Accuracy assessment (trueness and precision) of a confocal based intraoral scanner under twelve different ambient lighting conditions.

OBJECTIVES: The ambient lighting condition has been identified as an important factor that influences the accuracy of intraoral scanners (IOSs). The purpose of this study was to evaluate the influence of 12 different ambient lighting conditions on the accuracy of a confocal based IOS (PrimeScan). MATERIALS AND METHODS: A typodont was digitized using a laboratory scanner (L2i) to obtain a reference standard tessellation language (STLr) file. A restorative dentist recorded the scans using an IOS (PrimeScan) under 12 different ambient lighting conditions where the luminosity was measured using a light meter (LX1330B Light Meter). Twelve groups were created, namely 0-, 500-, 1000-, 2000-, 3000-, 4000-, 5000-, 6000-, 7000-, 8000-, 9000-, and 10 000 lux groups. Ten STL files were recorded per group. The STLr file was used as a reference with which to compare the distortion of the 120 STL files obtained using a software program (Meshlab). The normality Shapiro-Wilk test indicated that the distributions were not normal. Therefore, the nonparametric Kruskal-Wallis and pairwise multicomparison tests were used to analyze the data (α = 0.05). RESULTS: The group with the 1000 lux lighting condition obtained the smallest median ±interquartile range (IQR) with scanning distortion values of 69.5 ± 97.4 µm, followed by the 8000 lux group with a median ±IQR of 166.5 ± 318.1 µm. The 0 lx group presented the highest distortion values with a mean ±IQR of 355.5 ± 488.0 µm (p < 0.05). CONCLUSIONS: Ambient lighting conditions influenced the accuracy of the IOS tested. The highest accuracy values were obtained with 1000 lux. The lowest scanning accuracy was obtained with 0 lux.

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.

An objective assessment for bone drilling: A pilot study on vertical drilling.

The purpose of this study is to propose a quantitative assessment scheme to help with surgical bone drilling training. This pilot study gathered and compared motion and force data from expert surgeons (n = 3) and novice residents (n = 6). The experiment used three-dimensional printed bone simulants of young bone (YB) and osteoporotic bone (OB), and drilling overshoot, time, and force were measured. There was no statistically significant difference in overshoot between the two groups (p = 0.217 for YB and 0.215 for OB). The results, however, show that the experts took less time (mean = 4.01 s) than the novices (mean = 9.98 s), with a statistical difference (p = 0.003 for YB and 0.0001 for OB). In addition, the expert group performed more consistently than the novices. The force analysis further revealed that experts used a higher force to drill the first cortical section and a noticeably lower force in the second cortex to control the overshoot (approximate reduction of 5.5 N). Finally, when drilling time and overshoot distance were combined, the motion data distinguished the skill gap between expert and novice drilling; the force data provided insight into the drilling mechanism and performance outcomes. This study lays the groundwork for a data-driven training scheme to prepare novice residents for clinical practice.

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.

A learning-based, region of interest-tracking algorithm for catheter detection in echocardiography.

Echocardiography (echo) is gaining popularity to guide the catheter during surgical procedures. However, it is difficult to discern the catheter tip in echo even with an acoustically active catheter. An acoustically active catheter is detected for the first time in cardiac echo images using two methods. First, a convolutional neural network (CNN) model was trained to detect the region of interest (ROI), the interior of the left ventricle, containing the catheter tip. Color intensity difference detection technique was implemented on the ROI to detect the catheter. This method succeeded in detecting the catheter without any manual input on 94% and 57% of long- and short-axis projections, respectively. Second, several tracking methods were implemented and tested. Given the manually identified initial positions of the catheter, the tracking methods could distinguish between the target (catheter tip) and the surrounding on the rest of the frames. Combining the two techniques, for the first time, resulted in an automatic, robust, and fast method for catheter detection in echo images.

Geometrically Interlocking Space-Filling Tiling Based on Fabric Weaves.

In this article, we introduce a framework for the geometric design and fabrication of a family of geometrically interlocking space-filling shapes, which we call woven tiles. Our framework is based on a unique combination of (1) Voronoi partitioning of space using curve segments as the Voronoi sites and (2) the design of these curve segments based on weave patterns closed under symmetry operations. The underlying weave geometry provides an interlocking property to the tiles and the closure property under symmetry operations ensure single tile can fill space. In order to demonstrate this general framework, we focus on specific symmetry operations induced by fabric weaving patterns. We specifically showcase the design and fabrication of woven tiles on flat and curved domains by using the most common 2-fold fabrics, namely, plain, twill, and satin weaves. We further evaluate and compare the mechanical behavior of the so created woven tiles through finite element analysis.

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.

Automatic segmentation of the left ventricle in echocardiographic images using convolutional neural networks.

BACKGROUND: Two-dimensional echocardiography (2D echo) is the most widely used non-invasive imaging modality due to its fast acquisition time, low cost, and high temporal resolution. Boundary identification of left ventricle (LV) in 2D echo, i.e., image segmentation, is the first step to calculate relevant clinical parameters. Currently, LV segmentation in 2D echo is primarily conducted semi-manually. A fully-automatic segmentation of the LV wall needs further development. METHODS: We evaluated the performance of the state-of-the-art convolutional neural networks (CNNs) for the segmentation of 2D echo images from 6 standard projections of the LV. We used two segmentation algorithms: U-net and segAN. The models were trained using an in-house dataset, which consists of 1,649 porcine images from 6 to 8 different pigs. In addition, a transfer learning approach was used for the segmentation of long-axis projections by training models with our database based on the previously trained weights obtained from Cardiac Acquisitions for Multi-structure Ultrasound Segmentation (CAMUS) dataset. The models were tested on a separate set of images from two other pigs by computing several metrics. The segmentation process was combined with a 3D reconstruction framework to quantify the physiological indices such as LV volumes and ejection fraction (EF). RESULTS: The average dice metric for the LV cavity was 0.90 and 0.91 for the U-net and segAN, respectively, which was higher than 0.82 for the level-set (P value: 3.31×10-25). The average Hausdorff distance for the LV cavity was 2.71 mm and 2.82 mm for the U-net and segAN, respectively, which was lower than 3.64 mm for the level-set (P value: 4.86×10-16). The LV shapes and volumes obtained using the CNN segmentation models were in good agreement with the results segmented by the experts. In addition, the differences of the calculated physiological parameters between two 3D reconstruction models segmented by the experts and CNNs were less than 15%. CONCLUSIONS: The results showed that both CNN models achieve higher performance on LV segmentation than the level-set method. The error of the reconstruction from automatic segmentation compared to the expert segmentation is less than 15%, which is within the 20% error of echo compared to the gold standard.

3D printing parameters, supporting structures, slicing, and post-processing procedures of vat-polymerization additive manufacturing technologies: A narrative review.

OBJECTIVE: To review the elements of the vat-polymerization workflow, including the 3D printing parameters, support structures, slicing, and post-processing procedures, as well as how these elements affect the characteristics of the manufactured dental devices. DATA: Collection of published articles related to vat-polymerization technologies including manufacturing workflow description, and printing parameters definition and evaluation of its influence on the mechanical properties of vat-polymerized dental devices was performed. SOURCES: Three search engines were selected namely Medline/PubMed, EBSCO, and Cochrane. A manual search was also conducted. STUDY SELECTION: The selection of the optimal printing and supporting parameters, slicing, and post-processing procedures based on dental application is in continuous improvement. As well as their influence on the characteristics of the additively manufactured (AM) devices such as surface roughness, printing accuracy, and mechanical properties of the dental device. RESULTS: The accuracy and properties of the AM dental devices are influenced by the technology, printer, and material selected. The printing parameters, printing structures, slicing methods, and the post-processing techniques significantly influence on the surface roughness, printing accuracy, and mechanical properties of the manufactured dental device; yet, the optimization of each one may vary depending on the clinical application of the additively manufactured device. CONCLUSIONS: The printing parameters, supporting structures, slicing, and post-processing procedures have been identified, but additional studies are needed to establish the optimal manufacturing protocol and enhance the properties of the AM polymer dental devices. CLINICAL SIGNIFICANCE: The understanding of the factors involved in the additive manufacturing workflow leads to a printing success and better outcome of the additively manufactured dental device.

Analysis of Different Illuminance of the Room Lighting Condition on the Accuracy (Trueness and Precision) of An Intraoral Scanner.

PURPOSE: To measure the influence of illuminance of the ambient light between 1000 lux (room light) and 10 000 lux (chair light) on the accuracy of an intraoral scanner (IOS). MATERIAL AND METHODS: A typodont was digitized using an extraoral scanner to obtain a reference standard tessellation language (STL) file. Ten groups were created based on the different illuminance of the ambient light conditions tested starting from 1000 lux (no chair light) to 10000 lux (chair light) in increments of 1000 lux by increasing the distance between the chair light and the mannequin, with the room light turned on. Ten digital scans per group were obtained (n = 10) using an IOS (Trios 3; 3Shape). The accuracy of the digital scans was evaluated with respect to the reference mesh of the typodont using a 3D mesh processing software. Kruskal-Wallis and pair-wise comparison tests were used to analyze the data (α = 0.05). RESULTS: Significant difference for trueness and precision values were found among the groups (p < 0.001). The 1000-lux group exhibited the lowest discrepancy values with a median of 26.33 µm and an interquartile range (IQR) of 40.04 µm (11.97-52.00) (p < 0.001); while the 5000-lux group obtained the highest discrepancy values with a median of 46.38 µm and an IQR of 99.94 µm (19.05-118.98) (p < 0.001). The pair-wise multi-comparison showed no difference between the 8000- and 4000-lux groups (p = 0.287). In all groups, the IQR was higher than the mean errors from the control mesh, suggesting that the relative precision was low. CONCLUSIONS: A 1000-lux illumination lighting condition is recommended to maximize the scanning accuracy of the IOS tested; the chair light should be avoided. Furthermore, the scanning accuracy response under the illuminance range tested presented a lack of monotonicity.

Clinical Study of the Influence of Ambient Lighting Conditions on the Mesh Quality of an Intraoral Scanner.

PURPOSE: To compute the effect of ambient light illuminance settings on the mesh quality of the digital scans accomplished in a subject. MATERIAL AND METHODS: A subject was recruited. The maxillary dentition did not present any dental restoration. A prosthodontist recorded different complete-arch maxillary digital scans by using an IOS (TRIOS 3; 3Shape) under 4 different illuminance light conditions namely chair light at 10,000-lux illuminance (CL group), room light at 1000-lux illuminance (RL group), natural light at 500-lux illuminance (NL group), and no light at 0-lux luminosity (ZL group). Ten digital scans for each group were consecutively obtained. Mesh quality was examined using the iso2mesh MATLAB package. Shapiro-Wilk test revealed a nonnormally distributed data. Kruskal-Wallis one-way ANOVA, and pair-wise comparison were selected to evaluate the data (α = 0.05). RESULTS: Significant differences in mesh quality values were measured among the groups (p < 0.001). Pair-wise comparisons revealed that significant difference was found across all pairs of lighting groups, except for the RL-NL comparison (p = 0.279). However, the CL condition obtained the highest mean values, followed by RL and NL groups, and the lowest mean values were obtained on the ZL lighting condition. CONCLUSIONS: Chair light at 10,000-lux illuminance condition is recommended to maximize the quality mesh values of the IOS system tested (TRIOS 3; 3Shape).

Clinical Study of the Influence of Ambient Light Scanning Conditions on the Accuracy (Trueness and Precision) of an Intraoral Scanner.

PURPOSE: To quantify the impact of ambient lighting conditions on the accuracy (trueness and precision) of an intraoral scanner (IOS) when maxillary complete-arch and maxillary right quadrant digital scans were performed in a patient. MATERIAL AND METHODS: One complete dentate patient was selected. A complete maxillary arch vinyl polysiloxane impression was obtained and poured using Type IV dental stone. The working cast was digitized using a laboratory scanner (E4 Dental Scanner; 3Shape) and the reference standard tessellation language (STL file) was obtained. Two groups were created based on the extension of the maxillary digital scans performed namely complete-arch (CA group) and right quadrant (RQ) groups. The CA and RQ digital scans of the patient were performed using an IOS (TRIOS 3; 3Shape) with 4 lighting conditions chair light (CL), 10 000 lux, room light (RL), 1003 lux, natural light (NL), 500 lux, and no light (ZL), 0 lux. Ten digital scans per group at each ambient light settings (CL, RL, NL, and ZL) were consecutively obtained (n = 10). The STLR file was used to analyze the discrepancy between the digitized working cast and digital scans using MeshLab software. Kruskal-Wallis, one-way ANOVA, and pair-wise comparison were used to analyze the data. RESULTS: Significant difference in the trueness and precision values were found across different lighting conditions where RL condition obtained the lowest absolute error compared with the other lighting conditions tested followed by CL, NL, and ZL. On the CA group, RL condition also obtained the best accuracy values, CL and NL conditions performed closely and under ZL condition the mean error presented the highest values. On the RQ group, CL condition presented the lowest absolute error when compared with the other lighting conditions evaluated. A pair-wise multicomparison showed no significant difference between NL and ZL conditions. In all groups, the standard deviation was higher than the mean errors from the control mesh, indicating that the relative precision was low. CONCLUSIONS: Light conditions significantly influenced on the scanning accuracy of the IOS evaluated. RL condition obtained the lowest absolute error value of the digital scans performed. The extension of the digital scan was a scanning accuracy influencing factor. The higher the extension of the digital scan performed, the lower the accuracy values obtained. Furthermore, ambient light scanning conditions influenced differently depending on the extension of the digital scans made.

Intraoral digital scans: Part 2-influence of ambient scanning light conditions on the mesh quality of different intraoral scanners.

STATEMENT OF PROBLEM: Digital scans should be able to accurately reproduce the different complex geometries of the patient's mouth. Mesh quality of the digitized mouth is an important factor that influences the capabilities of the geometry reproduction of an intraoral scanner (IOS). However, the mesh quality capabilities of IOSs and the relationship with different ambient light scanning conditions are unclear. PURPOSE: The purpose of this in vitro study was to measure the impact of various light conditions on the mesh quality of different IOSs. MATERIAL AND METHODS: Three IOSs were evaluated-iTero Element, CEREC Omnicam, and TRIOS 3-with 4 lighting conditions-chair light, 10 000 lux; room light, 1003 lux; natural light, 500 lux; and no light, 0 lux. Ten digital scans per group were made of a mandibular typodont. The mesh quality of digital scans was analyzed by using the iso2mesh MATLAB package. Two-way ANOVA and Kruskal-Wallis 1-way ANOVA statistical tests were used to analyze the data (á=.05). RESULTS: Significant differences in mesh quality values were found among the different IOSs under the same lighting conditions and among the different lighting conditions using the same IOS. TRIOS 3 showed the highest consistency and mesh quality mean values across all scanning lighting conditions tested. CEREC Omnicam had the lowest mean mesh quality values across all scanning lighting conditions. iTero Element displayed some consistency in the mesh quality values depending on the scanning lighting conditions: chair light and room light conditions presented good consistency in mesh quality, indicating better mesh quality, and natural light and no light conditions displayed differing consistency in mesh quality values. Nevertheless, no light condition led to the minimal mean mesh quality across all IOS groups. CONCLUSIONS: Differences in the mesh quality between different IOSs should be expected. The photographic scanning techniques evaluated presented higher mesh quality mean values than the video-based scanning technology tested. Moreover, changes in lighting condition significantly affect mesh quality. TRIOS 3 showed the highest consistency in terms of the mean mesh quality, indicating better photographic system in comparison with iTero Element.

Intraoral digital scans-Part 1: Influence of ambient scanning light conditions on the accuracy (trueness and precision) of different intraoral scanners.

STATEMENT OF PROBLEM: Digital scans have increasingly become an alternative to conventional impressions. Although previous studies have analyzed the accuracy of the available intraoral scanners (IOSs), the effect of the light scanning conditions on the accuracy of those IOS systems remains unclear. PURPOSE: The purpose of this in vitro study was to measure the impact of lighting conditions on the accuracy (trueness and precision) of different IOSs. MATERIAL AND METHODS: A typodont was digitized by using an extraoral scanner (L2i; Imetric) to obtain a reference standard tessellation language (STL) file. Three IOSs were evaluated-iTero Element, CEREC Omnicam, and TRIOS 3-with 4 lighting conditions-chair light 10 000 lux, room light 1003 lux, natural light 500 lux, and no light 0 lux. Ten digital scans per group were recorded. The STL file was used as a reference to measure the discrepancy between the digitized typodont and digital scans by using the MeshLab software program. The Kruskal-Wallis, 1-way ANOVA, and pairwise comparison were used to analyze the data. RESULTS: Significant differences for trueness and precision mean values were observed across different IOSs tested with the same lighting conditions and across different lighting conditions for a given IOS. In all groups, precision mean values were higher than their trueness values, indicating low relative precision. CONCLUSIONS: Ambient lighting conditions influenced the accuracy (trueness and precision) of the IOSs tested. The recommended lighting conditions depend on the IOS selected. For iTero Element, chair and room light conditions resulted in better accuracy mean values. For CEREC Omnicam, zero light resulted in better accuracy, and for TRIOS 3, room light resulted in better accuracy.