13 March 2017 Diagnostic index: an open-source tool to classify TMJ OA condyles
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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.
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Beatriz Paniagua, Beatriz Paniagua, Laura Pascal, Laura Pascal, Juan Prieto, Juan Prieto, Jean Baptiste Vimort, Jean Baptiste Vimort, Liliane Gomes, Liliane Gomes, Marilia Yatabe, Marilia Yatabe, Antonio Carlos Ruellas, Antonio Carlos Ruellas, Francois Budin, Francois Budin, Steve Pieper, Steve Pieper, Martin Styner, Martin Styner, Erika Benavides, Erika Benavides, Lucia Cevidanes, Lucia Cevidanes, } "Diagnostic index: an open-source tool to classify TMJ OA condyles", Proc. SPIE 10137, Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging, 101372H (13 March 2017); doi: 10.1117/12.2254070; https://doi.org/10.1117/12.2254070

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