Diagnostic index: an open-source tool to classify TMJ OA condyles

Abstract

Osteoarthritis (OA) of temporomandibular joints (TMJ) occurs in about 40% of the patients who present TMJ disorders. Despite its prevalence, OA diagnosis and treatment remain controversial since there are no clear symptoms of the disease, especially in early stages. Quantitative tools based on 3D imaging of the TMJ condyle have the potential to help characterize TMJ OA changes. The goals of the tools proposed in this study are to ultimately develop robust imaging markers for diagnosis and assessment of treatment efficacy. This work proposes to identify differences among asymptomatic controls and different clinical phenotypes of TMJ OA by means of Statistical Shape Modeling (SSM), obtained via clinical expert consensus. From three different grouping schemes (with 3, 5 and 7 groups), our best results reveal that that the majority (74.5%) of the classifications occur in agreement with the groups assigned by consensus between our clinical experts. Our findings suggest the existence of different disease-based phenotypic morphologies in TMJ OA. Our preliminary findings with statistical shape modeling based biomarkers may provide a quantitative staging of the disease. The methodology used in this study is included in an open source image analysis toolbox, to ensure reproducibility and appropriate distribution and dissemination of the solution proposed.

Assessment of dentofacial growth deviation in juvenile idiopathic arthritis: Reliability and validity of three-dimensional morphometric measures

“…Nonetheless, this allowed consistent comparisons between sides and facilitated a complete characterization of facial asymmetry. Color-coded overlay mapping is an alternative technique for 3D visualization of facial asymmetry with promising clinical and research applicability [ 38 , 39 ].…”

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

“…After registration, all condylar models were simultaneously cropped to define the condylar region of interest. SPHARM-PDM software (Paniagua et al, 2017) was used to generate a mesh with 1002 correspondent vertices, via spherical parameterization of the input segmentations. An average 3D condylar shape was generated for the TMJ OA groups and control group using the ShapeVariationAnalyzer (SVA) extension for the 3D Slicer open-source software (Dumast, 2017, Fedorov et al, 2012).…”

“…Patient data in clinical research on Temporo-Mandibular Joint Osteoarthritis often includes large amounts of structured information, such as imaging data, biological marker levels, and clinical variables. The present study proposes improvement in the precision of the subjective radiological interpretation of morphological variability described in Su et al, (2014) the unsupervised statistical classification proposed by Gomes et al, (2015), and the shape statistical models proposed by Paniagua et al, (2017). Given the various sources of information, computerized methods can be a great help to clinicians to discover hidden patterns in the data.…”

Temporomandibular Joint Bioengineering Conference: Working Together Toward Improving Clinical Outcomes

“…This organization of collagen fiber alignment and type provides the TMJ with the functional characteristic of withstanding tensile loading better than hyaline cartilage. As such, Dr. Kapila explained that the significance of these distinctions to disease initiation and progression between the TMJ and appendicular joints may explain certain genetic disorders that affect every joint in the body while sparing the TMJ [39] as well as the unique age and gender distribution of TMDs [40][41][42][43][44].…”