Determination of growth and development periods in orthodontics with artificial neural network

Basit Öğe Kaydı
dc.contributor.authorKok, Hatice
dc.contributor.authorIzgi, Mehmet Said
dc.contributor.authorAcilar, Ayse Merve
dc.date.accessioned2024-02-23T14:24:33Z
dc.date.available2024-02-23T14:24:33Z
dc.date.issued2021
dc.departmentNEÜen_US
dc.description.abstractBackground We aimed to determine the growth-development periods and gender from the cervical vertebrae using the artificial neural network (ANN). Setting and Sample Population The cephalometric and hand-wrist radiographs obtained from 419 patients aged between 8 and 17 years were included in our study. Materials and Methods Our retrospective study consisted of 419 patients' cephalometric and hand-wrist radiographs. The cephalometric radiographs were divided into six cervical vertebrae stages (CVS). Correlations were evaluated between hand-wrist maturation level, CVS, and ages. Twenty-seven vertebral reference points are marked on the cephalometric radiograph, and 32 linear measurements were taken. With the combination of these measurements, 24 different data sets were formed to train ANN. Thus, 24 different ANN models for the determination of the growth-development periods were obtained. According to the results, seven ANN models that have a high success level and clinically applicable were selected. Also, an ANN model was done by all measurements and age for the determination of gender from cervical vertebrae. Results Significantly positive correlations between hand-wrist maturation level, CVS and ages were detected. The ANN-7 model (32 linear measurements and age) accuracy value was found 0.9427. The highest model accuracy, 0.8687, with the least linear measurements, was obtained by drawing 13 linear measurements, using vertical measurements and indents. Gender was determined using ANN (0.8950) on cervical vertebrae data. Conclusion The growth-development periods and gender were determined from the cervical vertebrae by using ANN. The success of the ANN algorithm has been satisfactory. Further studies are needed for a fully automatic decision support system.en_US
dc.identifier.doi10.1111/ocr.12443
dc.identifier.endpage83en_US
dc.identifier.issn1601-6335
dc.identifier.issn1601-6343
dc.identifier.pmid33232582en_US
dc.identifier.scopus2-s2.0-85098130035en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage76en_US
dc.identifier.urihttps://doi.org/10.1111/ocr.12443
dc.identifier.urihttps://hdl.handle.net/20.500.12452/14004
dc.identifier.volume24en_US
dc.identifier.wosWOS:000603021100001en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherWileyen_US
dc.relation.ispartofOrthodontics & Craniofacial Researchen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAge Determination By Skeletonen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectCephalometryen_US
dc.subjectCervical Vertebraeen_US
dc.subjectComputer? Assisted Diagnosisen_US
dc.subjectGrowth And Developmenten_US
dc.subjectNeural Networken_US
dc.titleDetermination of growth and development periods in orthodontics with artificial neural networken_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
PubMed İndeksli Yayın Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu