Zygomaticomaxillary suture maturation: A predictor of maxillary protraction? Part I ‐ A classification method

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136728/1/ocr12143.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136728/2/ocr12143_am.pd

Zygomaticomaxillary suture maturation: Part IIâ The influence of sutural maturation on the response to maxillary protraction

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137741/1/ocr12191_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137741/2/ocr12191.pd

Three‐dimensional skeletal mandibular changes associated with Herbst appliance treatment

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136722/1/ocr12154_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136722/2/ocr12154.pd

3D superimposition and understanding temporomandibular joint arthritis

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/111112/1/ocr12070.pd

A web-based system for neural network based classification in temporomandibular joint osteoarthritis

Objective: The purpose of this study is to describe the methodological innovations of a web-based system for storage, integration and computation of biomedical data, using a training imaging dataset to remotely compute a deep neural network classifier of temporomandibular joint osteoarthritis (TMJOA). Methods: This study imaging dataset consisted of three-dimensional (3D) surface meshes of mandibular condyles constructed from cone beam computed tomography (CBCT) scans. The training dataset consisted of 259 condyles, 105 from control subjects and 154 from patients with diagnosis of TMJ OA. For the image analysis classification, 34 right and left condyles from 17 patients (39.9 ± 11.7 years), who experienced signs and symptoms of the disease for less than 5 years, were included as the testing dataset. For the integrative statistical model of clinical, biological and imaging markers, the sample consisted of the same 17 test OA subjects and 17 age and sex matched control subjects (39.4 ± 15.4 years), who did not show any sign or symptom of OA. For these 34 subjects, a standardized clinical questionnaire, blood and saliva samples were also collected. The technological methodologies in this study include a deep neural network classifier of 3D condylar morphology (ShapeVariationAnalyzer, SVA), and a flexible web-based system for data storage, computation and integration (DSCI) of high dimensional imaging, clinical, and biological data. Results: The DSCI system trained and tested the neural network, indicating 5 stages of structural degenerative changes in condylar morphology in the TMJ with 91% close agreement between the clinician consensus and the SVA classifier. The DSCI remotely ran with a novel application of a statistical analysis, the Multivariate Functional Shape Data Analysis, that computed high dimensional correlations between shape 3D coordinates, clinical pain levels and levels of biological markers, and then graphically displayed the computation results. Conclusions: The findings of this study demonstrate a comprehensive phenotypic characterization of TMJ health and disease at clinical, imaging and biological levels, using novel flexible and versatile open-source tools for a web-based system that provides advanced shape statistical analysis and a neural network based classification of temporomandibular joint osteoarthritis

Minimally Invasive Approach for Diagnosing TMJ Osteoarthritis

This study’s objectives were to test correlations among groups of biomarkers that are associated with condylar morphology and to apply artificial intelligence to test shape analysis features in a neural network (NN) to stage condylar morphology in temporomandibular joint osteoarthritis (TMJOA). Seventeen TMJOA patients (39.9 ± 11.7 y) experiencing signs and symptoms of the disease for less than 10 y and 17 age- and sex-matched control subjects (39.4 ± 15.2 y) completed a questionnaire, had a temporomandibular joint clinical exam, had blood and saliva samples drawn, and had high-resolution cone beam computed tomography scans taken. Serum and salivary levels of 17 inflammatory biomarkers were quantified using protein microarrays. A NN was trained with 259 other condyles to detect and classify the stage of TMJOA and then compared to repeated clinical experts’ classifications. Levels of the salivary biomarkers MMP-3, VE-cadherin, 6Ckine, and PAI-1 were correlated to each other in TMJOA patients and were significantly correlated with condylar morphological variability on the posterior surface of the condyle. In serum, VE-cadherin and VEGF were correlated with one another and with significant morphological variability on the anterior surface of the condyle, while MMP-3 and CXCL16 presented statistically significant associations with variability on the anterior surface, lateral pole, and superior-posterior surface of the condyle. The range of mouth opening variables were the clinical markers with the most significant associations with morphological variability at the medial and lateral condylar poles. The repeated clinician consensus classification had 97.8% agreement on degree of degeneration within 1 group difference. Predictive analytics of the NN’s staging of TMJOA compared to the repeated clinicians’ consensus revealed 73.5% and 91.2% accuracy. This study demonstrated significant correlations among variations in protein expression levels, clinical symptoms, and condylar surface morphology. The results suggest that 3-dimensional variability in TMJOA condylar morphology can be comprehensively phenotyped by the NN

Concepts, protocol, variations and current trends in surgery first orthognathic approach: A literature review

In the current era of expedited orthodontics, among many clinicians, tertiary care hospitals and patients, surgery first orthognathic approach (SFOA) has gained popularity. The advantages of SFOA (face first approach) are the reduced overall treatment duration and the early improvement in facial esthetics. In SFOA, the absence of a presurgical phase allows surgery to be performed first, followed by comprehensive orthodontic treatment to achieve the desired occlusion. The basic concepts of surgery early, surgery last, SFOA and Sendai SFOA technique along with its variations are reviewed in the present article. The recent advancement in SFOA in the context of preoperative preparation, surgical procedures and post-surgical orthodontics with pertinent literature survey are also discussed

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