodontists, except for the corpus length measurement. CLINICAL SIGNIFICANCE: The artificial intelligence software program tested has the potential to automatically obtain cephalometric analysis using lateral cephalometric radiographs; however, additional studies are needed to further evaluate the accuracy of this AI-based system.

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.

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

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.

Prosthetic articulator-based implant rehabilitation virtual patient: A technique bridging implant surgery and reconstructive dentistry.

This technique report describes a fully digital workflow to create a prosthetic articulator-based implant rehabilitation (PAIR) virtual patient for complete-arch or complete-mouth implant rehabilitation. This workflow uses a custom gothic arch tracer during the cone beam computed tomography (CBCT) scan and a 3-dimensional virtual facebow when superimposing data. The PAIR virtual patient possesses reliable centric relation and vertical dimension of occlusion and is compatible with virtual articulators. Computer-aided implant planning and a digital prosthetic design can be seamlessly integrated by using this virtual patient.

Trueness and precision of complete-arch photogrammetry implant scanning assessed with a coordinate-measuring machine.

STATEMENT OF PROBLEM: Photogrammetry technology has been used for the digitalization of multiple dental implants, but its trueness and precision remain uncertain. PURPOSE: The purpose of this in vitro investigation was to compare the accuracy (trueness and precision) of multisite implant recordings between the conventional method and a photogrammetry dental system. MATERIAL AND METHODS: A definitive cast of an edentulous maxilla with 6 implant abutment replicas was tested. Two different recording methods were compared, the conventional technique and a photogrammetry digital scan (n=10). For the conventional group, the impression copings were splinted to an additively manufactured cobalt-chromium metal with autopolymerizing acrylic resin, followed by recording the maxillary edentulous arch with an elastomeric impression using an additively manufactured open custom tray. For the photogrammetry group, a scan body was placed on each implant abutment replica, followed by the photogrammetry digital scan. A coordinate-measuring machine was selected to assess the linear, angular, and 3-dimensional discrepancies between the implant abutment replica positions of the reference cast and the specimens by using a computer-aided design program. The Shapiro-Wilk test showed that the data were not normally distributed. The Mann-Whitney U test was used to analyze the data (α=.05). RESULTS: The conventional group obtained an overall accuracy (trueness ±precision) value of 18.40 ±6.81 µm, whereas the photogrammetry group showed an overall scanning accuracy value of 20.15 ±25.41 µm. Significant differences on the discrepancies on the x axis (U=1380.00, P=.027), z axis (U=601.00, P<.001), XZ angle (U=869.00, P<.001), and YZ angle (U=788.00, P<.001) were observed when the measurements of the 2 groups were compared. Furthermore, significant 3-dimensional discrepancy for implant 1 (U=0.00, P<.001), implant 2 (U=0.00, P<.001), implant 3 (U=6.00, P<.001), and implant 6 (U=9.00, P<.001) were computed between the groups. CONCLUSIONS: The conventional method obtained statistically significant higher overall accuracy values compared with the photogrammetry system tested, with a trueness difference of 1.8 µm and a precision difference of 18.6 µm between the systems. The conventional method transferred the implant abutment positions with a uniform 3-dimensional discrepancy, but the photogrammetry system obtained an uneven overall discrepancy among the implant abutment positions.

Effect of supramucosal height of a scan body and implant angulation on the accuracy of intraoral scanning: An in vitro study.

STATEMENT OF PROBLEM: Intraoral scanners (IOSs) provide a digital alternative to conventional implant impression techniques. However, the effect of the supramucosal height of the scan body and implant angulation on the accuracy of IOSs remains unclear. PURPOSE: The purpose of this in vitro study was to measure the impact of the supramucosal height of the scan body and implant angulation on the accuracy (trueness and precision) of intraoral digital implant scans in partially edentulous models. MATERIAL AND METHODS: Two maxillary partially edentulous casts with 4 implant analogs were fabricated, 1 with 4 parallel implants (P-groups) and 1 with 2 implants distally inclined 18 degrees (A-groups). An implant scan body was positioned on each implant analog (CARES RC Mono Scanbody). For each cast, 3 subgroups were determined based on the soft tissue moulage fabricated for each reference cast exposing 3 mm (P-3 and A-3 subgroups), 5 mm (P-5 and A-5 subgroups), and 7 mm (P-7 and A-7 subgroups) of the implant scan bodies. The 2 reference casts were registered by using a coordinate measurement machine and desktop scanner (7 Series Dental Wings) and then scanned using an IOS (TRIOS 4) (n=15). Linear and angular discrepancy values and root mean square (RMS) error values between the implant scan bodies measured on the reference and experimental scans were computed with an inspection software program (Geomagic). Mann-Whitney U tests with Bonferroni correction were applied for planned comparisons (α=.05/9 ≈ .006). RESULTS: For linear discrepancies, statistically significant differences were found between groups P-3 and A-3 (P=.004) and between P-7 and A-7 (P=.005). For angular discrepancies, statistically significant differences were found between groups A-3 and A-5 (P=.002) and between P-7 and A-7 (P=.003). The RMS error analysis found no statistically significant differences among the groups. CONCLUSIONS: Implant angulation of 18 degrees did not significantly affect the accuracy of the intraoral scans in terms of 6 of the 9 planned comparisons, although the angled groups had lower mean values. Also, the supramucosal height of the scan body did not significantly affect the accuracy of the intraoral scans in terms of 17 of the 18 planned comparisons. Results may vary with different implant scan body designs.

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 applications in implant dentistry: A systematic review.

STATEMENT OF PROBLEM: Artificial intelligence (AI) applications are growing in dental implant procedures. The current expansion and performance of AI models in implant dentistry applications have not yet been systematically documented and analyzed. PURPOSE: The purpose of this systematic review was to assess the performance of AI models in implant dentistry for implant type recognition, implant success prediction by using patient risk factors and ontology criteria, and implant design optimization combining finite element analysis (FEA) calculations and AI models. MATERIAL AND METHODS: An electronic systematic review was completed in 5 databases: MEDLINE/PubMed, EMBASE, World of Science, Cochrane, and Scopus. A manual search was also conducted. Peer-reviewed studies that developed AI models for implant type recognition, implant success prediction, and implant design optimization were included. The search strategy included articles published until February 21, 2021. Two investigators independently evaluated the quality of the studies by applying the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Quasi-Experimental Studies (nonrandomized experimental studies). A third investigator was consulted to resolve lack of consensus. RESULTS: Seventeen articles were included: 7 investigations analyzed AI models for implant type recognition, 7 studies included AI prediction models for implant success forecast, and 3 studies evaluated AI models for optimization of implant designs. The AI models developed to recognize implant type by using periapical and panoramic images obtained an overall accuracy outcome ranging from 93.8% to 98%. The models to predict osteointegration success or implant success by using different input data varied among the studies, ranging from 62.4% to 80.5%. Finally, the studies that developed AI models to optimize implant designs seem to agree on the applicability of AI models to improve the design of dental implants. This improvement includes minimizing the stress at the implant-bone interface by 36.6% compared with the finite element model; optimizing the implant design porosity, length, and diameter to improve the finite element calculations; or accurately determining the elastic modulus of the implant-bone interface. CONCLUSIONS: AI models for implant type recognition, implant success prediction, and implant design optimization have demonstrated great potential but are still in development. Additional studies are indispensable to the further development and assessment of the clinical performance of AI models for those implant dentistry applications reviewed.

Influence of the Number of Teeth and Location of the Virtual Occlusal Record on the Accuracy of the Maxillo-Mandibular Relationship Obtained by Using An Intraoral Scanner.

PURPOSE: To assess the influence of the number of teeth (2, 3, or 4) and location (molars, molar and premolar, or premolars and canines) of the bilateral virtual occlusal record on the accuracy of the virtual maxillo-mandibular relationship acquired by an intraoral scanner (IOS). MATERIAL AND METHODS: Diagnostic casts mounted on a semi-adjustable articulator were obtained. Four markers were adhered on the facial surfaces of the first molars and canines. The mounted casts were digitized using an extraoral scanner. Maxillary and mandibular intraoral digital scans were obtained using an intraoral scanner (TRIOS 4). The maxillary and mandibular digital scans were duplicated 105 times and divided into 7 groups based on the number of teeth (2, 3, or 4) and location (molar, molar and premolar, or premolars and canines) of the bilateral virtual occlusal records (n = 15). The alignment of the scans was automatically performed after the acquisition of the corresponding occlusal records by the IOS program. Eight linear distances between the gauge balls were computed on the reference scan and on the 105 digital scans. The distances obtained on the reference scan were used to calculate the discrepancies with the distances obtained on each experimental scan. The Shapiro-Wilk test showed that the data was normally distributed. The trueness and precision data were analyzed using 2-way ANOVA followed by pairwise comparison Tukey tests (α = 0.05). RESULTS: Two-way ANOVA showed that the number of teeth (p < 0.001) and the position of the virtual occlusal record (p < 0.001) were significant factors on the accuracy of the maxillo-mandibular relationship. Tukey test showed significant overall mean differences between the different groups tested: the 4-teeth group obtained the highest trueness, and the 2-teeth group showed the lowest trueness values (p < 0.001). Tukey test showed significant trueness differences between the virtual occlusal record locations. The 2-teeth record located more posteriorly obtained the lowest trueness. Significant differences in precision values were found among the subgroups tested (p < 0.001). The 2-teeth group obtained significantly more precision values than the 3- and 4-teeth groups. Additionally, there was a significant difference in precision values between the subgroup tested in which the first molar and second premolar location had the highest precision, while the first and second premolar's location obtained the lowest precision. CONCLUSIONS: The number of teeth and the location of the bilateral virtual occlusal record influenced the accuracy of the virtual maxillo-mandibular relationship obtained by the intraoral scanner tested. The more teeth included in the bilateral virtual occlusal record, the higher the accuracy of the maxillo-mandibular relationship. Additionally, the more anteriorly located the virtual bilateral occlusal record involving 2 or 3 teeth was, the higher the accuracy mean value.

Effects of thermal and mechanical cycling on the mechanical strength and surface properties of dental CAD-CAM restorative materials.

STATEMENT OF PROBLEM: The properties of dental computer-aided design and computer-aided manufacturing (CAD-CAM) materials vary. Studies regarding the effects of aging on the properties of these materials are lacking. PURPOSE: The purpose of this in vitro study was to evaluate the changes in the mechanical and surface properties of different CAD-CAM materials after thermocycling and mechanical loading. MATERIAL AND METHODS: In total, 150 bar-shaped specimens (17.0×4.0×2.0 mm) were prepared from feldspathic glass-ceramic (VM; Vitablocs Mark II), lithium disilicate glass-ceramic (EX; IPS e.max CAD), zirconia-reinforced lithium silicate glass-ceramic (CD; Celtra Duo), polymer-infiltrated ceramic network (VE; Vita Enamic), and resin-nanoceramic (CS; Cerasmart). Each type was divided into 2 groups (n=15; each). One group was subjected to thermocycling in distilled water at 5 °C to 55 °C for 6000 cycles and 50 N mechanical loading for 1.2×106 cycles. The other group was stored in 37 °C water for 24 hours. Nanoindentation hardness, Young modulus, and 3-point flexural strength were measured for the analyses of the mechanical properties. Surface roughness, surface microstructure, and elemental composition were measured to analyze the surface characteristics. Statistical analyses were performed with 1-way ANOVA with the Tukey HSD post hoc test, independent samples t test, Kruskal-Wallis test with Bonferroni post hoc test, Mann-Whitney U test, and 2-way ANOVA (α=.05). RESULTS: Before and after aging, CS exhibited the lowest hardness (1.20 to 1.04 GPa) and Young modulus (13.76 to 13.48 GPa) values (P<.05). EX exhibited the highest flexural strengths (393.43 to 391.86 MPa), and VM exhibited the lowest (109.98 to 112.73 MPa) values (P<.05). CS exhibited the highest surface roughness (Sa and Sq; 10.60 to 28.82, 14.21 to 38.27 nm) values (P<.05). After aging, the hardness and Young modulus of VM, EX, and VE decreased significantly (P<.001). No significant difference was observed in the flexural strengths of the CAD-CAM materials (P>.05). Significant increases were observed in the surface roughness of all the materials (P<.05), with altered microstructures. Except for the flexural strength, the mechanical properties and surface characteristics of the CAD-CAM materials were significantly affected by the material type after aging. CONCLUSIONS: Before and after aging, resin-nanoceramic exhibited the lowest hardness and Young modulus, and the highest surface roughness. Lithium disilicate glass-ceramic exhibited the highest flexural strength and feldspathic glass-ceramic exhibited the lowest value. After aging, increased surface roughness and microstructure alterations were observed. Significant interactions between aging process and material type were found for the mechanical properties and surface characteristics except for the flexural strength.

Influence of implant angulation and clinical implant scan body height on the accuracy of complete arch intraoral digital scans.

STATEMENT OF PROBLEM: The accuracy of digital implant scans can be affected by the implant angulation, implant depth, or interimplant distance. However, studies analyzing intraoral scanning accuracy with different implant angulations and different scan body heights are scarce. PURPOSE: The purpose of this in vitro study was to determine the influence of the implant angulation and clinical implant scan body height on the accuracy of complete arch scans. MATERIAL AND METHODS: Two definitive implant casts with 6 implant analogs (Zimmer Biomet) were obtained: 1 cast had all the implant analogs parallel (GP group), and 1 cast had the implant analogs with divergence of up to 30 degrees (GD group). A coordinate measurement machine (Global Evo 09.15.08) was used to measure the positions of the implant analogs. Each group was divided into 3 subgroups depending on the clinical implant scan body height: 10, 6, and 3 mm. An implant scan body (Elos Accurate Scan Body Brånemark system) was positioned on each implant analog. A total of 10 scans of each subgroup were recorded by using an intraoral scanner (TRIOS 3). Each STL file obtained was imported into a reverse engineering software program (Geomagic), and linear and angular Euclidean measurements were obtained. The Euclidean calculations between the implant analog positions of the definitive implant casts were used as a reference to calculate the discrepancies among the corresponding subgroups. The Kolmogorov-Smirnov test revealed that the lineal measurements were not normally distributed, so the Kruskal-Wallis and pairwise comparison Dunn tests were used (α=.05). The Kolmogorov-Smirnov test revealed that the angular measurements were normally distributed. Therefore, the 2-way ANOVA and pairwise comparison Tukey tests were used (α=.05). RESULTS: The Kruskal-Wallis test revealed significant differences in the linear Euclidean medians between the GP and GD groups with different clinical implant scan body heights (H(5)=23.18, P<.001). Significant differences in the linear Euclidean medians were computed between the GP-6 and GD-10 subgroups (P=.009), GD-3 and GD-6 subgroups (P=.029), and GD-3 and GD-10 subgroups (P=.001). Two-way ANOVA revealed that the implant angulation (F(1, 3.3437)=28.93, P<.001) and clinical implant scan body height (F(2, 0.4358)=3.77, P=.029) were significant predictors of discrepancies in the angular measurement. CONCLUSIONS: Implant angulation and clinical scan body height influenced scanning accuracy. The lowest clinical implant scan body height tested had the lowest accuracy in both parallel and angulated implants, but statistically significant differences were found only in the angulated group.

Artificial intelligence applications in restorative dentistry: A systematic review.

STATEMENT OF PROBLEM: Artificial intelligence (AI) applications are increasing in restorative procedures. However, the current development and performance of AI in restorative dentistry applications has not yet been systematically documented and analyzed. PURPOSE: The purpose of this systematic review was to identify and evaluate the ability of AI models in restorative dentistry to diagnose dental caries and vertical tooth fracture, detect tooth preparation margins, and predict restoration failure. MATERIAL AND METHODS: An electronic systematic review was performed in 5 databases: MEDLINE/PubMed, EMBASE, World of Science, Cochrane, and Scopus. A manual search was also conducted. Studies with AI models were selected based on 4 criteria: diagnosis of dental caries, diagnosis of vertical tooth fracture, detection of the tooth preparation finishing line, and prediction of restoration failure. Two investigators independently evaluated the quality assessment of the studies by applying the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Quasi-Experimental Studies (nonrandomized experimental studies). A third investigator was consulted to resolve lack of consensus. RESULTS: A total of 34 articles were included in the review: 29 studies included AI techniques for the diagnosis of dental caries or the elaboration of caries and postsensitivity prediction models, 2 for the diagnosis of vertical tooth fracture, 1 for the tooth preparation finishing line location, and 2 for the prediction of the restoration failure. Among the studies reviewed, the AI models tested obtained a caries diagnosis accuracy ranging from 76% to 88.3%, sensitivity ranging from 73% to 90%, and specificity ranging from 61.5% to 93%. The caries prediction accuracy among the studies ranged from 83.6% to 97.1%. The studies reported an accuracy for the vertical tooth fracture diagnosis ranging from 88.3% to 95.7%. The article using AI models to locate the finishing line reported an accuracy ranging from 90.6% to 97.4%. CONCLUSIONS: AI models have the potential to provide a powerful tool for assisting in the diagnosis of caries and vertical tooth fracture, detecting the tooth preparation margin, and predicting restoration failure. However, the dental applications of AI models are still in development. Further studies are required to assess the clinical performance of AI models in restorative dentistry.

Short Versus Regular-Length Implants to Rehabilitate Partially Edentulous Mandible: A 2-Year Prospective Split-Mouth Clinical Study.

Many studies have evaluated short implants (SIs); however, it is still unclear whether SIs are reliable and can be used to simplify surgical and prosthetic protocols with successful clinical outcomes. The aim of this nonrandom, conveniently sampled, prospective, split-mouth study was to compare the clinical outcomes when short SI (≤8 mm) or regular-length implants (RIs; >10 mm) were used in the posterior mandible 2 years after the delivery of splinted reconstructions. Each participant (N = 10) received 4 implants in the posterior mandible; 2 SIs were placed on one side, and 2 RIs were placed contralaterally. Implants were restored with splinted, screw-retained, porcelain-fused-to-metal reconstructions. Survival and success rates, peri-implant marginal bone level (MBL), and soft-tissue parameters were evaluated. No participant dropouts were recorded. Both types of implants showed 100% success and survival rates. From prosthetic delivery to 24 months postloading, bone remineralization of +0.40 mm for the SIs and +0.36 mm for the RIs was observed without statistically significant differences in MBL between the implant types (P = .993). SIs showed significantly higher (P = .001) clinical attachment level and probing depth values. Chipping occurred in one situation in the RI group, resulting in a 97.5% prosthetic success rate, which was 100% for the SIs. After 2 years, SIs with splinted reconstructions showed comparable clinical outcomes to those of RIs. Further long-term controlled clinical studies with balanced experimental designs evaluating random and larger populations are required to corroborate these findings.

STAR concept: A technique to improve the predictability of immediate implant placement and loading in edentulous arches.

The digital workflow in implant dentistry aims to provide safer and predictable implant placement. This is facilitated through visualization of the anatomical structures as well as the integration of the prosthetic information to dictate implant placement. Guided surgery is useful in edentulous arches planned for fixed rehabilitations, where the unfavorable position of the implants may limit the possibility to realize such a treatment option. In full-arch immediate implant placement and immediate loading, computer-guided implant surgery gains a significant importance. Here, clinicians rely on either soft tissue or bone to provide support for the surgical guide during implant placement. Due to tooth extraction and the loss of anatomical structures that improve the support of the surgical guide, the correct positioning of the guide can be challenging. Further, the procedure that follows to deliver the immediate temporary rehabilitation can also be challenging due to the lack of workflow integrity between the surgical and restorative phases. The present case report describes a novel digital workflow for immediate implant placement and immediate loading of a full-arch rehabilitation, which aims to improve the accuracy of implant placement surgery and simplify the procedure of delivering immediate provisional restorations. (Int J Comput Dent 2022;25(3):303-323; doi: 10.3290/j.ijcd.b3380919).

Additive Manufacturing Technologies: Current Status and Future Perspectives.

A review of the main additive manufacturing technologies including vat-polymerization, material extrusion, material jetting, binder jetting, powder-based fusion, sheet lamination, and direct energy deposition is provided. Additionally, the dental applications of polymer, metal, and ceramic printing technologies are discussed.

Tracking and recording the lip dynamics for the integration of a dynamic virtual patient: A novel dental technique.

A step-by-step technique is described for tracking and recording, in real time, the lip dynamics of a patient and integrating them into a 3D virtual patient representation using facial trackers and motion engine software programs. The main advantage of this technique is that it enables capturing of the lip movements of the patient in real time; therefore, the lip movements are not simulated from a previously recorded video of the patient using animation software programs.