A Patient Specific Biomechanical Analysis of Custom Root Analogue Implant Designs on Alveolar Bone Stress: A Finite Element Study

Objectives. The aim of this study was to analyse by means of FEA the influence of 5 custom RAI designs on stress distribution of peri-implant bone and to evaluate the impact on microdisplacement for a specific patient case. Materials and Methods. A 3D surface model of a RAI for the upper right canine was constructed from the cone beam computed tomography data of one patient. Subsequently, five (targeted) press-fit design modification FE models with five congruent bone models were designed: “Standard,” “Prism,” “Fins,” “Plug,” and “Bulbs,” respectively. Preprocessor software was applied to mesh the models. Two loads were applied: an oblique force (300 N) and a vertical force (150 N). Analysis was performed to evaluate stress distributions and deformed contact separation at the peri-implant region. Results. The lowest von Mises stress levels were numerically observed for the Plug design. The lowest levels of contact separation were measured in the Fins model followed by the Bulbs design. Conclusions. Within the limitations of the applied methodology, adding targeted press-fit geometry to the RAI standard design will have a positive effect on stress distribution, lower concentration of bone stress, and will provide a better primary stability for this patient specific case

Estimation of Alveolar Bone Loss in Periodontitis Using Machine Learning

Aim: The objective of this research was to perform a pilot study to develop an automatic analysis of periapical radiographs from patients with and without periodontitis for the percentage alveolar bone loss (ABL) on the approximal surfaces of teeth using a supervised machine learning model, that is, convolutional neural networks (CNN). Material and methods: A total of 1546 approximal sites from 54 participants on mandibular periapical radiographs were manually annotated (MA) for a training set (n = 1308 sites), a validation set (n = 98 sites), and a test set (n = 140 sites). The training and validation sets were used for the development of a CNN algorithm. The algorithm recognised the cemento-enamel junction, the most apical extent of the alveolar crest, the apex, and the surrounding alveolar bone. Results: For the total of 140 images in the test set, the CNN scored a mean of 23.1 ± 11.8 %ABL, whilst the corresponding value for MA was 27.8 ± 13.8 %ABL. The intraclass correlation (ICC) was 0.601 (P < .001), indicating moderate reliability. Further subanalyses for various tooth types and various bone loss patterns showed that ICCs remained significant, although the algorithm performed with excellent reliability for %ABL on nonmolar teeth (incisors, canines, premolars; ICC = 0.763). Conclusions: A CNN trained algorithm on radiographic images showed a diagnostic performance with moderate to good reliability to detect and quantify %ABL in periapical radiographs

Automated chart filing on panoramic radiographs using deep learning

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Non-surgical peri-implantitis treatment with or without systemic antibiotics:a randomized controlled clinical trial

OBJECTIVES: To assess the adjunctive effect of systemic amoxicillin (AMX) and metronidazole (MTZ) in patients receiving non‐surgical treatment (NST) for peri‐implantitis (PI). MATERIALS AND METHODS: Thirty‐seven patients were randomized into an experimental group treated with NST plus AMX + MTZ (N = 18) and a control group treated with NST alone (N = 19). Clinical parameters were evaluated at 12 weeks post‐treatment. The primary outcome was the change in peri‐implant pocket depth (PIPD) from baseline to 12 weeks, while secondary outcomes included bleeding on probing (BoP), suppuration on probing (SoP), and plaque. Data analysis was performed at patient level (one target site per patient). RESULTS: All 37 patients completed the study. Both groups showed a significant PIPD reduction after NST. The antibiotics group showed a higher mean reduction in PIPD at 12 weeks, compared with the control group (2.28 ± 1.49 mm vs. 1.47 ± 1.95 mm), however, this difference did not reach statistical significance. There was no significant effect of various potential confounders on PIPD reduction. Neither treatment resulted in significant improvements in BoP at follow‐up; 30 of 37 (81%) target sites still had BoP after treatment. Only two implants, one in each group, exhibited a successful outcome defined as PIPD < 5 mm, and absence of BoP and SoP. CONCLUSIONS: Non‐surgical treatment was able to reduce PIPD at implants with PI. The adjunctive use of systemic AMX and MTZ did not show statistically significant better results compared to NST alone. NST with or without antibiotics was ineffective to completely resolve inflammation around dental implants

Non-surgical peri-implantitis treatment with or without systemic antibiotics: a randomized controlled clinical trial

Objectives: To assess the adjunctive effect of systemic amoxicillin (AMX) and metronidazole (MTZ) in patients receiving non-surgical treatment (NST) for peri-implantitis (PI). Materials and methods: Thirty-seven patients were randomized into an experimental group treated with NST plus AMX + MTZ (N = 18) and a control group treated with NST alone (N = 19). Clinical parameters were evaluated at 12 weeks post-treatment. The primary outcome was the change in peri-implant pocket depth (PIPD) from baseline to 12 weeks, while secondary outcomes included bleeding on probing (BoP), suppuration on probing (SoP), and plaque. Data analysis was performed at patient level (one target site per patient). Results: All 37 patients completed the study. Both groups showed a significant PIPD reduction after NST. The antibiotics group showed a higher mean reduction in PIPD at 12 weeks, compared with the control group (2.28 ± 1.49 mm vs. 1.47 ± 1.95 mm), however, this difference did not reach statistical significance. There was no significant effect of various potential confounders on PIPD reduction. Neither treatment resulted in significant improvements in BoP at follow-up; 30 of 37 (81%) target sites still had BoP after treatment. Only two implants, one in each group, exhibited a successful outcome defined as PIPD < 5 mm, and absence of BoP and SoP. Conclusions: Non-surgical treatment was able to reduce PIPD at implants with PI. The adjunctive use of systemic AMX and MTZ did not show statistically significant better results compared to NST alone. NST with or without antibiotics was ineffective to completely resolve inflammation around dental implants

Positional assessment of lower third molar and mandibular canal using explainable artificial intelligence.

OBJECTIVE: The aim of this study is to automatically assess the positional relationship between lower third molars (M3i) and the mandibular canal (MC) based on the panoramic radiograph(s) (PR(s)). MATERIAL AND METHODS: A total of 1444 M3s were manually annotated and labeled on 863 PRs as a reference. A deep-learning approach, based on MobileNet-V2 combination with a skeletonization algorithm and a signed distance method, was trained and validated on 733 PRs with 1227 M3s to classify the positional relationship between M3i and MC into three categories. Subsequently, the trained algorithm was applied to a test set consisting of 130 PRs (217 M3s). Accuracy, precision, sensitivity, specificity, negative predictive value, and F1-score were calculated. RESULTS: The proposed method achieved a weighted accuracy of 0.951, precision of 0.943, sensitivity of 0.941, specificity of 0.800, negative predictive value of 0.865 and an F1-score of 0.938. CONCLUSION: AI-enhanced assessment of PRs can objectively, accurately, and reproducibly determine the positional relationship between M3i and MC. CLINICAL SIGNIFICANCE: The use of such an explainable AI system can assist clinicians in the intuitive positional assessment of lower third molars and mandibular canals. Further research is required to automatically assess the risk of alveolar nerve injury on panoramic radiographs

Mask-MCNet: Tooth instance segmentation in 3D point clouds of intra-oral scans

Computational dentistry uses computerized methods and mathematical models for dental image analysis. One of the fundamental problems in computational dentistry is accurate tooth instance segmentation in high-resolution mesh data of intra-oral scans (IOS). This paper presents a new computational model based on deep neural networks, called Mask-MCNet, for end-to-end learning of tooth instance segmentation in 3D point cloud data of IOS. The proposed Mask-MCNet localizes each tooth instance by predicting its 3D bounding box and simultaneously segments the points that belong to each individual tooth instance. The proposed model processes the input raw 3D point cloud in its original spatial resolution without employing a voxelization or down-sampling technique. Such a characteristic preserves the finely detailed context in data like fine curvatures in the border between adjacent teeth and leads to a highly accurate segmentation as required for clinical practice (e.g. orthodontic planning). The experiments show that the Mask-MCNet outperforms state-of-the-art models by achieving 98% Intersection over Union (IoU) score on tooth instance segmentation which is very close to human expert performance

Deep learning approach to semantic segmentation in 3D point cloud intra-oral scans of teeth

Accurate segmentation of data, derived from intra-oral scans (IOS), is a crucial step in a computer-aided design (CAD) system for many clinical tasks, such as implantology and orthodontics in modern dentistry. In order to reach the highest possible quality, a segmentation model may process a point cloud derived from an IOS in its highest available spatial resolution, especially for performing a valid analysis in finely detailed regions such as the curvatures in border lines between two teeth. In this paper, we propose an end-to-end deep learning framework for semantic segmentation of individual teeth as well as the gingiva from point clouds representing IOS. By introducing a non-uniform resampling technique, our proposed model is trained and deployed on the highest available spatial resolution where it learns the local fine details along with the global coarse structure of IOS. Furthermore, the point-wise cross-entropy loss for semantic segmentation of a point cloud is an ill-posed problem, since the relative geometrical structures between the instances (e.g. the teeth) are not formulated. By training a secondary simple network as a discriminator in an adversarial setting and penalizing unrealistic arrangements of assigned labels to the teeth on the dental arch, we improve the segmentation results considerably. Hence, a heavy post-processing stage for relational and dependency modeling (e.g. iterative energy minimization of a constructed graph) is not required anymore. Our experiments show that the proposed approach improves the performance of our baseline network and outperforms the state-of-the-art networks by achieving 0.94 IOU score

Intra-oral scan segmentation using deep learning

Abstract 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

Mask-MCNet: instance segmentation in 3D point cloud of intra-oral scans

Accurate segmentation of teeth in dental imaging is a principal element in computer-aided design (CAD) in modern dentistry. In this paper, we present a new framework based on deep learning models for segmenting tooth instances in 3D point cloud data of an intra-oral scan (IOS). At high level, the proposed framework, called Mask-MCNet, has analogy to the Mask R-CNN, which gives high performance on 2D images. However, the proposed framework is designed for the challenging task of instance segmentation of point cloud data from surface meshes. By employing the Monte Carlo Convolutional Network (MCCNet), the Mask-MCNet distributes the information from the processed 3D surface points into the entire void space (e.g. inside the objects). Consequently, the model is able to localize each object instance by predicting its 3D bounding box and simultaneously segmenting all the points inside each box. The experiments show that our Mask-MCNet outperforms state-of-the-art for IOS segmentation by achieving 98% IoU score