Development of intra-oral automated landmark recognition (ALR) for dental and occlusal outcome measurements

BACKGROUND: Previous studies embracing digital technology and automated methods of scoring dental arch relationships have shown that such technology is valid and accurate. To date, however there is no published literature on artificial intelligence and machine learning to completely automate the process of dental landmark recognition. OBJECTIVES: This study aimed to develop and evaluate a fully automated system and software tool for the identification of landmarks on human teeth using geometric computing, image segmenting, and machine learning technology. METHODS: Two hundred and thirty-nine digital models were used in the automated landmark recognition (ALR) validation phase, 161 of which were digital models from cleft palate subjects aged 5 years. These were manually annotated to facilitate qualitative validation. Additionally, landmarks were placed on 20 adult digital models manually by 3 independent observers. The same models were subjected to scoring using the ALR software and the differences (in mm) were calculated. All the teeth from the 239 models were evaluated for correct recognition by the ALR with a breakdown to find which stages of the process caused the errors. RESULTS: The results revealed that 1526 out of 1915 teeth (79.7%) were correctly identified, and the accuracy validation gave 95% confidence intervals for the geometric mean error of [0.285, 0.317] for the humans and [0.269, 0.325] for ALR—a negligible difference. CONCLUSIONS/IMPLICATIONS: It is anticipated that ALR software tool will have applications throughout clinical dentistry and anthropology, and in research will constitute an accurate and objective tool for handling large datasets without the need for time intensive employment of experts to place landmarks manually

Automatic 3D tooth segmentation using convolutional neural networks in harmonic parameter space

Automatic segmentation of 3D tooth models into individual teeth is an important step in orthodontic CAD systems. 3D tooth segmentation is a mesh instance segmentation task. Complex geometric features on the surface of 3D tooth models often lead to failure of tooth boundary detection, so it is difficult to achieve automatic and accurate segmentation by traditional mesh segmentation methods. We propose a novel solution to address this problem. We map a 3D tooth model isomorphically to a 2D harmonic parameter space and convert it into an image. This allows us to use a CNN to learn a highly robust image segmentation model to achieve automated and accurate segmentation of 3D tooth models. Finally, we map the image segmentation mask back to the 3D tooth model and refine the segmentation result using an improved Fuzzy Clustering-and-Cuts algorithm. Our method has been incorporated into an orthodontic CAD system, and performs well in practice

3D mandibular superimposition: Comparison of regions of reference for voxel-based registration

The aim was to evaluate three regions of reference (Björk, Modified Björk and mandibular Body) for mandibular registration testing them in a patients' CBCT sample.Mandibular 3D volumetric label maps were built from CBCTs taken before (T1) and after treatment (T2) in a sample of 16 growing subjects and labeled with eight landmarks. Registrations of T1 and T2 images relative to the different regions of reference were performed, and 3D surface models were generated. Seven mandibular dimensions were measured separately for each time-point (T1 and T2) in relation to a stable reference structure (lingual cortical of symphysis), and the T2-T1 differences were calculated. These differences were compared to differences measured between the superimposed T2 (generated from different regions of reference: Björk, Modified Björk and Mandibular Body) over T1 surface models. ICC and the Bland-Altman method tested the agreement of the changes obtained by nonsuperimposition measurements from the patients' sample, and changes between the overlapped surfaces after registration using the different regions of reference.The Björk region of reference (or mask) did work properly only in 2 of 16 patients. Evaluating the two other masks (Modified Björk and Mandibular body) on patients' scans registration, the concordance and agreement of the changes obtained from superimpositions (registered T2 over T1) compared to results obtained from non superimposed T1 and T2 separately, indicated that Mandibular Body mask displayed more consistent results.The mandibular body mask (mandible without teeth, alveolar bone, rami and condyles) is a reliable reference for 3D regional registration

Biomechanical function of the periodontal ligament in biting and orthodontic tooth movement

Alveolar bone remodelling is vital for the success of dental implants and orthodontic treatments. However, the underlying biomechanical mechanisms, in particular the function of the periodontal ligament (PDL) in bone remodelling, are not well understood. The PDL is a soft fibrous connective tissue that joins the tooth root to the alveolar bone and plays a critical role in the transmission of loads from the teeth to the surrounding bone. However, due to its complex structure, small size and location within the tooth socket it is difficult to study in vivo. Finite element analysis (FEA) is an ideal tool with which to investigate the role of the PDL, but inclusion of the PDL in FE models is complex and time consuming and most FE models that include teeth do not consider the PDL. The aim of this study was to investigate the effects of including the PDL and its fibrous structure in mandibular finite element models.This research involved the development of a novel method to include the fibres of the PDL in FE models. A simplified single tooth model was developed to assess the effects of modelling fibrous PDL compared to the traditional approach of representing the PDL as a simple layer of solid material and to an absent PDL. The same study design was then applied to a high-resolution model of the human molar region, which is the first time that the fibrous structure of the PDL has been included in a model with realistic tooth and bone geometry. Finally, molar region models of five additional species (cat, cercocebus, pig, rabbit and sheep) were tested with and without a PDL.The results from the research showed that omission of the PDL creates a more rigid model, reducing the strains observed in the mandibular corpus for all six species studied. This suggests that the results obtained are not specific to the human molar region, but may be true for the mammalian mandible in general. Compared to a solid PDL, the fibrous PDL altered the strains in the models, in particular increasing the strains observed in the tooth socket. This may be important for the management of orthodontic treatment, as strains in this region are thought to play an important role in bone remodelling during orthodontic tooth movement

Designing of Customized Devices in Orthodontics by Digital Imaging and CAD/FEM Modeling

In current decade, patient’s demand of minimal orthodontic treatment have encouraged the introduction of appliance that will be lighter, improved profile and better esthetics with regard to conventional orthodontic treatment. Considering aesthetic treatment options, removable clear aligner treatment got popular among patients since it allows clinician to deliver comprehensive orthodontic treatment while maintaining comfort of patient. The aligners should exert an adequate force in order to shift the tooth to a desirable position. But, in recent times, the relation of applied force and aligner property (eg. thickness) is inadequately witnessed. This article focuses on a patient-focused framework has been formulated which depicts orthodontic movement of teeth with the help of aligners. Particularly, a finite element model is being formulated which optimizes the thickness of these aligners with respect to amount of force and moment system applied to a lower central incisor while tipping it bucco-lingually

Computational design and engineering of polymeric orthodontic aligners

Transparent and removable aligners represent an effective solution to correct various orthodontic malocclusions through minimally invasive procedures. An aligner-based treatment requires patients to sequentially wear dentition-mating shells obtained by thermoforming polymeric disks on reference dental models. An aligner is shaped introducing a geometrical mismatch with respect to the actual tooth positions to induce a loading system, which moves the target teeth toward the correct positions. The common practice is based on selecting the aligner features (material, thickness, and auxiliary elements) by only considering clinician's subjective assessments. In this article, a computational design and engineering methodology has been developed to reconstruct anatomical tissues, to model parametric aligner shapes, to simulate orthodontic movements, and to enhance the aligner design. The proposed approach integrates computer-aided technologies, from tomographic imaging to optical scanning, from parametric modeling to finite element analyses, within a 3-dimensional digital framework. The anatomical modeling provides anatomies, including teeth (roots and crowns), jaw bones, and periodontal ligaments, which are the references for the down streaming parametric aligner shaping. The biomechanical interactions between anatomical models and aligner geometries are virtually reproduced using a finite element analysis software. The methodology allows numerical simulations of patient-specific conditions and the comparative analyses of different aligner configurations. In this article, the digital framework has been used to study the influence of various auxiliary elements on the loading system delivered to a maxillary and a mandibular central incisor during an orthodontic tipping movement. Numerical simulations have shown a high dependency of the orthodontic tooth movement on the auxiliary element configuration, which should then be accurately selected to maximize the aligner's effectiveness

3D Morphometric Quantification of Maxillae and Palatal Defects for Patients with Unilateral Cleft Lip and Palate via Auto-segmentation

The accurate quantification of the complex 3D cleft defect structure is key for optimal treatment planning and patient outcomes. Furthermore, very little is known about the morphometric differences between the affected versus the unaffected maxillary halves. The aim of this study is to characterize the 3D morphometry of the maxillae and cleft defects in non-syndromic patients with unilateral cleft lip and palate. To test the hypothesis that the defect size is positively correlated with the affected maxillary half, CBCT images were acquired from 60 patients presenting with unilateral cleft lip and palate. The machine learning program LINKS was used to segment the maxilla and defect. The height, width, and length of the maxilla and defect were measured from the segmented images. To fully characterize the defect, the distribution probability was mapped from superimposed 3D models, paired t tests were performed for statistical analysis, and a multiple linear regression was completed. The defect side demonstrated a significant decrease in maxillary length, anterior width, and volume with mean measurements of 34.31±2.56mm, 17.83±2.06mm, and 18.02±3.24x103mm3, respectively, and an increased maxillary anterior height with a mean of 25.91±4.12mm as compared to the non-defect side. Defect superimposition displayed a concentrated distribution near the alveolar bone region and anterior maxillary structures appeared to contribute to defect variability.Master of Scienc

Craniofacial Growth Series Volume 56

https://deepblue.lib.umich.edu/bitstream/2027.42/153991/1/56th volume CF growth series FINAL 02262020.pdfDescription of 56th volume CF growth series FINAL 02262020.pdf : Proceedings of the 46th Annual Moyers Symposium and 44th Moyers Presymposiu

ImplantFormer: Vision Transformer based Implant Position Regression Using Dental CBCT Data

Implant prosthesis is the most appropriate treatment for dentition defect or dentition loss, which usually involves a surgical guide design process to decide the implant position. However, such design heavily relies on the subjective experiences of dentists. In this paper, a transformer-based Implant Position Regression Network, ImplantFormer, is proposed to automatically predict the implant position based on the oral CBCT data. We creatively propose to predict the implant position using the 2D axial view of the tooth crown area and fit a centerline of the implant to obtain the actual implant position at the tooth root. Convolutional stem and decoder are designed to coarsely extract image features before the operation of patch embedding and integrate multi-level feature maps for robust prediction, respectively. As both long-range relationship and local features are involved, our approach can better represent global information and achieves better location performance. Extensive experiments on a dental implant dataset through five-fold cross-validation demonstrated that the proposed ImplantFormer achieves superior performance than existing methods