Temporomandibular Joint Disorders Multi-Class Classification Using Deep Learning.

Journal: Studies in health technology and informatics
Published Date: Jan 25, 2024

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.

Authors

  • Bhornsawan Thanathornwong
    Faculty of Dentistry, Srinakharinwirot University, Bangkok, Thailand.
  • Treesukon Treebupachatsakul
    School of Engineering, King Mongkut's Institute of Technology, Bangkok, Thailand.
  • Thitirat Teechot
    School of Engineering, King Mongkut's Institute of Technology, Bangkok, Thailand.
  • Suvit Poomrittigul
    Faculty of Science and Technology, Pathumwan Institute of Technology, Bangkok, Thailand.
  • Kritsasith Warin
    Division of Oral and Maxillofacial Surgery, Faculty of Dentistry, Thammasat University, Pathum Thani, Thailand.
  • Siriwan Suebnukarn
    Faculty of Dentistry, Thammasat University, Pathum Thani, Thailand.

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