Temporomandibular Joint Disorders Multi-Class Classification Using Deep Learning

Thanathornwong, Bhornsawan; Treebupachatsakul, Treesukon; Teechot, Thitirat; Poomrittigul, Suvit; Warin, Kritsasith; Suebnukarn, Siriwan

doi:10.3233/SHTI231261

Temporomandibular Joint Disorders Multi-Class Classification Using Deep Learning

Authors

Bhornsawan Thanathornwong, Treesukon Treebupachatsakul, Thitirat Teechot, Suvit Poomrittigul, Kritsasith Warin, Siriwan Suebnukarn

Pages

1495 - 1496

DOI

10.3233/SHTI231261

Category

Research Article

Series

Studies in Health Technology and Informatics

Ebook

Volume 310: MEDINFO 2023 — The Future Is Accessible

Abstract

Temporomandibular joint (TMJ) disorders have been misinterpreted by various normal TMJ features leading to treatment failure. This study assessed deep learning algorithms, DenseNet-121 and InceptionV3, for multi-class classification of TMJ normal variations and disorders in 1,710 panoramic radiographs. The overall accuracy of DenseNet-121 and InceptionV3 were 0.99 and 0.95, respectively. The AUC from 0.99 to 1.00, indicating high performance for TMJ disorders classification in panoramic radiographs.

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