Robust tooth surface reconstruction by iterative deformation

Automatic adjustment of dental crowns using Laplacian mesh editing

Currently, the restoration of missing teeth by means of dental implants is a common treatment method in dentistry. Ensuring optimal contact between teeth (occlusion) when designing the occlusal surface of an implant-supported crown is crucial for the patient. Although there are various occlusal concepts and guidelines for achieving optimised occlusion, adapting an occlusal surface is challenging. The contact points must be established in certain areas of the occlusal surface without impairing the aesthetics of the teeth and the masticatory function. A computer-aided, automated modelling approach can assist in the design process and can reduce the reliance on manual labour. This study aimed to develop a modelling approach that enables the automatic adaptation of an occlusal surface to specific occlusal concepts while preserving the natural appearance. In this study, the occlusal surface of an implant-supported crown based on a scanned first right mandibular molar was adopted. Nominal contact points were determined based on occlusal concepts by Ramfjord and Ash (RA) and Thomas (T). The shape of the occlusal surface was then adapted concerning the desired contact points using Laplacian mesh editing. The modification results were validated for different forces and crown materials (3Y-TZP and PMMA) using a finite element contact analysis. The contact analysis results showed that locations with high compressive stresses correspond with the locations of the nominal contact points. The reaction forces were more evenly distributed in PMMA crowns, due to the lower Young's modulus of PMMA compared to 3Y-TZP. Furthermore, the occlusal scheme with fewer contact points (RA) showed higher maximum reaction forces per contact area. The presented method enables the automated adaptation of an (implant-supported) crown to specific occlusal schemes, proving to be valuable in dental CAD.

Generative deep learning approaches for the design of dental restorations: A narrative review

OBJECTIVES

This study aims to explore and discuss recent advancements in tooth reconstruction utilizing deep learning (DL) techniques. A review on new DL methodologies in partial and full tooth reconstruction is conducted.

DATA/SOURCES

PubMed, Google Scholar, and IEEE Xplore databases were searched for articles from 2003 to 2023.

STUDY SELECTION

The review includes 9 articles published from 2018 to 2023. The selected articles showcase novel DL approaches for tooth reconstruction, while those concentrating solely on the application or review of DL methods are excluded. The review shows that data is acquired via intraoral scans or laboratory scans of dental plaster models. Common data representations are depth maps, point clouds, and voxelized point clouds. Reconstructions focus on single teeth, using data from adjacent teeth or the entire jaw. Some articles include antagonist teeth data and features like occlusal grooves and gap distance. Primary network architectures include Generative Adversarial Networks (GANs) and Transformers. Compared to conventional digital methods, DL-based tooth reconstruction reports error rates approximately two times lower.

CONCLUSIONS

Generative DL models analyze dental datasets to reconstruct missing teeth by extracting insights into patterns and structures. Through specialized application, these models reconstruct morphologically and functionally sound dental structures, leveraging information from the existing teeth. The reported advancements facilitate the feasibility of DL-based dental crown reconstruction. Beyond GANs and Transformers with point clouds or voxels, recent studies indicate promising outcomes with diffusion-based architectures and innovative data representations like wavelets for 3D shape completion and inference problems.

CLINICAL SIGNIFICANCE

Generative network architectures employed in the analysis and reconstruction of dental structures demonstrate notable proficiency. The enhanced accuracy and efficiency of DL-based frameworks hold the potential to enhance clinical outcomes and increase patient satisfaction. The reduced reconstruction times and diminished requirement for manual intervention may lead to cost savings and improved accessibility of dental services.

Precise tooth design using deep learning-based templates

OBJECTIVES

In prosthodontic procedures, traditional computer-aided design (CAD) is often time-consuming and lacks accuracy in shape restoration. In this study, we combined implicit template and deep learning (DL) to construct a precise neural network for personalized tooth defect restoration.

METHODS

Ninety models of right maxillary central incisor (80 for training, 10 for validation) were collected. A DL model named ToothDIT was trained to establish an implicit template and a neural network capable of predicting unique identifications. In the validation stage, teeth in validation set were processed into corner, incisive, and medium defects. The defective teeth were inputted into ToothDIT to predict the unique identification, which actuated the deformation of the implicit template to generate the highly customized template (DIT) for the target tooth. Morphological restorations were executed with templates from template shape library (TSL), average tooth template (ATT), and DIT in Exocad (GmbH, Germany). RMSestimate, width, length, aspect ratio, incisal edge curvature, incisive end retraction, and guiding inclination were introduced to assess the restorative accuracy. Statistical analysis was conducted using two-way ANOVA and paired t-test for overall and detailed differences.

RESULTS

DIT displayed significantly smaller RMSestimate than TSL and ATT. In 2D detailed analysis, DIT exhibited significantly less deviations from the natural teeth compared to TSL and ATT.

CONCLUSION

The proposed DL model successfully reconstructed the morphology of anterior teeth with various degrees of defects and achieved satisfactory accuracy. This approach provides a more reliable reference for prostheses design, resulting in enhanced accuracy in morphological restoration.

CLINICAL SIGNIFICANCE

This DL model holds promise in assisting dentists and technicians in obtaining morphology templates that closely resemble the original shape of the defective teeth. These customized templates serve as a foundation for enhancing the efficiency and precision of digital restorative design for defective teeth.

A data-driven approach for the partial reconstruction of individual human molar teeth using generative deep learning

Background and objective

Due to the high prevalence of dental caries, fixed dental restorations are regularly required to restore compromised teeth or replace missing teeth while retaining function and aesthetic appearance. The fabrication of dental restorations, however, remains challenging due to the complexity of the human masticatory system as well as the unique morphology of each individual dentition. Adaptation and reworking are frequently required during the insertion of fixed dental prostheses (FDPs), which increase cost and treatment time. This article proposes a data-driven approach for the partial reconstruction of occlusal surfaces based on a data set that comprises 92 3D mesh files of full dental crown restorations.

Methods

A Generative Adversarial Network (GAN) is considered for the given task in view of its ability to represent extensive data sets in an unsupervised manner with a wide variety of applications. Having demonstrated good capabilities in terms of image quality and training stability, StyleGAN-2 has been chosen as the main network for generating the occlusal surfaces. A 2D projection method is proposed in order to generate 2D representations of the provided 3D tooth data set for integration with the StyleGAN architecture. The reconstruction capabilities of the trained network are demonstrated by means of 4 common inlay types using a Bayesian Image Reconstruction method. This involves pre-processing the data in order to extract the necessary information of the tooth preparations required for the used method as well as the modification of the initial reconstruction loss.

Results

The reconstruction process yields satisfactory visual and quantitative results for all preparations with a root mean square error (RMSE) ranging from 0.02 mm to 0.18 mm. When compared against a clinical procedure for CAD inlay fabrication, the group of dentists preferred the GAN-based restorations for 3 of the total 4 inlay geometries.

Conclusions

This article shows the effectiveness of the StyleGAN architecture with a downstream optimization process for the reconstruction of 4 different inlay geometries. The independence of the reconstruction process and the initial training of the GAN enables the application of the method for arbitrary inlay geometries without time-consuming retraining of the GAN.

Automated tooth crown design with optimized shape and biomechanics properties

Despite the large demand for dental restoration each year, the design of crown restorations is mainly performed via manual software operation, which is tedious and subjective. Moreover, the current design process lacks biomechanics optimization, leading to localized stress concentration and reduced working life. To tackle these challenges, we develop a fully automated algorithm for crown restoration based on deformable model fitting and biomechanical optimization. From a library of dental oral scans, a conditional shape model (CSM) is constructed to represent the inter-teeth shape correlation. By matching the CSM to the patient's oral scan, the optimal crown shape is estimated to coincide with the surrounding teeth. Next, the crown is seamlessly integrated into the finish line of preparation via a surface warping step. Finally, porous internal supporting structures of the crown are generated to avoid excessive localized stresses. This algorithm is validated on clinical oral scan data and achieved less than 2 mm mean surface distance as compared to the manual designs of experienced human operators. The mechanical simulation was conducted to prove that the internal supporting structures lead to uniform stress distribution all over the model.

Image generation technology for functional occlusal pits and fissures based on a conditional generative adversarial network

The occlusal surfaces of natural teeth have complex features of functional pits and fissures. These morphological features directly affect the occlusal state of the upper and lower teeth. An image generation technology for functional occlusal pits and fissures is proposed to address the lack of local detailed crown surface features in existing dental restoration methods. First, tooth depth image datasets were constructed using an orthogonal projection method. Second, the optimization and improvement of the model parameters were guided by introducing the jaw position spatial constraint, the L1 loss and the perceptual loss functions. Finally, two image quality evaluation metrics were applied to evaluate the quality of the generated images, and deform the dental crown by using the generated occlusal pits and fissures as constraints to compare with expert data. The results showed that the images generated using the network constructed in this study had high quality, and the detailed pit and fissure features on the crown were effectively restored, with a standard deviation of 0.1802mm compared to the expert-designed tooth crown models.

3D reconstruction for maxillary anterior tooth crown based on shape and pose estimation networks

PURPOSE

The design of a maxillary anterior tooth crown is crucial to post-treatment aesthetic appearance. Currently, the design is performed manually or by semi-automatic methods, both of which are time-consuming. As such, automatic methods could improve efficiency, but existing automatic methods ignore the relationships among crowns and are primarily used for occlusal surface reconstruction. In this study, the authors propose a novel method for automatically reconstructing a three-dimensional model of the maxillary anterior tooth crown.

METHOD

A pose estimation network (PEN) and a shape estimation network (SEN) are developed for jointly estimating the crown point cloud. PEN is a regression network used for estimating the crown pose, and SEN is based on an encoder-decoder architecture and used for estimating the initial crown point cloud. First, SEN adopts a transformer encoder to calculate the shape relationship among crowns to ensure that the shape of the reconstructed point cloud is precise. Second, the initial point cloud is subjected to pose transformation according to the estimated pose. Finally, the iterative method is used to form the crown mesh model based on the point cloud.

RESULT

The proposed method is evaluated on a dataset with 600 cases. Both SEN and PEN are converged within 1000 epochs. The average deviation between the reconstructed point cloud and the ground truth of the point cloud is 0.22 mm. The average deviation between the reconstructed crown mesh model and the ground truth of the crown model is 0.13 mm.

CONCLUSION

The results show that the proposed method can automatically and accurately reconstruct the three-dimensional model of the missing maxillary anterior tooth crown, which indicates the method has promising application prospects. Furthermore, the reconstruction time takes less than 11 s for one case, demonstrating improved work efficiency.

DCPR-GAN: Dental Crown Prosthesis Restoration Using Two-stage Generative Adversarial Networks

Restoring the correct masticatory function of broken teeth is the basis of dental crown prosthesis rehabilitation. However, it is a challenging task primarily due to the complex and personalized morphology of the occlusal surface. In this article, we address this problem by designing a new two-stage generative adversarial network (GAN) to reconstruct a dental crown surface in the data-driven perspective. Specifically, in the first stage, a conditional GAN (CGAN) is designed to learn the inherent relationship between the defective tooth and the target crown, which can solve the problem of the occlusal relationship restoration. In the second stage, an improved CGAN is further devised by considering an occlusal groove parsing network (GroNet) and an occlusal fingerprint constraint to enforce the generator to enrich the functional characteristics of the occlusal surface. Experimental results demonstrate that the proposed framework significantly outperforms the state-of-the-art deep learning methods in functional occlusal surface reconstruction using a real-world patient database. Moreover, the standard deviation (SD) and root mean square (RMS) between the generated occlusal surface and the target crown calculated by our method are both less than 0.161mm. Importantly, the designed dental crown has enough anatomical morphology and higher clinical applicability.

Efficient Computer-Aided Design of Dental Inlay Restoration: A Deep Adversarial Framework

Restoring the normal masticatory function of broken teeth is a challenging task primarily due to the defect location and size of a patient's teeth. In recent years, although some representative image-to-image transformation methods (e.g. Pix2Pix) can be potentially applicable to restore the missing crown surface, most of them fail to generate dental inlay surface with realistic crown details (e.g. occlusal groove) that are critical to the restoration of defective teeth with varying shapes. In this article, we design a computer-aided Deep Adversarial-driven dental Inlay reStoration (DAIS) framework to automatically reconstruct a realistic surface for a defective tooth. Specifically, DAIS consists of a Wasserstein generative adversarial network (WGAN) with a specially designed loss measurement, and a new local-global discriminator mechanism. The local discriminator focuses on missing regions to ensure the local consistency of a generated occlusal surface, while the global discriminator aims at defective teeth and adjacent teeth to assess if it is coherent as a whole. Experimental results demonstrate that DAIS is highly efficient to deal with a large area of missing teeth in arbitrary shapes and generate realistic occlusal surface completion. Moreover, the designed watertight inlay prostheses have enough anatomical morphology, thus providing higher clinical applicability compared with more state-of-the-art methods.

Three Dimensional Reconstruction Models for Medical Modalities: A Comprehensive Investigation and Analysis

BACKGROUND

Image reconstruction is the mathematical process which converts the signals obtained from the scanning machine into an image. The reconstructed image plays a fundamental role in the planning of surgery and research in the medical field.

DISCUSSION

This paper introduces the first comprehensive survey of the literature about medical image reconstruction related to diseases, presenting a categorical study about the techniques and analyzing advantages and disadvantages of each technique. The images obtained by various imaging modalities like MRI, CT, CTA, Stereo radiography and Light field microscopy are included. A comparison on the basis of the reconstruction technique, Imaging Modality and Visualization, Disease, Metrics for 3D reconstruction accuracy, Dataset and Execution time, Evaluation of the technique is also performed.

CONCLUSION

The survey makes an assessment of the suitable reconstruction technique for an organ, draws general conclusions and discusses the future directions.

Personalized design technique for the dental occlusal surface based on conditional generative adversarial networks

The tooth defect is a frequently occurring disease within the field of dental clinic. However, the traditional manual restoration for the defective tooth needs an especially long treatment time, and dental computer aided design and manufacture (CAD/CAM) systems fail to restore the personalized anatomical features of natural teeth. Aiming to address the shortcomings of existed methods, this article proposes an intelligent network model for designing tooth crown surface based on conditional generative adversarial networks. Then, the data set for training the network model is constructed via generating depth maps of 3D tooth models scanned by the intraoral. Through adversarial training, the network model is able to generate tooth occlusal surface under the constraint of the space occlusal relationship, the perceptual loss, and occlusal groove filter loss. Finally, we carry out the assessment experiments for the quality of the occlusal surface and the occlusal relationship with the opposing tooth. The experimental results demonstrate that our method can automatically reconstruct the personalized anatomical features on occlusal surface and shorten the treatment time while restoring the full functionality of the defective tooth.