Shape variation analyzer: A classifier for temporomandibular joint damaged by osteoarthritis
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Data
2019-01-01
Autores
Ribera, Nina Tubau
De Dumast, Priscille
Yatabe, Marilia
Ruellas, Antonio
Ioshida, Marcos
Paniagua, Beatriz
Styner, Martin
Gonçalves, João Roberto [UNESP]
Bianchi, Jonas [UNESP]
Cevidanes, Lucia
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Resumo
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.
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Classification, Deep Learning, Neural Network, Osteoarthritis, Temporomandibular Joint Disorders
Como citar
Progress in Biomedical Optics and Imaging - Proceedings of SPIE, v. 10950.