Can Typical Cervical Vertebrae Be Distinguished from One Another by Using Machine Learning Algorithms? Radioanatomic New Markers

Basit Öğe Kaydı
dc.authoridSECGIN, YUSUF/0000-0002-0118-6711
dc.authoridOner, Serkan/0000-0002-7802-880X
dc.authoridONER, ZULAL/0000-0003-0459-1015
dc.contributor.authorSenol, Deniz
dc.contributor.authorSecgin, Yusuf
dc.contributor.authorToy, Seyma
dc.contributor.authorOner, Serkan
dc.contributor.authorOner, Zulal
dc.date.accessioned2024-09-29T16:06:38Z
dc.date.available2024-09-29T16:06:38Z
dc.date.issued2023
dc.departmentKarabük Üniversitesien_US
dc.description.abstractObjective: The aim of this study is to distinguish the typical cervical vertebrae that cannot be separated from one another with the naked eye by using machine algorithms (ML) with measurements made on computerized tomography (CT) images and to show the differences of these vertebrae.Methods: This study was conducted by examining the 536 typical cervical vertebrae CT images of 134 (between the ages of 20 and 55) individuals. Measurements of cervical vertebrae were made on coronal, axial and sagittal section. 6 different combinations (Group 1: C3 - C4, Group 2: C3 - C5, Group 3: C3 - C6, Group 4: C4 - C5, Group 5: C4 - C6, Group 6: C5 - C6) were formed with parameters of each vertebrae and they were analyzed in ML algorithms. Accuracy (Acc), Matthews correlation coefficient (Mcc), Specificity (Spe), Sensitivity (Sen) values were obtained as a result of the analysis.Results: As a result of this study, the highest success was obtained with Linear Discriminant Analysis (LDA) and Logistic Regression (LR) algorithms. The highest Acc rate was found as 0.94 with LDA and LR algorithm in Groups 3 and Group 4, the highest Spe value was found as 0.95 with LDA and LR algorithm in Group 5, the highest Mcc value was found as 0.90 with LDA and LR algorithm in Group 5 and the highest Sen value was found as 0.94 with LDA and LR algorithm in Groups 3 and 5. Conclusions: As a conclusion, it was found that typical cervical vertebrae can be distinguished from each other with high accuracy by using ML algorithms.en_US
dc.identifier.doi10.18521/ktd.1177279
dc.identifier.endpage218en_US
dc.identifier.issn1309-3878
dc.identifier.issue2en_US
dc.identifier.startpage210en_US
dc.identifier.trdizinid1185747en_US
dc.identifier.urihttps://doi.org/10.18521/ktd.1177279
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1185747
dc.identifier.urihttps://hdl.handle.net/20.500.14619/6947
dc.identifier.volume15en_US
dc.identifier.wosWOS:001029516200008en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.publisherDuzce Univ, Fac Medicineen_US
dc.relation.ispartofKonuralp Tip Dergisien_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectTypical Cervical Vertebraeen_US
dc.subjectMachine Learning Algorithmsen_US
dc.subjectComputerized Tomographyen_US
dc.titleCan Typical Cervical Vertebrae Be Distinguished from One Another by Using Machine Learning Algorithms? Radioanatomic New Markersen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
TR-Dizin İndeksli Yayınlar Koleksiyonu