Clear aligner mandibular advancement in growing patients with Class II malocclusion.

Treatment effects occurring during Class II malocclusion treatment with the clear aligner mandibular advancement protocol were evaluated in two growing patients: one male (12 years, 3 months) and one female (11 years, 9 months). Both patients presented with full cusp Class II molar and canine relationships. Intraoral scans and cone-beam computed tomography were acquired before treatment and after mandibular advancement. Three-dimensional skeletal and dental long-axis changes were quantified, in which the dental long axis was determined by registering the dental crowns obtained from intraoral scans to the root canals in cone-beam computed tomography scans obtained at the same time points. Class II correction was achieved by a combination of mandibular skeletal and dental changes. A similar direction of skeletal and dental changes was observed in both patients, with downward and forward displacement of the mandible resulting from the growth of the mandibular condyle and ramus. Dental changes in both patients included mesialization of the mandibular posterior teeth with flaring of mandibular anterior teeth. In these two patients, clear aligner mandibular advancement was an effective treatment modality for Class II malocclusion correction with skeletal and dental effects and facial profile improvement.

Automatic multi-anatomical skull structure segmentation of cone-beam computed tomography scans using 3D UNETR.

The segmentation of medical and dental images is a fundamental step in automated clinical decision support systems. It supports the entire clinical workflow from diagnosis, therapy planning, intervention, and follow-up. In this paper, we propose a novel tool to accurately process a full-face segmentation in about 5 minutes that would otherwise require an average of 7h of manual work by experienced clinicians. This work focuses on the integration of the state-of-the-art UNEt TRansformers (UNETR) of the Medical Open Network for Artificial Intelligence (MONAI) framework. We trained and tested our models using 618 de-identified Cone-Beam Computed Tomography (CBCT) volumetric images of the head acquired with several parameters from different centers for a generalized clinical application. Our results on a 5-fold cross-validation showed high accuracy and robustness with a Dice score up to 0.962±0.02. Our code is available on our public GitHub repository.

Presurgical orthodontic decompensation with clear aligners.

INTRODUCTION: Orthodontists, surgeons, and patients have taken an interest in using clear aligners in combination with orthognathic surgery. This study aimed to evaluate the accuracy of tooth movements with clear aligners during presurgical orthodontics using novel 3-dimensional superimposition techniques. METHODS: The study sample consisted of 20 patients who have completed presurgical orthodontics using Invisalign clear aligners. Initial (pretreatment) digital dental models, presurgical digital dental models, and ClinCheck prediction models were obtained. Presurgical models were superimposed onto initial ones using stable anatomic landmarks; ClinCheck models were superimposed onto presurgical models using surface best-fit superimposition. Five hundred forty-five teeth were measured for 3 angular movements (buccolingual torque, mesiodistal tip, and rotation) and 4 linear movements (buccolingual, mesiodistal, vertical, and total scalar displacement). The predicted tooth movement was compared with the achieved amount for each movement and tooth, using both percentage accuracy and numerical difference. RESULTS: Average percentage accuracy (63.4% ± 11.5%) was higher than in previously reported literature. The most accurate tooth movements were buccal torque and mesial displacement compared with lingual torque and distal displacement, particularly for mandibular posterior teeth. Clinically significant inaccuracies were found for the buccal displacement of maxillary second molars, lingual displacement of all molars, intrusion of mandibular second molars, the distal tip of molars, second premolars, and mandibular first premolars, buccal torque of maxillary central and lateral incisors, and lingual torque of premolars and molars. CONCLUSIONS: Superimposition techniques used in this study lay the groundwork for future studies to analyze advanced clear aligner patients. Invisalign is a treatment modality that can be considered for presurgical orthodontics-tooth movements involved in arch leveling and decompensation are highly accurate when comparing the simulated and the clinically achieved movements.

Temporomandibular Joint Osteoarthritis Diagnosis Using Privileged Learning of Protein Markers.

Diagnosis of temporomandibular joint (TMJ) Osteoarthritis (OA) before serious degradation of cartilage and subchondral bone occurs can help prevent chronic pain and disability. Clinical, radiomic, and protein markers collected from TMJ OA patients have been shown to be predictive of OA onset. Since protein data can often be unavailable for clinical diagnosis, we harnessed the learning using privileged information (LUPI) paradigm to make use of protein markers only during classifier training. Three different LUPI algorithms are compared with traditional machine learning models on a dataset extracted from 92 unique OA patients and controls. The best classifier performance of 0.80 AUC and 75.6 accuracy was obtained from the KRVFL+ model using privileged protein features. Results show that LUPI-based algorithms using privileged protein data can improve final diagnostic performance of TMJ OA classifiers without needing protein microarray data during classifier diagnosis.

Automatic Segmentation of Dental Root Canal and Merging with Crown Shape.

In this paper, machine learning approaches are proposed to support dental researchers and clinicians to study the shape and position of dental crowns and roots, by implementing a Patient Specific Classification and Prediction tool that includes RootCanalSeg and DentalModelSeg algorithms and then merges the output of these tools for intraoral scanning and volumetric dental imaging. RootCanalSeg combines image processing and machine learning approaches to automatically segment the root canals of the lower and upper jaws from large datasets, providing clinical information on tooth long axis for orthodontics, endodontics, prosthodontic and restorative dentistry procedures. DentalModelSeg includes segmenting the teeth from the crown shape to provide clinical information on each individual tooth. The merging algorithm then allows users to integrate dental models for quantitative assessments. Precision in dentistry has been mainly driven by dental crown surface characteristics, but information on tooth root morphology and position is important for successful root canal preparation, pulp regeneration, planning of orthodontic movement, restorative and implant dentistry. In this paper we propose a patient specific classification and prediction of dental root canal and crown shape analysis workflow that employs image processing and machine learning methods to analyze crown surfaces, obtained by intraoral scanners, and three-dimensional volumetric images of the jaws and teeth root canals, obtained by cone beam computed tomography (CBCT).

Automatic Segmentation of Mandibular Ramus and Condyles.

In order to diagnose TMJ pathologies, we developed and tested a novel algorithm, MandSeg, that combines image processing and machine learning approaches for automatically segmenting the mandibular condyles and ramus. A deep neural network based on the U-Net architecture was trained for this task, using 109 cone-beam computed tomography (CBCT) scans. The ground truth label maps were manually segmented by clinicians. The U-Net takes 2D slices extracted from the 3D volumetric images. All the 3D scans were cropped depending on their size in order to keep only the mandibular region of interest. The same anatomic cropping region was used for every scan in the dataset. The scans were acquired at different centers with different resolutions. Therefore, we resized all scans to 512×512 in the pre-processing step where we also performed contrast adjustment as the original scans had low contrast. After the pre-processing, around 350 slices were extracted from each scan, and used to train the U-Net model. For the cross-validation, the dataset was divided into 10 folds. The training was performed with 60 epochs, a batch size of 8 and a learning rate of 2×10-5. The average performance of the models on the test set presented 0.95 ± 0.05 AUC, 0.93 ± 0.06 sensitivity, 0.9998 ± 0.0001 specificity, 0.9996 ± 0.0003 accuracy, and 0.91 ± 0.03 F1 score. This study findings suggest that fast and efficient CBCT image segmentation of the mandibular condyles and ramus from different clinical data sets and centers can be analyzed effectively. Future studies can now extract radiomic and imaging features as potentially relevant objective diagnostic criteria for TMJ pathologies, such as osteoarthritis (OA). The proposed segmentation will allow large datasets to be analyzed more efficiently for disease classification.

Decision Support Systems in Temporomandibular Joint Osteoarthritis: A review of Data Science and Artificial Intelligence Applications.

With the exponential growth of computational systems and increased patient data acquisition, dental research faces new challenges to manage a large quantity of information. For this reason, data science approaches are needed for the integrative diagnosis of multifactorial diseases, such as Temporomandibular joint (TMJ) Osteoarthritis (OA). The Data science spectrum includes data capture/acquisition, data processing with optimized web-based storage and management, data analytics involving in-depth statistical analysis, machine learning (ML) approaches, and data communication. Artificial intelligence (AI) plays a crucial role in this process. It consists of developing computational systems that can perform human intelligence tasks, such as disease diagnosis, using many features to help in the decision-making support. Patient's clinical parameters, imaging exams, and molecular data are used as the input in cross-validation tasks, and human annotation/diagnosis is also used as the gold standard to train computational learning models and automatic disease classifiers. This paper aims to review and describe AI and ML techniques to diagnose TMJ OA and data science approaches for imaging processing. We used a web-based system for multi-center data communication, algorithms integration, statistics deployment, and process the computational machine learning models. We successfully show AI and data-science applications using patients' data to improve the TMJ OA diagnosis decision-making towards personalized medicine.

Clinical decision support systems in orthodontics: A narrative review of data science approaches.

Advancements in technology and data collection generated immense amounts of information from various sources such as health records, clinical examination, imaging, medical devices, as well as experimental and biological data. Proper management and analysis of these data via high-end computing solutions, artificial intelligence and machine learning approaches can assist in extracting meaningful information that enhances population health and well-being. Furthermore, the extracted knowledge can provide new avenues for modern healthcare delivery via clinical decision support systems. This manuscript presents a narrative review of data science approaches for clinical decision support systems in orthodontics. We describe the fundamental components of data science approaches including (a) Data collection, storage and management; (b) Data processing; (c) In-depth data analysis; and (d) Data communication. Then, we introduce a web-based data management platform, the Data Storage for Computation and Integration, for temporomandibular joint and dental clinical decision support systems.

Web Infrastructure for Data Management, Storage and Computation.

The Data Storage for Computation and Integration (DSCI) proposes management innovations for web-based secure data storage, algorithms deployment, and task execution. Its architecture allows inclusion of plugins for upload, browsing, sharing, and task execution in remote computing grids. Here, we demonstrate the DSCI implementation and the deployment of Image processing tools (TMJSeg), machine learning algorithms (MandSeg, DentalModelSeg), and advanced statistical packages (Multivariate Functional Shape Data Analysis, MFSDA), with data transfer and task execution handled by the clusterpost plug-in. Due to its comprehensive web-based design, local software installation is no longer required. The DSCI aims to enable and maintain a distributed computing and collaboration environment across multi-site clinical centers for the data processing of multisource features such as clinical, biological markers, volumetric images, and 3D surface models, with particular emphasis on analytics for temporomandibular joint osteoarthritis (TMJ OA).

TMJOAI: An Artificial Web-Based Intelligence Tool for Early Diagnosis of the Temporomandibular Joint Osteoarthritis.

Osteoarthritis is a chronic disease that affects the temporomandibular joint (TMJ), causing chronic pain and disability. To diagnose patients suffering from this disease before advanced degradation of the bone, we developed a diagnostic tool called TMJOAI. This machine learning based algorithm is capable of classifying the health status TMJ in of patients using 52 clinical, biological and jaw condyle radiomic markers. The TMJOAI includes three parts. the feature preparation, selection and model evaluation. Feature generation includes the choice of radiomic features (condylar trabecular bone or mandibular fossa), the histogram matching of the images prior to the extraction of the radiomic markers, the generation of feature pairwise interaction, etc.; the feature selection are based on the p-values or AUCs of single features using the training data; the model evaluation compares multiple machine learning algorithms (e.g. regression-based, tree-based and boosting algorithms) from 10 times 5-fold cross validation. The best performance was achieved with averaging the predictions of XGBoost and LightGBM models; and the inclusion of 32 additional markers from the mandibular fossa of the joint improved the AUC prediction performance from 0.83 to 0.88. After cross-validation and testing, the tools presented here have been deployed on an open-source, web-based system, making it accessible to clinicians. TMJOAI allows users to add data and automatically train and update the machine learning models, and therefore improve their performance.

Identification of Specific Lysines and Arginines That Mediate Angiomotin Membrane Association.

The family of Angiomotin (Amot) proteins regulate several biological pathways associated with cellular differentiation, proliferation, and migration. These adaptor proteins target proteins to the apical membrane, actin fibers, or the nucleus. A major function of the Amot coiled-coil homology (ACCH) domain is to initiate protein interactions with the cellular membrane, particularly those containing phosphatidylinositol lipids. The work presented in this article uses several ACCH domain lysine/arginine mutants to probe the relative importance of individual residues for lipid binding. This identified four lysine and three arginine residues that mediate full lipid binding. Based on these findings, three of these residues were mutated to glutamates in the Angiomotin 80 kDa splice form and were incorporated into human mammary cell lines. Results show that mutating three of these residues in the context of full-length Angiomotin reduced the residence of the protein at the apical membrane. These findings provide new insight into how the ACCH domain mediates lipid binding to enable Amot proteins to control epithelial cell growth.