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Proceedings Paper

Diagnostic index of 3D osteoarthritic changes in TMJ condylar morphology
Author(s): Liliane R. Gomes; Marcelo Gomes; Bryan Jung; Beatriz Paniagua; Antonio C. Ruellas; João Roberto Gonçalves; Martin A. Styner; Larry Wolford; Lucia Cevidanes
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Paper Abstract

The aim of this study was to investigate imaging statistical approaches for classifying 3D osteoarthritic morphological variations among 169 Temporomandibular Joint (TMJ) condyles. Cone beam Computed Tomography (CBCT) scans were acquired from 69 patients with long-term TMJ Osteoarthritis (OA) (39.1 ± 15.7 years), 15 patients at initial diagnosis of OA (44.9 ± 14.8 years) and 7 healthy controls (43 ± 12.4 years). 3D surface models of the condyles were constructed and Shape Correspondence was used to establish correspondent points on each model. The statistical framework included a multivariate analysis of covariance (MANCOVA) and Direction-Projection- Permutation (DiProPerm) for testing statistical significance of the differences between healthy control and the OA group determined by clinical and radiographic diagnoses. Unsupervised classification using hierarchical agglomerative clustering (HAC) was then conducted. Condylar morphology in OA and healthy subjects varied widely. Compared with healthy controls, OA average condyle was statistically significantly smaller in all dimensions except its anterior surface. Significant flattening of the lateral pole was noticed at initial diagnosis (p < 0.05). It was observed areas of 3.88 mm bone resorption at the superior surface and 3.10 mm bone apposition at the anterior aspect of the long-term OA average model. 1000 permutation statistics of DiProPerm supported a significant difference between the healthy control group and OA group (t = 6.7, empirical p-value = 0.001). Clinically meaningful unsupervised classification of TMJ condylar morphology determined a preliminary diagnostic index of 3D osteoarthritic changes, which may be the first step towards a more targeted diagnosis of this condition.

Paper Details

Date Published: 20 March 2015
PDF: 9 pages
Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 941405 (20 March 2015); doi: 10.1117/12.2082226
Show Author Affiliations
Liliane R. Gomes, Univ. of Michigan (United States)
UNESP Univ. Estadual Paulista (Brazil)
Marcelo Gomes, Private Practice (Brazil)
Bryan Jung, The Univ. of North Carolina at Chapel Hill (United States)
Beatriz Paniagua, The Univ. of North Carolina at Chapel Hill (United States)
Antonio C. Ruellas, Univ. of Michigan (United States)
João Roberto Gonçalves, UNESP Univ. Estadual Paulista (Brazil)
Martin A. Styner, The Univ. of North Carolina at Chapel Hill (United States)
Larry Wolford, Baylor Univ. Medical Ctr. (United States)
Lucia Cevidanes, Univ. of Michigan (United States)


Published in SPIE Proceedings Vol. 9414:
Medical Imaging 2015: Computer-Aided Diagnosis
Lubomir M. Hadjiiski; Georgia D. Tourassi, Editor(s)

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