Evaluation of the Artificial Neural Network and Naive Bayes Models Trained with Vertebra Ratios for Growth and Development Determination

Basit Öğe Kaydı
dc.contributor.authorKok, Hatice
dc.contributor.authorIzgi, Mehmet Said
dc.contributor.authorAcilar, Ayse Merve
dc.date.accessioned2024-02-23T14:41:19Z
dc.date.available2024-02-23T14:41:19Z
dc.date.issued2021
dc.departmentNEÜen_US
dc.description.abstractObjective: This study aimed to evaluate the success rates of the artificial neural network models (NNMs) and naive Bayes models (NBMs) trained with various cervical vertebra ratios in cephalometric radiographs for determining growth and development. Methods: Our retrospective study was performed on 360 individuals between the ages of 8 and 17 years, whose cephalometric radiographs were taken. According to the evaluation of cephalometric radiographs, growth and development periods were divided into 6 vertebral stages. Each stage was considered as a group, each group had 30 girls and 30 boys. Twenty-eight cervical vertebral ratios were obtained by using 10 horizontal and 13 vertical measurements. These 28 vertebral ratios were combined in 4 different combinations, leading to 4 different datasets. Each dataset was split into 2 parts as training and testing. To prevent the overfitting, a 5-cross fold validation technique was also used in the training phase. The experiments were conducted on 2 different train/test ratios as 80%-20% and 70%-30% for both NNMs and NBMs. Results: The highest determination success rate was obtained in NNM 3 (0.95) and the lowest in NBM 4 (0.50). The determination success of NBM 1 and NBM 3 was almost similar (0.60). The success of NNM 2 did not differ much from that of NNM 1 (0.94). The determination success of stage 5 was relatively lower than the others in NNM 1 and NNM 2 (0.83). Conclusion: The NNMs were more successful than the NBMs in our developed models. It is important to determine the effective ratio and/or measurements that will be useful for differentiation.en_US
dc.identifier.doi10.5152/TurkJOrthod.2020.20059
dc.identifier.endpage9en_US
dc.identifier.issn2148-9505
dc.identifier.issue1en_US
dc.identifier.pmid33828872en_US
dc.identifier.scopus2-s2.0-85103645443en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage2en_US
dc.identifier.urihttps://doi.org/10.5152/TurkJOrthod.2020.20059
dc.identifier.urihttps://hdl.handle.net/20.500.12452/16801
dc.identifier.volume34en_US
dc.identifier.wosWOS:000628791300001en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherAvesen_US
dc.relation.ispartofTurkish Journal Of Orthodonticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectBone Age Measurementen_US
dc.subjectCephalometryen_US
dc.subjectCervical Vertebraeen_US
dc.titleEvaluation of the Artificial Neural Network and Naive Bayes Models Trained with Vertebra Ratios for Growth and Development Determinationen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

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