Designing dental crowns with computer-aided design software in dental laboratories is complex and time-consuming. Using real clinical datasets, we developed an end-to-end deep learning model that automatically generates personalized dental crown meshes. The input context includes the prepared tooth, its adjacent teeth, and the two closest teeth in the opposing jaw. The training set contains this context, the ground truth crown, and the extracted margin line. Our model consists of two components: First, a feature extractor converts the input point cloud into a set of local feature vectors, which are then fed into a transformer-based model to predict the geometric features of the crown. Second, a point-to-mesh module generates a dense array of points with normal vectors, and a differentiable Poisson surface reconstruction method produces an accurate crown mesh. Training is conducted with three losses: (1) a customized margin line loss; (2) a contrastive-based Chamfer distance loss; and (3) a mean square error (MSE) loss to control mesh quality. We compare our method with our previously published method, Dental Mesh Completion (DMC). Extensive testing confirms our method's superiority, achieving a 12.32% reduction in Chamfer distance and a 46.43% reduction in MSE compared to DMC. Margin line loss improves Chamfer distance by 5.59%.

Carbon fiber reinforced polymer (CFRP) composites are increasingly utilized for their lightweight and superior mechanical properties. This study uses machine learning models to predict the mechanical properties of CFRP composites based on the volume fraction of carbon nanotubes (CNTs), interlayer volume fraction, glass transition temperature, and manufacturing pressure. Sixty-two samples covering nine different types of CFRPs were designed, manufactured, and experimentally tested. Three machine learning models, namely ridge regression, random forest, and support vector regression, were trained on the data and compared. The results demonstrated a high prediction accuracy for the flexural strength (R² = 0.966), flexural modulus (R² = 0.871), and the mode-II energy release rate (R² = 0.903). The study highlights the effectiveness of data-driven models in predicting key mechanical properties of CFRP composites, potentially reducing the need for extensive experimental testing and facilitating more efficient material design.

During a crown generation procedure, dental technicians depend on commercial software to generate a margin line to define the design boundary for the crown. The margin line generation remains a non-reproducible, inconsistent, and challenging procedure. In this work, we propose to generate margin line points on prepared teeth meshes using adaptive point learning inspired by the AdaPointTr model. We extracted ground truth margin lines as point clouds from the prepared teeth and crown bottom meshes. The chamfer distance (CD) and infoCD loss functions were used for training a supervised deep learning model that outputs a margin line as a point cloud. To enhance the generation results, the deep learning model was trained based on three different resolutions of the target margin lines, which were used to back-propagate the losses. Five folds were trained and an ensemble model was constructed. The training and test sets contained 913 and 134 samples, respectively, covering all teeth positions. Intraoral scanning was used to collect all samples. Our post-processing involves removing outlier points based on local point density and principal component analysis (PCA) followed by a spline prediction. Comparing our final spline predictions with the ground truth margin line using CD, we achieved a median distance of 0.137 mm. The median Hausdorff distance was 0.242 mm. We also propose a novel confidence metric for uncertainty quantification of generated margin lines during deployment. The metric was defined based on the percentage of removed outliers during the post-processing stage. The proposed end-to-end framework helps dental professionals in generating and evaluating margin lines consistently. The findings underscore the potential of deep learning to revolutionize the detection and extraction of 3D landmarks, offering personalized and robust methods to meet the increasing demands for precision and efficiency in the medical field.

Automatic and accurate dental arch segmentation is a fundamental task in computer-aided dentistry. Recent trends in digital dentistry are tackling the design of 3D crowns using artificial intelligence, which initially requires a proper semantic segmentation of teeth from intraoral scans (IOS). In practice, most IOS are partial with as few as three teeth on the scanned arch, and some of them might have preparations, missing, or incomplete teeth. Existing deep learning-based methods (e.g., MeshSegNet, DArch) were proposed for dental arch segmentation, but they are not as efficient for partial arches that include imperfections such as missing teeth and preparations. In this work, we present the ArchSeg framework that can leverage various deep learning models for semantic segmentation of perfect and imperfect dental arches. The Point Transformer V2 deep learning model is used as the backbone for the ArchSeg framework. We present experiments to demonstrate the efficiency of the proposed framework to segment arches with various types of imperfections. Using a raw dental arch scan with two labels indicating the range of present teeth in the arch (i.e., the first and the last teeth), our ArchSeg can segment a standalone dental arch or a pair of aligned master/antagonist arches with more available information (i.e., die mesh). Two generic models are trained for lower and upper arches; they achieve dice similarity coefficient scores of $0.936\pm 0.008$ and $0.948\pm 0.007$, respectively, on test sets composed of challenging imperfect arches. Our work also highlights the impact of appropriate data pre-processing and post-processing on the final segmentation performance. Our ablation study shows that the segmentation performance of the Point Transformer V2 model integrated in our framework is improved compared with the original standalone model.