Classification of Intraoral Photographs with Deep Learning Algorithms Trained According to Cephalometric Measurements

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dc.contributor.authorKartbak, Sultan Buesra Ay
dc.contributor.authorOzel, Mehmet Birol
dc.contributor.authorKocakaya, Duygu Nur Cesur
dc.contributor.authorCakmak, Muhammet
dc.contributor.authorSinanoglu, Enver Alper
dc.date.accessioned2026-04-25T14:20:27Z
dc.date.available2026-04-25T14:20:27Z
dc.date.issued2025
dc.departmentSinop Üniversitesi
dc.description.abstractBackground/Objectives: Clinical intraoral photographs are important for orthodontic diagnosis, treatment planning, and documentation. This study aimed to evaluate deep learning algorithms trained utilizing actual cephalometric measurements for the classification of intraoral clinical photographs. Methods: This study was executed on lateral cephalograms and intraoral right-side images of 990 patients. IMPA, interincisal angle, U1-palatal plane angle, and Wits appraisal values were measured utilizing WebCeph. Intraoral photographs were divided into three groups based on cephalometric measurements. A total of 14 deep learning models (DenseNet 121, DenseNet 169, DenseNet 201, EfficientNet B0, EfficientNet V2, Inception V3, MobileNet V2, NasNetMobile, ResNet101, ResNet152, ResNet50, VGG16, VGG19, and Xception) were employed to classify the intraoral photographs. Performance metrics (F1 scores, accuracy, precision, and recall) were calculated and confusion matrices were formed. Results: The highest accuracy rates were 98.33% for IMPA groups, 99.00% for interincisal angle groups, 96.67% for U1-palatal plane angle groups, and 98.33% for Wits measurement groups. Lowest accuracy rates were 59% for IMPA groups, 53% for interincisal angle groups, 33.33% for U1-palatal plane angle groups, and 83.67% for Wits measurement groups. Conclusions: Although accuracy rates varied among classifications and DL algorithms, successful classification could be achieved in the majority of cases. Our results may be promising for case classification and analysis without the need for lateral cephalometric radiographs.
dc.identifier.doi10.3390/diagnostics15091059
dc.identifier.issn2075-4418
dc.identifier.issue9
dc.identifier.orcid0000-0002-2984-9468
dc.identifier.orcid0000-0003-3862-5658
dc.identifier.orcid0000-0002-3752-6642
dc.identifier.orcid0000-0002-8349-3239
dc.identifier.pmid40361877
dc.identifier.scopus2-s2.0-105004852103
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.3390/diagnostics15091059
dc.identifier.urihttps://hdl.handle.net/11486/8586
dc.identifier.volume15
dc.identifier.wosWOS:001486346400001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofDiagnostics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260420
dc.subjectdeep learning
dc.subjectartificial intelligence
dc.subjectintraoral photograph
dc.subjectcephalometry
dc.titleClassification of Intraoral Photographs with Deep Learning Algorithms Trained According to Cephalometric Measurements
dc.typeArticle

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