Sex estimation with parameters of the facial canal by computed tomography using machine learning algorithms and artificial neural networks
 
Yazarlar (7)
Yusuf Seçgin Karabük Üniversitesi, Türkiye
Seren Kaya
Düzce Üniversitesi, Türkiye
Öğr. Gör. Dr. Oğuzhan HARMANDAOĞLU Kastamonu Üniversitesi, Türkiye
Öğr. Gör. Oğuzhan ÖZTÜRK Kastamonu Üniversitesi, Türkiye
Doç. Dr. Deniz Şenol Düzce Üniversitesi, Türkiye
Prof. Dr. Ömer Önbaş Düzce Üniversitesi, Türkiye
Nihat Yılmaz Karabük Üniversitesi, Türkiye
Makale Türü Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
Dergi Adı BMC MEDICAL IMAGING (Q1)
Dergi ISSN 1471-2342 Dergi Bilgileri (2025)
Dergi Tarandığı Indeksler SCI-Expanded
Makale Dili İngilizce Basım Tarihi 07-2025
Cilt / Sayı / Sayfa 25 / 1 / 291–0 DOI 10.1186/s12880-025-01834-7
Makale Linki https://doi.org/10.1186/s12880-025-01834-7
UAK Araştırma Alanları
Mimarlık, Planlama ve Tasarım
Özet
BackgroundThe skull is highly durable and plays a significant role in sex determination as one of the most dimorphic bones. The facial canal (FC), a clinically significant canal within the temporal bone, houses the facial nerve. This study aims to estimate sex using morphometric measurements from the FC through machine learning (ML) and artificial neural networks (ANNs).Materials and methodsThe study utilized Computed Tomography (CT) images of 200 individuals (100 females, 100 males) aged 19–65 years. These images were retrospectively retrieved from the Picture Archiving and Communication Systems (PACS) at Düzce University Faculty of Medicine, Department of Radiology, covering 2021–2024. Bilateral measurements of nine temporal bone parameters were performed in axial, coronal, and sagittal planes. ML algorithms including Quadratic Discriminant Analysis (QDA), Linear Discriminant Analysis …
Anahtar Kelimeler
Sex estimation | Machine learning | Artificial neural network | Facial canal | Fallopian canal
BM Sürdürülebilir Kalkınma Amaçları
Atıf Sayıları
Web of Science 5
Google Scholar 8
Sex estimation with parameters of the facial canal by computed tomography using machine learning algorithms and artificial neural networks

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