A novel digital way to measure the vertical and horizontal dimensions of the supra-crestal peri-implant soft tissues.

AIM: To report on a novel digital superimposition workflow that enables measuring the supra-crestal peri-implant soft tissue dimensions all along implant treatment and afterwards. MATERIALS AND METHODS: A preoperative CBCT and intra-oral scans (IOS) are successively taken before surgery, at the end of the healing period, at prosthesis delivery, and over time; they are digitally superposed on a dedicated software. Then, the stereolithography files (STL) of the healing abutment, of the prosthetic abutment and the crown are successively merged into the superposition set of IOSs. RESULT: The workflow protocol of merging successively the STL of each item into the superposition set of IOSs enables capturing the dimensions of the height and width of the supra-crestal soft tissues, at every level of the healing abutment, the prosthetic abutment and the crown. In addition, it allows measuring the vertical distance that the crown exerts pressure on the gingiva and the thickness of the papillae at every level of the abutment. CONCLUSION: This novel digital superimposition workflow provides a straightforward method of measuring the vertical and horizontal dimensions of the supra-crestal peri-implant soft tissues, including the papillae, at each stage of the implant treatment process. It allows investigating a certain number of soft tissue variables that were previously inaccessible to clinical research. It should help enhancing our comprehension of the peri-implant soft tissue dynamics.

Soft-tissue dimensional change following guided bone regeneration on peri-implant defects using soft-type block or particulate bone substitutes: 1-year outcomes of a randomized controlled clinical trial.

AIM: To compare the peri-implant soft-tissue dimensional changes following guided bone regeneration between particulate (particle group) and collagenated soft-block-type (block group) biphasic calcium phosphate (BCP). MATERIALS AND METHODS: This study investigated 35 subjects: 18 in the particle group and 17 in the block group. Cone-beam computed tomography obtained at 6 months post surgery and optical impressions taken periodically (before surgery, 6 months post surgery, and 1 year post surgery) were superimposed. The ridge contour changes over time and the peri-implant mucosal thicknesses were measured diagonally and horizontally, and analysed statistically. RESULTS: The increases in diagonal (1.12 ± 0.78 mm) and horizontal (2.79 ± 1.90 mm) ridge contour of the block group were significantly higher than those in the particle group during the first 6 months (p < .05); however, the contour hardly changed thereafter (diagonal: 0.07 ± 0.75 mm; horizontal: -0.34 ± 1.26 mm), resulting in the 1-year contour changes similar between the two groups. Regardless of the type of BCP, the ridge contour increased significantly over 1 year when the dehiscence defect had a contained configuration (p < .05). CONCLUSIONS: The increase in soft-tissue dimensions for 1 year was similar between the two groups. The mucosal contour increase was larger when the surgery was conducted in a more contained defect, and this was not influenced by the type of BCP.

What is the impact of miniscrew-assisted rapid palatal expansion on the midfacial soft tissues? A prospective three-dimensional stereophotogrammetry study.

OBJECTIVES: To evaluate the midfacial soft tissue changes of the face in patients treated with miniscrew-assisted rapid palatal expansion (MARPE). MATERIALS AND METHODS: 3D facial images and intra-oral scans (IOS) were obtained before expansion (T0), immediately after completion of expansion (T1), and 1 year after expansion (T2). The 3D images were superimposed and two 3D distance maps were generated to measure the midfacial soft tissue changes: immediate effects between timepoints T0 and T1 and overall effects between T0 and T2. Changes of the alar width were also measured and dental expansion was measured as the interpremolar width (IPW) on IOS. RESULTS: Twenty-nine patients (22 women, 7 men, mean age 25.9 years) were enrolled. The soft tissue in the regions of the nose, left of philtrum, right of philtrum, and upper lip tubercle demonstrated a statistically significant anterior movement of 0.30 mm, 0.93 mm, 0.74 mm, and 0.81 mm, respectively (p < 0.01) immediately after expansion (T0-T1). These changes persisted as an overall effect (T0-T2). The alar width initially increased by 1.59 mm, and then decreased by 0.08 mm after 1 year, but this effect was not significant. The IPW increased by 4.58 mm and remained stable 1 year later. There was no significant correlation between the increase in IPW and alar width (r = 0.35, p = 0.06). CONCLUSIONS: Our findings indicate that MARPE results in significant but small changes of the soft tissue in the peri-oral and nasal regions. However, the clinical importance of these findings is limited. CLINICAL RELEVANCE: MARPE is an effective treatment modality to expand the maxilla, incurring only minimal and clinically insignificant changes to the midfacial soft tissues.

Intra-oral scan segmentation using deep learning.

OBJECTIVE: Intra-oral scans and gypsum cast scans (OS) are widely used in orthodontics, prosthetics, implantology, and orthognathic surgery to plan patient-specific treatments, which require teeth segmentations with high accuracy and resolution. Manual teeth segmentation, the gold standard up until now, is time-consuming, tedious, and observer-dependent. This study aims to develop an automated teeth segmentation and labeling system using deep learning. MATERIAL AND METHODS: As a reference, 1750 OS were manually segmented and labeled. A deep-learning approach based on PointCNN and 3D U-net in combination with a rule-based heuristic algorithm and a combinatorial search algorithm was trained and validated on 1400 OS. Subsequently, the trained algorithm was applied to a test set consisting of 350 OS. The intersection over union (IoU), as a measure of accuracy, was calculated to quantify the degree of similarity between the annotated ground truth and the model predictions. RESULTS: The model achieved accurate teeth segmentations with a mean IoU score of 0.915. The FDI labels of the teeth were predicted with a mean accuracy of 0.894. The optical inspection showed excellent position agreements between the automatically and manually segmented teeth components. Minor flaws were mostly seen at the edges. CONCLUSION: The proposed method forms a promising foundation for time-effective and observer-independent teeth segmentation and labeling on intra-oral scans. CLINICAL SIGNIFICANCE: Deep learning may assist clinicians in virtual treatment planning in orthodontics, prosthetics, implantology, and orthognathic surgery. The impact of using such models in clinical practice should be explored.

The effect of scanning pathways on trueness and precision in full-arch optical impression.

BACKGROUND: In this study, we investigated the effects of differences in scanning pathways during optical impression on the trueness and precision of full-arch impressions. METHODS: Reference data were obtained using a laboratory scanner. All optical impressions were measured across the dental arch using TRIOS® 3 in four different pathways. The reference and optical impression data were superimposed using the best-fit method. The criteria for superimposition were based on the starting side of the dental arch (partial arch best-fit method, PB) and based on the full arch (full arch best-fit method, FB). The data were compared between the left and right molars (starting and ending sides). The scan deviations for trueness (n = 5) and precision (n = 10) were obtained for each group by calculating the root mean square (RMS) of the deviation at each measurement point. Visual observations using superimposed color map images revealed variations in trueness. RESULTS: There were no significant differences in scanning time or amount of scan data between the four scanning pathways. Trueness did not differ significantly among the four pathways with respect to the starting and ending sides, regardless of the superimposition criteria. Precision with PB was significantly different between scanning pathways A and B, and pathways B and C for the starting sides, and between scanning pathways A and B, and pathways A and D for the ending sides. In contrast, there was no significant difference between the starting and ending side in pathways for FB. Regarding PB, color map images showed a large error range in the direction toward the molar radius for the occlusal surface and cervical regions on the ending sides. CONCLUSION: Differences in the scanning pathways did not affect trueness, regardless of the superimposition criteria. On the other hand, differences in the scanning pathways affected the precision of the starting and ending sides with PB. Scanning pathways B and D were more precise on the starting and ending sides, respectively.

Highly variable rate of orthodontic tooth movement measured by a novel 3D method correlates with gingival inflammation.

OBJECTIVES: Individual orthodontic treatment duration is hard to predict. Individual biological factors are amongst factors influencing individual rate of orthodontically induced tooth movement (OTM). The study aim is to determine the rate of OTM by a novel 3D method and investigate parameters that may predict the rate of tooth movement. MATERIALS AND METHODS: In this prospective cohort study, rate of OTM was determined from 90 three-dimensional intra-oral scans in 15 patients (aged 12-15) undergoing orthodontic treatment. For each patient, intra-oral scans were taken every week for up to 6 weeks (T0-T5). The teeth were segmented from the scans and the scans were superimposed on the palatal rugae. The rate of OTM was calculated for each tooth. Other parameters were gingival inflammation, contact-point displacement and the biological markers, matrix metalloproteinases (MMP), MMP-9 and MMP-2 in gingival crevicular fluid (GCF). RESULTS: Our study showed a high variation in the rate of OTM, varying from 0.15 to 1.24 mm/week. Teeth in the anterior segment tended to move more compared with the posterior segment. The contact point displacement and gingival inflammation varied greatly amongst the patients. The MMPs measured did not correlate with tooth movement. However, the gingival inflammation index showed a significant correlation with OTM. Future studies should include other biological markers related to bone-remodeling. CONCLUSION: This novel and efficient 3D method is suitable for measuring OTM and showed large individual variation in rate of OTM. CLINICAL RELEVANCE: Patients show different rates of OTM. The rate of OTM in an individual patient can provide guidance in timing of follow-up appointments.

Trueness and Precision Achieved With Conventional and Digital Implant Impressions: A Comparative Investigation of Stone Versus 3-D Printed Master Casts.

OBJECTIVE: To analyze implant casts obtained from intraoral optical scanning and conventional impressions. MATERIALS AND METHODS: Ten optical scans (test) and ten conventional polyether impressions (control) were obtained from a two-implant reference model. Ten casts each were manufactured additively or from stone. All casts were digitized and virtually superimposed to the digital reference (trueness) applying a best-fit algorithm, and secondary analysis for intra-group comparisons (precision). The signed nearest neighbor distance of each surface point (FDI:24/26) was computed and deviations of the three-dimensional vectors (X,Y,Z) analyzed. The groups were compared with the Wilcoxon's rank sum test. RESULTS: The printed casts had mean deviations of 106.0µm and the stone casts 187.9µm compared to the reference. Controls had significantly higher deviations and dispersion (p⟨0.001). The printed casts showed mean intra-group deviations of 149.8µm and the stone casts 181.2µm without significant differences (p=0.162). There was no statistically significant difference in any of the vector analyses (X:p=0.105, Y:p=0.089, Z:p=0.123). CONCLUSION: Optical scanning seems to be an alternative to conventional impressions in terms of trueness and precision of implant master casts, simulating the scenario of a three-unit implant-supported fixed dental prosthesis. Digitally manufactured master casts might serve as reliable reference for the final restorations.

Accuracy of digital impressions of multiple dental implants: an in vitro study.

INTRODUCTION: Studies demonstrated that the accuracy of intra-oral scanners can be compared with conventional impressions for most indications. However, little is known about their applicability to take impressions of multiple implants. AIM: The aim of this study was to evaluate the accuracy of four intra-oral scanners when applied for implant impressions in the edentulous jaw. MATERIAL AND METHODS: An acrylic mandibular cast containing six external connection implants (region 36, 34, 32, 42, 44 and 46) with PEEK scanbodies was scanned using four intra-oral scanners: the Lava C.O.S. and the 3M True Definition, Cerec Omnicam and 3Shape Trios. Each model was scanned 10 times with every intra-oral scanner. As a reference, a highly accurate laboratory scanner (104i, Imetric, Courgenay, Switzerland) was used. The scans were imported into metrology software (Geomagic Qualify 12) for analyses. Accuracy was measured in terms of trueness (comparing test and reference) and precision (determining the deviation between different test scans). Mann-Whitney U-test and Wilcoxon signed rank test were used to detect statistically significant differences in trueness and precision respectively. RESULTS: The mean trueness was 0.112 mm for Lava COS, 0.035 mm for 3M TrueDef, 0.028 mm for Trios and 0.061 mm for Cerec Omnicam. There was no statistically significant difference between 3M TrueDef and Trios (P = 0.262). Cerec Omnicam was less accurate than 3M TrueDef (P = 0.013) and Trios (P = 0.005), but more accurate compared to Lava COS (P = 0.007). Lava COS was also less accurate compared to 3M TrueDef (P = 0.005) and Trios (P = 0.005). The mean precision was 0.066 mm for Lava COS, 0.030 mm for 3M TrueDef, 0.033 mm for Trios and 0.059 mm for Cerec Omnicam. There was no statistically significant difference between 3M TrueDef and Trios (P = 0.119). Cerec Omnicam was less accurate compared to 3M TrueDef (P < 0.001) and Trios (P < 0.001), but no difference was found with Lava COS (P = 0.169). Lava COS was also less accurate compared to 3M TrueDef (P < 0.001) and Trios (P < 0.001). CONCLUSIONS: Based on the findings of this in vitro study, the 3M True Definition and Trios scanner demonstrated the highest accuracy. The Lava COS was found not suitable for taking implant impressions for a cross-arch bridge in the edentulous jaw.

Validation of automated registration of intraoral scan onto Cone Beam Computed Tomography for an efficient digital dental workflow.

OBJECTIVE: This study aimed to validate a newly developed automated method (Virtual Patient Creator, Relu, Leuven, Belgium) for multimodal registration of intraoral scan (IOS) and Cone Beam Computed Tomography (CBCT). METHODS: Time point-matched IOS and CBCT scans of forty patients with variable dental statuses (natural dentition, partial edentulism, presence of orthodontic brackets) were selected. Three operators registered IOS and CBCT scans using three state-of-the-art softwares for orthodontics and orthognathic surgery (IPS Case Designer, Proplan CMF and Dolphin Imaging). Automated registration was compared to expert-performed semi-automated registration. Time consumption, accuracy, and consistency of the proposed method were benchmarked to semi-automated registration using root mean squared error calculations. The robustness of the automated registration was evaluated in relationship to the dental status of the patients in the dataset. RESULTS: On average, automatic registration was 7.3 times faster than semi-automatic registration performed by an expert operator. Automatic registration yielded reliable results with low deviation errors compared to the differently skilled operators and semi-automated software. Automated registration surpassed human variability as expressed in intra- and inter-operator inconsistencies. Neither orthodontic brackets nor edentulism impacted registration accuracy. CONCLUSIONS: The presented automated method for IOS and CBCT registration is faster, equally accurate, and more consistent than semi-automatic registration performed by an expert or an occasional operator. With similar results among cases with different dental statuses, the clinical feasibility of the method is ensured. CLINICAL SIGNIFICANCE: A validated automated registration method provides accurate and fast multimodal image integration without incorporating operator bias at the very start of the digital workflows for dentistry, periodontics, orthodontics and orthognathic surgery.

Accurate gingival segmentation from 3D images with artificial intelligence: an animal pilot study.

BACKGROUND: Gingival phenotype plays an important role in dental diagnosis and treatment planning. Traditionally, determining the gingival phenotype is done by manual probing of the gingival soft tissues, an invasive and time-consuming procedure. This study aims to evaluate the feasibility and accuracy of an alternatively novel, non-invasive technology based on the precise 3-dimension (3D) soft tissue reconstruction from intraoral scanning and cone beam computed tomography (CBCT) to predict the gingival biotype. METHODS: As a proof-of-concept, Yorkshire pig mandibles were scanned, and the CBCT data were fed into a deep-learning model to reconstruct the teeth and surrounding bone structure in 3D. By overlaying the CBCT scan with the intraoral scans, an accurate superposition was created and used for virtual measurements of the soft tissue thickness. Meanwhile, gingival thicknesses were also measured by a periodontal probe and digital caliper on the buccal and lingual sides at 3 mm apical to the gingival margin of the posterior teeth and compared with the virtual assessment at the same location. The data obtained from virtual and clinical measurements were compared by Wilcoxon matched-pairs signed-rank analysis, while their correlation was determined by Pearson's r value. The Mann-Whitney U test was used for intergroup comparisons of the amount of difference. RESULTS: Among 108 investigated locations, the clinical and virtual measurements are strongly positively correlated (r = 0.9656, P < 0.0001), and only clinically insignificant differences (0.066 ± 0.223 mm) were observed between the two assessments. There is no difference in the agreement between the virtual and clinical measurements on sexually matured samples (0.087 ± 0.240 mm) and pre-pubertal samples (0.033 ± 0.195 mm). Noticeably, there is a greater agreement between the virtual and clinical measurements at the buccal sites (0.019 ± 0.233 mm) than at the lingual sites (0.116 ± 0.215 mm). CONCLUSION: In summary, the artificial intelligence-based virtual measurement proposed in this work provides an innovative technique potentially for accurately measuring soft tissue thickness using clinical routine 3D imaging systems, which will aid clinicians in generating a more comprehensive diagnosis with less invasive procedures and, in turn, optimize the treatment plans with more predictable outcomes.

Deep learning method for reducing metal artifacts in dental cone-beam CT using supplementary information from intra-oral scan.

Objective.Recently, dental cone-beam computed tomography (CBCT) methods have been improved to significantly reduce radiation dose while maintaining image resolution with minimal equipment cost. In low-dose CBCT environments, metallic inserts such as implants, crowns, and dental fillings cause severe artifacts, which result in a significant loss of morphological structures of teeth in reconstructed images. Such metal artifacts prevent accurate 3D bone-teeth-jaw modeling for diagnosis and treatment planning. However, the performance of existing metal artifact reduction (MAR) methods in handling the loss of the morphological structures of teeth in reconstructed CT images remains relatively limited. In this study, we developed an innovative MAR method to achieve optimal restoration of anatomical details.Approach.The proposed MAR approach is based on a two-stage deep learning-based method. In the first stage, we employ a deep learning network that utilizes intra-oral scan data as side-inputs and performs multi-task learning of auxiliary tooth segmentation. The network is designed to improve the learning ability of capturing teeth-related features effectively while mitigating metal artifacts. In the second stage, a 3D bone-teeth-jaw model is constructed with weighted thresholding, where the weighting region is determined depending on the geometry of the intra-oral scan data.Main results.The results of numerical simulations and clinical experiments are presented to demonstrate the feasibility of the proposed approach.Significance.We propose for the first time a MAR method using radiation-free intra-oral scan data as supplemental information on the tooth morphological structures of teeth, which is designed to perform accurate 3D bone-teeth-jaw modeling in low-dose CBCT environments.

Digital Impressions in Implant Dentistry: A Literature Review.

INTRODUCTION: Digital impressions in implant dentistry rely on many variables, and their accuracy, particularly in complete edentulous patients, is not well understood. Aim. The purpose of this literature review was to determine which factors may influence the accuracy of digital impressions in implant dentistry. Emphasized attention was given to the design of the intra-oral scan body (ISB) and scanning techniques. MATERIALS AND METHODS: A Medline, PubMed and EBSCO Host databases search, complemented by a hand search, was performed in order to select relevant reports regarding the appliance of digital impressions in implant dentistry. The search subject included but was not limited to accuracy of digital impressions in implant dentistry, digital scanning techniques, the design and material of the ISBs, and the depth and angulation of the implant. The related titles and abstracts were screened, and the remaining articles that fulfilled the inclusion criteria were selected for full-text readings. RESULTS: The literature search conducted for this review initially resulted in 108 articles, among which only 21 articles fulfilled the criteria for inclusion. Studies were evaluated according to five subjects: accuracy of digital impressions in implant dentistry; the design and material of the intra-oral scan bodies; scanning technique; the influence of implants depth/angulations on the digital impression and accuracy of different intra-oral scanner devices. CONCLUSIONS: The accuracy of digital impressions in implant dentistry depends on several aspects. The depth/angulation of the implant, the experience of the operator, the intra-oral scanner used, and environmental conditions may influence the accuracy of digital impressions in implant dentistry. However, it seems that ISBs' design and material, as well as scanning technique, have a major impact on the trueness and precision of digital impressions in implant dentistry. Future research is suggested for the better understanding of this subject, focusing on the optimization of the ISB design and scanning protocols.

Accuracy of Guided Implant Surgery in the Edentulous Jaw Using Desktop 3D-Printed Mucosal Supported Guides.

PURPOSE: The aim of this in vitro study is to evaluate the accuracy of implant position using mucosal supported surgical guides, produced by a desktop 3D printer. METHODS: Ninety implants (Bone Level Roxolid, 4.1 mm × 10 mm, Straumann, Villerat, Switzerland) were placed in fifteen mandibular casts (Bonemodels, Castellón de la Plana, Spain). A mucosa-supported guide was designed and printed for each of the fifteen casts. After placement of the implants, the location was assessed by scanning the cast and scan bodies with an intra-oral scanner (Primescan®, Dentsply Sirona, York, PA, USA). Two comparisons were performed: one with the mucosa as a reference, and one where only the implants were aligned. Angular, coronal and apical deviations were measured. RESULTS: The mean implant angular deviation for tissue and implant alignment were 3.25° (SD 1.69°) and 2.39° (SD 1.42°) respectively, the coronal deviation 0.82 mm (SD 0.43 mm) and 0.45 mm (SD 0.31 mm) and the apical deviation 0.99 mm (SD 0.45 mm) and 0.71 mm (SD 0.43 mm). All three variables were significantly different between the tissue and implant alignment (p < 0.001). CONCLUSION: Based on the results of this study, we conclude that guided implant surgery using desktop 3D printed mucosa-supported guides has a clinically acceptable level of accuracy. The resilience of the mucosa has a negative effect on the guide stability and increases the deviation in implant position.

A novel method for fusion of intra-oral scans and cone-beam computed tomography scans for orthognathic surgery planning.

AIM: To assess the feasibility of a new method to augment the three-dimensional virtual skull model with a detailed surface model of the dentition produced by intra-oral scanning, compared to the conventional triple scan procedure. MATERIALS & METHODS: In four patients who were planned for bimaxillary orthognathic surgery, the conventional triple scan procedure was carried out to create an augmented model. During scanning the patient wears a modified wax bite containing radio-opaque markers. An additional CBCT and laser scan of the wax bite and an intra-oral scan of the dentition were acquired. Surface-based and marker-based matching procedures were performed to integrate the intra-oral scans into the CBCT scan of the patient. The accuracy of the proposed method was assessed by measuring the distance between the occlusal surfaces of the registered intra-oral scans and the augmented model. RESULTS: Mean distances between the ios-dental cast registration and the augmented model were 0.30 mm (SD 0.20) and 0.27 mm (SD 0.20) for the upper and lower jaw, respectively. CONCLUSION: Intra-oral scans provide an accurate representation of the dental arches compared to the AlgiNot-dental casts and can be fused with CBCT scans.