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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Thanathornwong, B., Treebupachatsakul, T., Teechot, T., Poomrittigul, S., Warin, K., & Suebnukarn, S. (2024). Temporomandibular Joint Disorders Multi-Class Classification Using Deep Learning. In Studies in Health Technology and Informatics (Vol. 310, pp. 1495–1496). IOS Press BV. https://doi.org/10.3233/SHTI231261
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