Scopus
🔓 Açık Erişim YÖKSİS ISSN Eşleşti
SJR Q2
Evaluation of the artificial neural network and naive bayes models trained with vertebra ratios for growth and development determination
Turkish Journal of Orthodontics · Mart 2021
YÖKSİS Kayıtları — ISSN Eşleşmesi
Evaluation of Enamel Surface Roughness after Various Finishing Techniques for Debonding of Orthodontic Brackets
2016 ISSN: 25289659 ESCI
Prof. Dr. ZEHRA İLERİ →
Evaluation of the Artificial Neural Network and Naive Bayes Models Trained with Vertebra Ratios for Growth and Development Determination
2021 ISSN: 2528-9659 Emerging Sources Citation Index (ESCI)
Doç. Dr. HATİCE KÖK →
Alterations in Facial Soft Tissue Thickness
Post-Facemask Treatment in Noncleft Skeletal Class III
and Bilateral Cleft Lip Palate Class III Patients
2022 ISSN: 2528-9659 ESCI
Dr. Öğr. Üyesi ESRA ULUSOY MUTLUOL →
Makale Bilgileri
ISSN25289659
Yayın TarihiMart 2021
Cilt / Sayfa34 · 2-9
Scopus ID2-s2.0-85103645443
Erişim🔓 Açık Erişim
Özet
Objective: 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.
Yazarlar (3)
1
Hatice Kök
ORCID: 0000-0002-5874-9474
2
Mehmet Said Izgi
3
Ayşe Merve Acilar
ORCID: 0000-0002-0133-2694
Anahtar Kelimeler
Artificial intelligence
Bone age measurement
Cephalometry
Cervical vertebrae
Kurumlar
Necmettin Erbakan Üniversitesi
Meram Turkey
Private Practice
Istanbul Turkey
Selçuk Üniversitesi
Selçuklu Turkey
Scimago Dergi (ISSN Eşleşmesi)
Turkish Journal of Orthodontics
Q2
OA
SJR Skoru0,409
H-Index14
YayıncıGalenos Publishing House
ÜlkeTurkey
Orthodontics (Q2)
Metrikler
21
Atıf
3
Yazar
4
Anahtar Kelime