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

Personalized dental crown design: A point-to-mesh completion network

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%.

AI-driven 3D bioprinting for regenerative medicine: From bench to bedside

In recent decades, 3D bioprinting has garnered significant research attention due to its ability to manipulate biomaterials and cells to create complex structures precisely. However, due to technological and cost constraints, the clinical translation of 3D bioprinted products (BPPs) from bench to bedside has been hindered by challenges in terms of personalization of design and scaling up of production. Recently, the emerging applications of artificial intelligence (AI) technologies have significantly improved the performance of 3D bioprinting. However, the existing literature remains deficient in a methodological exploration of AI technologies' potential to overcome these challenges in advancing 3D bioprinting toward clinical application. This paper aims to present a systematic methodology for AI-driven 3D bioprinting, structured within the theoretical framework of Quality by Design (QbD). This paper commences by introducing the QbD theory into 3D bioprinting, followed by summarizing the technology roadmap of AI integration in 3D bioprinting, including multi-scale and multi-modal sensing, data-driven design, and in-line process control. This paper further describes specific AI applications in 3D bioprinting's key elements, including bioink formulation, model structure, printing process, and function regulation. Finally, the paper discusses current prospects and challenges associated with AI technologies to further advance the clinical translation of 3D bioprinting.

TSegLab: Multi-stage 3D dental scan segmentation and labeling

This study introduces a novel deep learning approach for 3D teeth scan segmentation and labeling, designed to enhance accuracy in computer-aided design (CAD) systems. Our method is organized into three key stages: coarse localization, fine teeth segmentation, and labeling. In the teeth localization stage, we employ a Mask-RCNN model to detect teeth in a rendered three-channel 2D representation of the input scan. For fine teeth segmentation, each detected tooth mesh is isomorphically mapped to a 2D harmonic parameter space and segmented with a Mask-RCNN model for precise crown delineation. Finally, for labeling, we propose a graph neural network that captures both the 3D shape and spatial distribution of the teeth, along with a new data augmentation technique to simulate missing teeth and teeth position variation during training. The method is evaluated using three key metrics: Teeth Localization Accuracy (TLA), Teeth Segmentation Accuracy (TSA), and Teeth Identification Rate (TIR). We tested our approach on the Teeth3DS dataset, consisting of 1800 intraoral 3D scans, and achieved a TLA of 98.45%, TSA of 98.17%, and TIR of 97.61%, outperforming existing state-of-the-art techniques. These results suggest that our approach significantly enhances the precision and reliability of automatic teeth segmentation and labeling in dental CAD applications. Link to the project page: https://crns-smartvision.github.io/tseglab.

Artificial neural networks development in prosthodontics - a systematic mapping review

OBJECTIVES

This study aimed to systematically categorize the available literature and offer a comprehensive overview of artificial neural network (ANN) prediction models in prosthodontics. Specifically, the present research introduces a systematic analysis of ANN aims, data, architectures, evaluation metrics, and limitations in prosthodontics.

DATA

The review included articles published until June 2024. The search terms included "prosthodontics" (and related MeSH terms), "neural networks", and "deep learning". Out of 597 identified articles, 70 reports remained after deduplication and screening (2007-2024). Of these, 33 % were from 2023. Implant prosthodontics was the focus in approximately 29 % of reports, and non-implant prosthodontics in 71 %.

SOURCES

Data were collected through electronic searches of PubMed MedLine, PubMed Central, ScienceDirect, Web of Science, and IEEE Xplore databases, along with manual searches in specific journals.

STUDY SELECTION

This study focused on English-language research articles and conference proceedings detailing the development and implementation of ANN prediction models specifically designed for prosthodontics.

CONCLUSIONS

This study shows how ANN models are used in implant and non-implant prosthodontics, with various types of data, architectures, and metrics used for their development and evaluation. It also reveals limitations in ANN development, particularly in the data lifecycle.

CLINICAL SIGNIFICANCE

This study equips practitioners with insights, guiding them in optimizing clinical protocols through ANN integration and facilitating informed decision-making on commercially available systems. Additionally, it supports regulatory efforts, smoothing the path for AI integration in dentistry. Moreover, it sets a trajectory for future exploration, identifying untapped tools and research avenues, fostering interdisciplinary collaborations, and driving innovation in the field.

Two-stage deep learning framework for occlusal crown depth image generation

The generation of depth images of occlusal dental crowns is complicated by the need for customization in each case. To decrease the workload of skilled dental technicians, various computer vision models have been used to generate realistic occlusal crown depth images with definite crown surface structures that can ultimately be reconstructed to three-dimensional crowns and directly used in patient treatment. However, it has remained difficult to generate images of the structure of dental crowns in a fluid position using computer vision models. In this paper, we propose a two-stage model for generating depth images of occlusal crowns in diverse positions. The model is divided into two parts: segmentation and inpainting to obtain both shape and surface structure accuracy. The segmentation network focuses on the position and size of the crowns, which allows the model to adapt to diverse targets. The inpainting network based on a GAN generates curved structures of the crown surfaces based on the target jaw image and a binary mask made by the segmentation network. The performance of the model is evaluated via quantitative metrics for the area detection and pixel-value metrics. Compared to the baseline model, the proposed method reduced the MSE score from 0.007001 to 0.002618 and increased DICE score from 0.9333 to 0.9648. It indicates that the model showed better performance in terms of the binary mask from the addition of the segmentation network and the internal structure through the use of inpainting networks. Also, the results demonstrated an improved ability of the proposed model to restore realistic details compared to other models.

Feasibility of using two generative AI models for teeth reconstruction

OBJECTIVES

This feasibility study investigates the application of artificial intelligence (AI) models, specifically transformer-based (TM) and diffusion-based (DM) models, for the reconstruction of single and multiple missing teeth.

METHODS

A dataset of 129 digitized models was utilized to create both TM and DM models. Single and multiple missing teeth were artificially generated. Reconstruction accuracy was assessed against ground truth data using Root Mean Square (RMS) and mean absolute error (MAE) across various artificially generated teeth. Paired t-tests were used for analyzing differences between the two models (p < 0.05).

RESULTS

Both TM and DM models demonstrated similar accuracy in the reconstruction of single and multiple missing teeth. The greatest disparity occurred in the reconstruction of all remaining teeth, with the exception of 33 and 43 for both models (RMS TM: 0.37; DM: 0.43). TM exhibited the highest precision in reconstructing tooth 34 (RMS: 0.21), whereas DM demonstrated superior accuracy in reconstructing tooth 21 (RMS: 0.19). Despite there was no significant difference between the models.

CONCLUSIONS

AI-based TM and DM models demonstrate promising results in reconstructing missing teeth, with superior accuracy in single-tooth compared to multiple-tooth edentulous spaces. Despite the need for additional refining and larger datasets, including antagonistic teeth, these models have the potential to streamline and improve the dental restoration processes, potentially leading to cost savings and enhanced clinical outcomes.

SIGNIFICANCE

This study demonstrates the feasibility and potential of transformer- and diffusion-based AI models to accurately reconstruct missing teeth, offering a novel approach that could streamline and enhance the precision of implant planning.

Efficient complete denture metal base design via a dental feature-driven segmentation network

BACKGROUND AND OBJECTIVE

Complete denture is a common restorative treatment in dental patients and the design of the core components (major connector and retentive mesh) of complete denture metal base (CDMB) is the basis of successful restoration. However, the automated design process of CDMB has become a challenging task primarily due to the complexity of manual interaction, low personalization, and low design accuracy.

METHODS

To solve the existing problems, we develop a computer-aided Segmentation Network-driven CDMB design framework, called CDMB-SegNet, to automatically generate personalized digital design boundaries for complete dentures of edentulous patients. Specifically, CDMB-SegNet consists of a novel upright-orientation adjustment module (UO-AM), a dental feature-driven segmentation network, and a specific boundary-optimization design module (BO-DM). UO-AM automatically identifies key points for locating spatial attitude of the three-dimensional dental model with arbitrary posture, while BO-DM can result in smoother and more personalized designs for complete denture. In addition, to achieve efficient and accurate feature extraction and segmentation of 3D edentulous models with irregular gingival tissues, the light-weight backbone network is also incorporated into CDMB-SegNet.

RESULTS

Experimental results on a large clinical dataset showed that CDMB-SegNet can achieve superior performance over the state-of-the-art methods. Quantitative evaluation (major connector/retentive mesh) showed improved Accuracy (98.54 ± 0.58 %/97.73 ± 0.92 %) and IoU (87.42 ± 5.48 %/70.42 ± 7.95 %), and reduced Maximum Symmetric Surface Distance (4.54 ± 2.06 mm/4.62 ± 1.68 mm), Average Symmetric Surface Distance (1.45 ± 0.63mm/1.28 ± 0.54 mm), Roughness Rate (6.17 ± 1.40 %/6.80 ± 1.23 %) and Vertices Number (23.22 ± 1.85/43.15 ± 2.72). Moreover, CDMB-SegNet shortened the overall design time to around 4 min, which is one tenth of the comparison methods.

CONCLUSIONS

CDMB-SegNet is the first intelligent neural network for automatic CDMB design driven by oral big data and dental features. The designed CDMB is able to couple with patient's personalized dental anatomical morphology, providing higher clinical applicability compared with the state-of-the-art methods.

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.

NKUT: Dataset and Benchmark for Pediatric Mandibular Wisdom Teeth Segmentation

Germectomy is a common surgery in pediatric dentistry to prevent the potential dangers caused by impacted mandibular wisdom teeth. Segmentation of mandibular wisdom teeth is a crucial step in surgery planning. However, manually segmenting teeth and bones from 3D volumes is time-consuming and may cause delays in treatment. Deep learning based medical image segmentation methods have demonstrated the potential to reduce the burden of manual annotations, but they still require a lot of well-annotated data for training. In this paper, we initially curated a Cone Beam Computed Tomography (CBCT) dataset, NKUT, for the segmentation of pediatric mandibular wisdom teeth. This marks the first publicly available dataset in this domain. Second, we propose a semantic separation scale-specific feature fusion network named WTNet, which introduces two branches to address the teeth and bones segmentation tasks. In WTNet, We design a Input Enhancement (IE) block and a Teeth-Bones Feature Separation (TBFS) block to solve the feature confusions and semantic-blur problems in our task. Experimental results suggest that WTNet performs better on NKUT compared to previous state-of-the-art segmentation methods (such as TransUnet), with a maximum DSC lead of nearly 16%.

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.

Tooth morphology, internal fit, occlusion and proximal contacts of dental crowns designed by deep learning-based dental software: A comparative study

OBJECTIVES

This study compared the tooth morphology, internal fit, occlusion, and proximal contacts of dental crowns automatically generated via two deep learning (DL)-based dental software systems with those manually designed by an experienced dental technician using conventional software.

METHODS

Thirty partial arch scans of prepared posterior teeth were used. The crowns were designed using two DL-based methods (AA and AD) and a technician-based method (NC). The crown design outcomes were three-dimensionally compared, focusing on tooth morphology, internal fit, occlusion, and proximal contacts, by calculating the geometric relationship. Statistical analysis utilized the independent t-test, Mann-Whitney test, one-way ANOVA, and Kruskal-Wallis test with post hoc pairwise comparisons (α = 0.05).

RESULTS

The AA and AD groups, with the NC group as a reference, exhibited no significant tooth morphology discrepancies across entire external or occlusal surfaces. The AD group exhibited higher root mean square and positive average values on the axial surface (P < .05). The AD and NC groups exhibited a better internal fit than the AA group (P < .001). The cusp angles were similar across all groups (P = .065). The NC group yielded more occlusal contact points than the AD group (P = .006). Occlusal and proximal contact intensities varied among the groups (both P < .001).

CONCLUSIONS

Crowns designed by using both DL-based software programs exhibited similar morphologies on the occlusal and axial surfaces; however, they differed in internal fit, occlusion, and proximal contacts. Their overall performance was clinically comparable to that of the technician-based method in terms of the internal fit and number of occlusal contact points.

CLINICAL SIGNIFICANCE

DL-based dental software for crown design can streamline the digital workflow in restorative dentistry, ensuring clinically-acceptable outcomes on tooth morphology, internal fit, occlusion, and proximal contacts. It can minimize the necessity of additional design optimization by dental technician.

Time efficiency, occlusal morphology, and internal fit of anatomic contour crowns designed by dental software powered by generative adversarial network: A comparative study

OBJECTIVES

To evaluate the time efficiency, occlusal morphology, and internal fit of dental crowns designed using generative adversarial network (GAN)-based dental software compared to conventional dental software.

METHODS

Thirty datasets of partial arch scans for prepared posterior teeth were analyzed. Each crown was designed on each abutment using GAN-based software (AI) and conventional dental software (non-AI). The AI and non-AI groups were compared in terms of time efficiency by measuring the elapsed work time. The difference in the occlusal morphology of the crowns before and after design optimization and the internal fit of the crown to the prepared abutment were also evaluated by superimposition for each software. Data were analyzed using independent t tests or Mann-Whitney test with statistical significance (α=.05).

RESULTS

The working time was significantly less for the AI group than the non-AI group at T1, T5, and T6 (P≤.043). The working time with AI was significantly shorter at T1, T3, T5, and T6 for the intraoral scan (P≤.036). Only at T2 (P≤.001) did the cast scan show a significant difference between the two groups. The crowns in the AI group showed less deviation in occlusal morphology and significantly better internal fit to the abutment than those in the non-AI group (both P<.001).

CONCLUSIONS

Crowns designed by AI software showed improved outcomes than that designed by non-AI software, in terms of time efficiency, difference in occlusal morphology, and internal fit.

CLINICAL SIGNIFICANCE

The GAN-based software showed better time efficiency and less deviation in occlusal morphology during the design process than the conventional software, suggesting a higher probability of optimized outcomes of crown design.

Evaluating the performance of generative adversarial network-synthesized periapical images in classifying C-shaped root canals

This study evaluated the performance of generative adversarial network (GAN)-synthesized periapical images for classifying C-shaped root canals, which are challenging to diagnose because of their complex morphology. GANs have emerged as a promising technique for generating realistic images, offering a potential solution for data augmentation in scenarios with limited training datasets. Periapical images were synthesized using the StyleGAN2-ADA framework, and their quality was evaluated based on the average Frechet inception distance (FID) and the visual Turing test. The average FID was found to be 35.353 (± 4.386) for synthesized C-shaped canal images and 25.471 (± 2.779) for non C-shaped canal images. The visual Turing test conducted by two radiologists on 100 randomly selected images revealed that distinguishing between real and synthetic images was difficult. These results indicate that GAN-synthesized images exhibit satisfactory visual quality. The classification performance of the neural network, when augmented with GAN data, showed improvements compared with using real data alone, and could be advantageous in addressing data conditions with class imbalance. GAN-generated images have proven to be an effective data augmentation method, addressing the limitations of limited training data and computational resources in diagnosing dental anomalies.

Two-dimensional facial measurements for anterior tooth selection in complete denture treatment

Purpose

Anterior tooth selection is one of the most challenging parts in determining tooth dimensions and critical to the aesthetic aspect of the complete denture treatment. However, the methods for anterior tooth size selection using facial measurements are still controversial. This study aimed to investigate the relationship between dental measurements and facial measurements, and to establish the anterior tooth size prediction equation using facial dimensions in the Thai population for the complete denture treatment.

Materials & methods

One hundred and twenty-five Thai participants (53 men and 72 women) aged 18-35 years old with Angle class I occlusion, did not currently undergo orthodontic treatment, had normal alignment on the maxillary anterior teeth, no attrition, abrasion, proximal restoration or prosthesis were investigated. One frontal facial photograph and one dental photograph of each participant were made using an image analyzing program (ImageJ version 1.53b) to measure the six horizontal facial distances, five vertical facial distances and three dental distances as 2D facial and dental measurements. Pearson correlation and multiple linear regression analysis were performed.

Results

The difference of facial and dental measurements between men and women were statistically significant (P < .001). Interpupillary width, interlateral canthal width, intercommissural width and bizygomatic width were correlated to dental measurements in both sexes. Intermedial canthal width and lip thickness were correlated to dental measurements in women. Face length and lateral canthus to lower border of face were correlated to dental measurements in men. Prediction equations of each dental measurement were established using only horizontal facial dimension and using both horizontal and vertical facial dimensions.

Conclusions

Facial and dental dimensions are sex-dependent. Facial measurements can be applied in a regression equation to predict dental measurements. Adding vertical dimensions of facial measurements to the prediction equations of anterior tooth size selection results in a higher R squared to 0.444. This finding can be used as a tool for anterior tooth size selection in the complete denture treatment.

Measurement of proximal contact of single crowns to assess interproximal relief: A pilot study

Background

It is common for dental technicians to adjust the proximal surface of adjacent teeth on casts when fabricating single crowns. However, whether the accuracy of the proximal contact is affected if this step is eliminated is unclear.

Objective

To evaluate the accuracy of the proximal contact of single crowns for mandibular first molars fabricated from four different restorative materials, without adjustment of the proximal surface of the adjacent teeth by the laboratory/dental technician.

Methods

This study was in vitro; all the clinical procedures were conducted on a dentoform. The mandibular first molar tooth on the dentoform was prepared using diamond burs and a high speed handpiece. Twenty single crowns were fabricated, five for each group (monolithic zirconia, lithium disilicate, metal ceramic, and cast gold). No proximal surface adjacent to the definitive crowns was adjusted for tight contact in the dental laboratory. Both the qualitative analyses, using dental floss and shimstock, and the quantitative analyses, using a stereo microscope, were performed to evaluate the accuracy of the proximal contact of the restoration with the adjacent teeth. In the quantitative analysis, one-way analysis of variance was used to compare mean values at a significance level of 0.05.

Results

In quantitative analysis, the differences between the proximal contact tightness of the four groups was not statistically significant (P = 0.802 for mesial contacts, P = 0.354 for distal contacts). In qualitative analysis, in most crowns, dental floss passed through the contact with tight resistance and only one film of shimstock could be inserted between the adjacent teeth and the restoration. However, one specimen from the cast gold crown had open contact.

Conclusions

Even without proximal surface adjustment of the adjacent teeth during the crown fabrication process, adequate proximal contact tightness between the restoration and adjacent teeth could be achieved.

TranSDFNet: Transformer-Based Truncated Signed Distance Fields for the Shape Design of Removable Partial Denture Clasps

The ever-growing aging population has led to an increasing need for removable partial dentures (RPDs) since they are typically the least expensive treatment options for partial edentulism. However, the digital design of RPDs remains challenging for dental technicians due to the variety of partially edentulous scenarios and complex combinations of denture components. To accelerate the design of RPDs, we propose a U-shape network incorporated with Transformer blocks to automatically generate RPD clasps, one of the most frequently used RPD components. Unlike existing dental restoration design algorithms, we introduce the voxel-based truncated signed distance field (TSDF) as an intermediate representation, which reduces the sensitivity of the network to resolution and contributes to more smooth reconstruction. Besides, a selective insertion scheme is proposed for solving the memory issue caused by Transformer blocks and enables the algorithm to work well in scenarios with insufficient data. We further design two weighted loss functions to filter out the noisy signals generated from the zero-gradient areas in TSDF. Ablation and comparison studies demonstrate that our algorithm outperforms state-of-the-art reconstruction methods by a large margin and can serve as an intelligent auxiliary in denture design.

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.

A multi-modal fundus image based auxiliary location method of lesion boundary for guiding the layout of laser spot in central serous chorioretinopathy therapy

The lesion boundary of central serous chorioretinopathy (CSCR) is the guarantee to guide the ophthalmologist to accurately arrange the laser spots, so as to enable this ophthalmopathy to be treated precisely. Currently, the accuracy and rapidity of manually locating CSCR lesion boundary in clinic based on single-modal fundus image are limited by imaging quality and ophthalmologist experience, which is also accompanied by poor repeatability, weak reliability and low efficiency. Consequently, a multi-modal fundus image-based lesion boundary auxiliary location method is developed. Firstly, the initial location module (ILM) is employed to achieve the preliminary location of key boundary points of CSCR lesion area on the optical coherence tomography (OCT) B-scan image, then followed by the joint location module (JLM) created based on reinforcement learning for further enhancing the location accuracy. Secondly, the scanning line detection module (SLDM) is constructed to realize the location of lesion scanning line on the scanning laser ophthalmoscope (SLO) image, so as to facilitate the cross-modal mapping of key boundary points. Finally, a simple yet effective lesion boundary location module (LBLM) is designed to assist the automatic cross-modal mapping of key boundary points and enable the final location of lesion boundary. Extensive experiments show that each module can perform well on its corresponding sub task, such as JLM, which makes the correction rate (CR) of ILM increase to 92.11%, comprehensively indicating the effectiveness and feasibility of this method in providing effective lesion boundary guidance for assisting ophthalmologists to precisely arrange the laser spots, and also opening a new research idea for the automatic location of lesion boundary of other fundus diseases.

Deep learning: A primer for dentists and dental researchers

OBJECTIVES

Despite deep learning's wide adoption in dental artificial intelligence (AI) research, researchers from other dental fields and, more so, dental professionals may find it challenging to understand and interpret deep learning studies, their employed methods, and outcomes. The objective of this primer is to explain the basic concept of deep learning. It will lay out the commonly used terms, and describe different deep learning approaches, their methods, and outcomes.

METHODS

Our research is based on the latest review studies, medical primers, as well as the state-of-the-art research on AI and deep learning, which have been gathered in the current study.

RESULTS

In this study, a basic understanding of deep learning models and various approaches to deep learning is presented. An overview of data management strategies for deep learning projects is presented, including data collection, data curation, data annotation, and data preprocessing. Additionally, we provided a step-by-step guide for completing a real-world project.

CONCLUSION

Researchers and clinicians can benefit from this study by gaining insight into deep learning. It can be used to critically appraise existing work or plan new deep learning projects.

CLINICAL SIGNIFICANCE

This study may be useful to dental researchers and professionals who are assessing and appraising deep learning studies within the field of dentistry.

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.

Multi-task deep learning for medical image computing and analysis: A review

The renaissance of deep learning has provided promising solutions to various tasks. While conventional deep learning models are constructed for a single specific task, multi-task deep learning (MTDL) that is capable to simultaneously accomplish at least two tasks has attracted research attention. MTDL is a joint learning paradigm that harnesses the inherent correlation of multiple related tasks to achieve reciprocal benefits in improving performance, enhancing generalizability, and reducing the overall computational cost. This review focuses on the advanced applications of MTDL for medical image computing and analysis. We first summarize four popular MTDL network architectures (i.e., cascaded, parallel, interacted, and hybrid). Then, we review the representative MTDL-based networks for eight application areas, including the brain, eye, chest, cardiac, abdomen, musculoskeletal, pathology, and other human body regions. While MTDL-based medical image processing has been flourishing and demonstrating outstanding performance in many tasks, in the meanwhile, there are performance gaps in some tasks, and accordingly we perceive the open challenges and the perspective trends. For instance, in the 2018 Ischemic Stroke Lesion Segmentation challenge, the reported top dice score of 0.51 and top recall of 0.55 achieved by the cascaded MTDL model indicate further research efforts in high demand to escalate the performance of current models.

Collaborative deep learning model for tooth segmentation and identification using panoramic radiographs

Panoramic radiographs are an integral part of effective dental treatment planning, supporting dentists in identifying impacted teeth, infections, malignancies, and other dental issues. However, screening for anomalies solely based on a dentist's assessment may result in diagnostic inconsistency, posing difficulties in developing a successful treatment plan. Recent advancements in deep learning-based segmentation and object detection algorithms have enabled the provision of predictable and practical identification to assist in the evaluation of a patient's mineralized oral health, enabling dentists to construct a more successful treatment plan. However, there has been a lack of efforts to develop collaborative models that enhance learning performance by leveraging individual models. The article describes a novel technique for enabling collaborative learning by incorporating tooth segmentation and identification models created independently from panoramic radiographs. This collaborative technique permits the aggregation of tooth segmentation and identification to produce enhanced results by recognizing and numbering existing teeth (up to 32 teeth). The experimental findings indicate that the proposed collaborative model is significantly more effective than individual learning models (e.g., 98.77% vs. 96% and 98.44% vs.91% for tooth segmentation and recognition, respectively). Additionally, our models outperform the state-of-the-art segmentation and identification research. We demonstrated the effectiveness of collaborative learning in detecting and segmenting teeth in a variety of complex situations, including healthy dentition, missing teeth, orthodontic treatment in progress, and dentition with dental implants.

A Dual Discriminator Adversarial Learning Approach for Dental Occlusal Surface Reconstruction

Objective. Restoring the correct masticatory function of partially edentulous patient is a challenging task primarily due to the complex tooth morphology between individuals. Although some deep learning-based approaches have been proposed for dental restorations, most of them do not consider the influence of dental biological characteristics for the occlusal surface reconstruction. Description. In this article, we propose a novel dual discriminator adversarial learning network to address these challenges. In particular, this network architecture integrates two models: a dilated convolutional-based generative model and a dual global-local discriminative model. While the generative model adopts dilated convolution layers to generate a feature representation that preserves clear tissue structure, the dual discriminative model makes use of two discriminators to jointly distinguish whether the input is real or fake. While the global discriminator focuses on the missing teeth and adjacent teeth to assess whether it is coherent as a whole, the local discriminator aims only at the defective teeth to ensure the local consistency of the generated dental crown. Results. Experiments on 1000 real-world patient dental samples demonstrate the effectiveness of our method. For quantitative comparison, the image quality metrics are used to measure the similarity of the generated occlusal surface, and the root mean square between the generated result and the target crown calculated by our method is 0.114 mm. In qualitative analysis, the proposed approach can generate more reasonable dental biological morphology. Conclusion. The results demonstrate that our method significantly outperforms the state-of-the-art methods in occlusal surface reconstruction. Importantly, the designed occlusal surface has enough anatomical morphology of natural teeth and superior clinical application value.

Meta-learning with implicit gradients in a few-shot setting for medical image segmentation

Widely used traditional supervised deep learning methods require a large number of training samples but often fail to generalize on unseen datasets. Therefore, a more general application of any trained model is quite limited for medical imaging for clinical practice. Using separately trained models for each unique lesion category or a unique patient population will require sufficiently large curated datasets, which is not practical to use in a real-world clinical set-up. Few-shot learning approaches can not only minimize the need for an enormous number of reliable ground truth labels that are labour-intensive and expensive, but can also be used to model on a dataset coming from a new population. To this end, we propose to exploit an optimization-based implicit model agnostic meta-learning (iMAML) algorithm under few-shot settings for medical image segmentation. Our approach can leverage the learned weights from diverse but small training samples to perform analysis on unseen datasets with high accuracy. We show that, unlike classical few-shot learning approaches, our method improves generalization capability. To our knowledge, this is the first work that exploits iMAML for medical image segmentation and explores the strength of the model on scenarios such as meta-training on unique and mixed instances of lesion datasets. Our quantitative results on publicly available skin and polyp datasets show that the proposed method outperforms the naive supervised baseline model and two recent few-shot segmentation approaches by large margins. In addition, our iMAML approach shows an improvement of 2%-4% in dice score compared to its counterpart MAML for most experiments.

A Few-Shot Learning-Based Retinal Vessel Segmentation Method for Assisting in the Central Serous Chorioretinopathy Laser Surgery

BACKGROUND

The location of retinal vessels is an important prerequisite for Central Serous Chorioretinopathy (CSC) Laser Surgery, which does not only assist the ophthalmologist in marking the location of the leakage point (LP) on the fundus color image but also avoids the damage of the laser spot to the vessel tissue, as well as the low efficiency of the surgery caused by the absorption of laser energy by retinal vessels. In acquiring an excellent intra- and cross-domain adaptability, the existing deep learning (DL)-based vessel segmentation scheme must be driven by big data, which makes the densely annotated work tedious and costly.

METHODS

This paper aims to explore a new vessel segmentation method with a few samples and annotations to alleviate the above problems. Firstly, a key solution is presented to transform the vessel segmentation scene into the few-shot learning task, which lays a foundation for the vessel segmentation task with a few samples and annotations. Then, we improve the existing few-shot learning framework as our baseline model to adapt to the vessel segmentation scenario. Next, the baseline model is upgraded from the following three aspects: (1) A multi-scale class prototype extraction technique is designed to obtain more sufficient vessel features for better utilizing the information from the support images; (2) The multi-scale vessel features of the query images, inferred by the support image class prototype information, are gradually fused to provide more effective guidance for the vessel extraction tasks; and (3) A multi-scale attention module is proposed to promote the consideration of the global information in the upgraded model to assist vessel localization. Concurrently, the integrated framework is further conceived to appropriately alleviate the low performance of a single model in the cross-domain vessel segmentation scene, enabling to boost the domain adaptabilities of both the baseline and the upgraded models.

RESULTS

Extensive experiments showed that the upgraded operation could further improve the performance of vessel segmentation significantly. Compared with the listed methods, both the baseline and the upgraded models achieved competitive results on the three public retinal image datasets (i.e., CHASE_DB, DRIVE, and STARE). In the practical application of private CSC datasets, the integrated scheme partially enhanced the domain adaptabilities of the two proposed models.

[Independent innovation research, development and transformation of precise bionic repair technology for oral prosthesis]

According to the fourth national oral health epidemiological survey report (2018), billions of teeth are lost or missing in China, inducing chewing dysfunction, which is necessary to build physiological function using restorations. Digital technology improves the efficiency and accuracy of oral restoration, with the application of three-dimensional scans, computer-aided design (CAD), computer-aided manufacturing (CAM), bionic material design and so on. However, the basic research and product development of digital technology in China lack international competitiveness, with related products basically relying on imports, including denture 3D design software, 3D oral printers, and digitally processed materials. To overcome these difficulties, from 2001, Yuchun Sun's team, from Peking University School and Hospital of Stomatology, developed a series of studies in artificial intelligence design and precision bionics manufacturing of complex oral prostheses. The research included artificial intelligence design technology for complex oral prostheses, 3D printing systems for oral medicine, biomimetic laminated zirconia materials and innovative application of digital prosthetics in clinical practice. The research from 2001 to 2007 was completed under the guidance of Prof. Peijun Lv and Prof. Yong Wang. Under the support of the National Natural Science Foundation of China, the National Science and Technology Support Program, National High-Tech R & D Program (863 Program) and Beijing Training Project for the Leading Talents in S & T, Yuchun Sun's team published over 200 papers in the relevant field, authorized 49 national invention patents and 1 U.S. invention patent and issued 2 national standards. It also developed 8 kinds of core technology products in digital oral prostheses and 3 kinds of clinical diagnosis and treatment programs, which significantly improved the design efficiency of complex oral prostheses, the fabrication accuracy of metal prostheses and the bionic performance of ceramic materials. Compared with similar international technologies, the program doubled the efficiency of bionic design and manufacturing accuracy and reduced the difficulty of diagnosis and cost of treatment and application by 50%, with the key indicators of those products reaching the international leading level. This program not only helped to realize precision, intelligence and efficiency during prostheses but also provided functional and aesthetic matches for patients after prostheses. The program was rewarded with the First Technical Innovation Prize of the Beijing Science and Technology Awards (2020), Gold Medal of Medical Research Group in the First Medical Science and Technology Innovation Competition of National Health Commission of the People's Republic of China (2020) and Best Creative Award in the First Translational Medical Innovation Competition of Capital (2017). This paper is a review of the current situation of artificial intelligence design and precision bionics manufacturing of complex oral prosthesis.