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Temporomandibular Joint Disorders Multi-Class Classification Using Deep Learning

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dc.contributor.authorThanathornwong B.
dc.contributor.authorTreebupachatsakul T.
dc.contributor.authorTeechot T.
dc.contributor.authorPoomrittigul S.
dc.contributor.authorWarin K.
dc.contributor.authorSuebnukarn S.
dc.contributor.correspondenceThanathornwong B.
dc.contributor.otherSrinakharinwirot University
dc.date.accessioned2025-05-28T07:55:19Z
dc.date.issued2024-01-25
dc.date.issuedBE2567-01-25
dc.description.abstractTemporomandibular 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.
dc.identifier.citationStudies in Health Technology and Informatics Vol.310 (2024) , 1495-1496
dc.identifier.doi10.3233/SHTI231261
dc.identifier.eissn18798365
dc.identifier.issn09269630
dc.identifier.pmid38269713
dc.identifier.scopus2-s2.0-85183574923
dc.identifier.urihttps://hdl.handle.net/20.500.14740/20282
dc.rights.holderSCOPUS
dc.subjectEngineering
dc.subjectMedicine
dc.subjectHealth Professions
dc.titleTemporomandibular Joint Disorders Multi-Class Classification Using Deep Learning
dc.typeConference Paper
dspace.entity.typePublication
oaire.citation.endPage1496
oaire.citation.startPage1495
oaire.citation.titleStudies in Health Technology and Informatics
oaire.citation.volume310
oairecerif.author.affiliationThammasat University
oairecerif.author.affiliationSrinakharinwirot University
oairecerif.author.affiliationKing Mongkut's Institute of Technology
oairecerif.author.affiliationPathumwan Institute of Technology
swu.datasource.scopushttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85183574923&origin=inward

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Scopus: Year 2024