SVA: Shape variation analyzer

Temporo-mandibular osteo arthritis (TMJ OA) is characterized by progressive cartilage degradation and subchondral bone remodeling. The causes of this pathology remain unclear. Current research efforts are concentrated in finding new biomarkers that will help us understand disease progression and ultimately improve the treatment of the disease. In this work, we present Shape Variation Analyzer (SVA), the goal is to develop a noninvasive technique to provide information about shape changes in TMJ OA. SVA uses neural networks to classify morphological variations of 3D models of the mandibular condyle. The shape features used for training include normal vectors, curvature and distances to average models of the condyles. The selected features are purely geometric and are shown to favor the classification task into 6 groups generated by consensus between two clinician experts. With this new approach, we were able to accurately classify 3D models of condyles. In this paper, we present the methods used and the results obtained with this new tool

Novel application and validation of in vivo micro‐CT to study bone modelling in 3D

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149362/1/ocr12265.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149362/2/ocr12265_am.pd

Three‐dimensional evaluation of the maxillary effects of two orthopaedic protocols for the treatment of Class III malocclusion: A prospective study

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146332/1/ocr12247.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146332/2/ocr12247_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

A web-based system for statistical shape analysis in temporomandibular joint osteoarthritis

This study presents a web-system repository: Data Storage for Computation and Integration (DSCI) for Osteoarthritis of the temporomandibular joint (TMJ OA). This environment aims to maintain and allow contributions to the database from multi-clinical centers and compute novel statistics for disease classification. For this purpose, imaging datasets stored in the DSCI consisted of three-dimensional (3D) surface meshes of condyles from CBCT, clinical markers and biological markers in healthy and TMJ OA subjects. A clusterpost package was included in the web platform to be able to execute the jobs in remote computing grids. The DSCI application allowed runs of statistical packages, such as the Multivariate Functional Shape Data Analysis to compute global correlations between covariates and the morphological variability, as well as local p-values in the 3D condylar morphology. In conclusion, the DSCI allows interactive advanced statistical tools for non-statistical experts

Shape variation analyzer: A classifier for temporomandibular joint damaged by osteoarthritis

We developed a deep learning neural network, the Shape Variation Analyzer (SVA), that allows disease staging of bony changes in temporomandibular joint (TMJ) osteoarthritis (OA). The sample was composed of 259 TMJ CBCT scans for the training set and 34 for the testing dataset. The 3D meshes had been previously classified in 6 groups by 2 expert clinicians. We improved the robustness of the training data using data augmentation, SMOTE, to alleviate over-fitting and to balance classes. We combined geometrical features and a shape descriptor, heat kernel signature, to describe every shape. The results were compared to nine different supervised machine learning algorithms. The deep learning neural network was the most accurate for classification of TMJ OA. In conclusion, SVA is a 3D Slicer extension that classifies pathology of the temporomandibular joint osteoarthritis cases based on 3D morphology

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

Condyle‐glenoid fossa relationship after Herbst appliance treatment during two stages of craniofacial skeletal maturation: A retrospective study

ObjectivesTo perform a three‐dimensional evaluation of the position of the condyles in patients treated with Herbst appliance (HA) in two stages of cervical vertebral maturation.Setting and sample populationRetrospective case‐control study. Pubertal Herbst group (PHG; n = 24, mean age 14.5 years, CS 3 and CS 4) and pre‐pubertal Herbst group (PPHG; n = 17, mean age 9.9 years, CS 1 and CS 2) were contrasted with comparison groups of non‐orthopaedically treated Class II patients in pubertal (PCG; n = 17, mean age 13.9 years) and pre‐pubertal maturational stages (PPCG; n = 18, mean age 10.6 years).Materials and MethodsCone‐beam computer tomography scans were taken before treatment (T0) and at T1 after 8 to 12 months. Point‐to‐point measurements of the displacement of the condyles between T0 and T1, relative to the glenoid fossae, were performed in the X, Y, Z and 3D perspectives. Qualitative assessments using semi‐transparent overlays and colour mapping also were produced.ResultsThe displacement of the condyles within the glenoid fossae in the treated groups was small ( .05). Relative to the glenoid fossa, condylar position at T1 was similar to T0 in pre‐pubertal and pubertal groups (P > .05). Similar condylar rotations from T0 to T1 were observed in Herbst and comparison groups, and no significant difference was found between pre‐pubertal and pubertal patients.ConclusionsRegardless the stage of skeletal maturation, HA treatment did not change the condyle‐glenoid fossa relationship.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151917/1/ocr12338_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151917/2/ocr12338.pd