Gender Prediction Using Cone-Beam Computed Tomography Measurements from Foramen Incisivum: Application of Machine Learning Algorithms and Artificial Neural Networks
Yazarlar (6)
Doç. Dr. Deniz Şenol Düzce Üniversitesi, Türkiye
Yusuf Seçgin Karabük Üniversitesi, Türkiye
Öğr. Gör. Dr. Oğuzhan HARMANDAOĞLU Kastamonu Üniversitesi, Türkiye
Seren Kaya
T.C. Beykent Üniversitesi, Türkiye
T.C. Beykent Üniversitesi, Türkiye
Prof. Dr. Şuayip Burak Duman İnönü Üniversitesi, Türkiye
Prof. Dr. Zülal Öner İzmir Bakırçay Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
|
||
| Dergi Adı | Journal of the Anatomical Society of India (Q4) | ||
| Dergi ISSN | 0003-2778 Dergi Bilgileri (2024) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 04-2024 |
| Kabul Tarihi | 12-05-2024 | Yayınlanma Tarihi | 01-04-2024 |
| Cilt / Sayı / Sayfa | 73 / 2 / 152–159 | DOI | 10.4103/jasi.jasi_129_23 |
| Makale Linki | https://journals.lww.com/joai/fulltext/2024/04000/gender_prediction_using_cone_beam_computed.10.aspx | ||
| UAK Araştırma Alanları |
Mimarlık, Planlama ve Tasarım
|
||
| Özet |
| Materials and Methods:This study was conducted on 162 individuals in total. Precise measurements were meticulously extracted, extending from the foramen incisivum to the arcus alveolaris maxillaris, through employment of CBCT. The ML and ANN models were meticulously devised, allocating 20% for rigorous testing and 80% for comprehensive training.Results:All parameters that are evaluated, except for the angle between foramen palatinum majus and foramen incisivum-spina nasalis posterior (GPFIFPNS-A), exhibited a significant gender difference. ANN and among the ML algorithms, logistic regression (LR), linear discriminant analysis (LDA), and random rorest (RF) demonstrated the highest accuracy (Acc) rate of 0.82. The Acc rates for other algorithms ranged from 0.76 to 0.79. In the models with the highest Acc rates, 14 out of 17 male individuals and 13 out of 16 female individuals in the test set were … |
| Anahtar Kelimeler |
| Artificial intelligence | foramen incisivum | forensic anthropology | gender prediction | machine learning | maxilla |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 8 |
| Google Scholar | 8 |