Hatice KÖK
(Ortodonti Anabilim Dalı, Selçuk Üniversitesi, Diş Hekimliği Fakültesi, Konya, Türkiye)
Mehmet Said İZGİ
(Özel Büro, İstanbul, Türkiye)
Ayşe Merve ACILAR
(Bilgisayar Mühendisliği Bölümü, Necmettin Erbakan Üniversitesi, Konya Mühendislik ve Mimarlık Fakültesi, Türkiye)
Yıl: 2021Cilt: 34Sayı: 1ISSN: 2528-9659 / 2148-9505Sayfa Aralığı: 2 - 9İngilizce

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Evaluation of the Artificial Neural Network and Naive Bayes Models Trained with Vertebra Ratios for Growth and Development Determination
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 deter- mination 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
DergiAraştırma MakalesiErişime Açık
  • 1. Abdel-Kader HM. The Reliability of Dental X-ray film in assessment of MP3 stages of the pubertal growth spurt. Am J Orthod Dentofa- cial Orthop 1998; 114: 427-9. [Crossref]
  • 2. Mendes YBE, Bergmann JR, Pellissari MF, Hilgenberg SP, Coelho U. Analysis of skeletal maturation in patients aged 13 to 20 years by means of hand wrist radiographs. Dental Press J Orthod 2010; 15: 74-9. [Crossref]
  • 3. Kumar V, Hegde SK, Bhat SS. The relationship between dental age, bone age and chronological age in children with short stature. J Contemp Dent Pract 2011; 2.
  • 4. Baccetti T, Franchi L, McNamara JA Jr. An improved version of the cervical vertebral maturation (CVM) method for the assessment of mandibular growth. Angle Orthod. 2002; 2: 316-23.
  • 5. Franchi L, Baccetti T, De Toffol L, Polimeni A, Cozza P. Phases of the dentition for the assessment of skeletal maturity: A diagnostic per- formance study. Am J Orthod Dentofacial Orthop 2008; 133: 395- 400. [Crossref]
  • 6. Ioi H, Nakata S, Nakasima A. Comparasion of cephalometric norms between Japanese and Caucasian adults in antero-pos- terior and vertical dimension. Eur J Orthod. 2007; 29: 493-99. [Crossref]
  • 7. Panchbhai AS. Dental radiographic indicators, a key to age estima- tion. Dentomaxillofac Radiol 2011; 40: 199-212. [Crossref] 8. Greulich WW, Pyle SI. Radiographic atlas of skeletal development of hand and wrist. Stanford University Press. 1959.
  • 9. Lamparski DG. Skeletal age assessment utilizing cervical vertebrae. Am J Orthod. 1975; 67: 458-59. [Crossref]
  • 10. Hassel B, Farman AG. Skeletal maturation evaluation using cervi- cal vertebrae. Am J Orthod Dentofacial Orthop 1995; 107: 58-66. [Crossref]
  • 11. Tanner J, Oshman D, Bahhage F, Healy M. Tanner-Whitehouse bone age reference values for North American children. J Pediatr 1997; 131: 34-40. [Crossref]
  • 12. Tanner JM, Whitehouse RH, Cameron N, Marshall WA, Healy MJR, Goldstein H. Assessment of skeletal maturity and prediction of adult height (TW2 method). London, Saunders. 2001.
  • 13. Demirjian A, Goldstein H. New systems for dental maturity based on seven and four teeth. Ann Hum Biol. 1976; 3:411–421.
  • 14. Flores-Mir C, Nebbe B, Major PW. Use of skeletal maturation based on hand-wrist radiographic analysis as a predictor of facial growth: A systematic review. Angle Orthod. 2004; 74: 118-24.
  • 15. Chen L, Liu J, Xu T, Long X, Lin J. Quantitative skeletal evaluation based on cervical vertebral maturation: a longitudinal study of ad- olescents with normal occlusion. Int J Oral Maxillofac Surg 2010; 39: 653-9. [Crossref]
  • 16. Fudalej P, Bollen AM. Effectiveness of the cervical vertebral matura- tion method to predict postpeak circumpubertal growth of cranio- facial structures. Am J Orthod Dentofacial Orthop. 2010; 137: 59-65. [Crossref]
  • 17. Grave K, Townsend G. Cervical vertebral maturation as a predictor of the adolescent growth spurt. Aust Orthod J 2003; 19: 25-32.
  • 18. Gabriel DB, Southard KA, Qian F, Marshall SD, Franciscus RG, South- ard TE. Cervical vertebrae maturation method: poor reproducibility. Am J Orthod Dentofacial Orthop. 2009; 136: 478.e1-7 [Crossref]
  • 19. Zhao XG, Lin J, Jiang JH, Wang Q, Ng SH. Validity and reliability of a method for assessment of cervical vertebral maturation. Angle Or- thod 2012; 82: 229-34 [Crossref]
  • 20. Predko-Engel A, Kaminek M, Langova K, Kowalski P, Fudalej PS. Re- liability of the cervical vertebrae maturation (CVM) method. Bratisl Lek Listy. 2015; 116: 222-6. [Crossref]
  • 21. Nabiyev VV. Yapay Zeka, Seçkin Yayıncılık Sanayi ve Ticaret. AŞ. An- kara. 2003.p.25
  • 22. Yasav M. Yapay sinir ağlarıyla yüz mimiklerinin tanınması. YTU. Fen Bilimleri Enstitüsü, Yüksek Lisans Tezi. 2008.
  • 23. Fausett L. Fundamentals of Neural Networks: Architectures, Algo- rithms and Applications. Prentice Hall. United State. 1994.p.3
  • 24. Raith S, Vogel EP, Anees N, Keul C, Güth JF, Edelhoff D, et al. Artificial Neural Networks as a powerful numerical tool to classify specific features of a tooth based on 3D scan data. Comput Biol Med 2017; 80: 65-76. [Crossref]
  • 25. Xie X, Wang L, Wang A. Artificial neural network modeling for de - ciding if extractions are necessary prior to orthodontic treatment. Angle Orthod 2010; 80: 262-6. [Crossref]
  • 26. Larson DB, Chen MC, Lungren MP, Halabi SS, Stence NV, Langlotz CP. Performance of a deep-learning neural network model in assessing skeletal maturity on pediatric hand radiographs. Radiology. 2018; 287: 313-22. [Crossref]
  • 27. Wang S, Shen Y, Shi C, Yin P, Wang Z, Cheung PW,et al. Skeletal matu- rity recognition using a fully automated system with convolutional neural networks. IEEE Access 2018; 6: 29979-93. [Crossref]
  • 28. Kök H, Acilar AM, İzgi MS. Usage and comparison of artificial intel- ligence algorithms for determination of growth and development by cervical vertebrae stages in orthodontics. Prog Orthod 2019; 20: 41. [Crossref]
  • 29. Amasya H, Yıldırım D, Aydogan T, Kemaloglu N, Orhan, K. Cervical vertebral maturation assessment on lateral cephalometric radio- graphs using artificial intelligence: Comparison of machine learn- ing classifier models. Dentomaxillofac Radiol 2020; 49: 20190441. [Crossref]
  • 30. Hassel B, Farman AG. Skeletal maturation evaluation using cervi- cal vertebrae. Am J Orthod Dentofacial Orthop 1995; 107: 58-66. [Crossref]
  • 31. Gandini P, Mancini M, Andreani F. Comparison of hand-wrist bone and cervical vertebral analyses in measuring skeletal maturation. Angle Orthod 2006; 76: 984-9. [Crossref]
  • 32. Wong RW, Alkhal HA, Rabie AB. Use of cervical vertebral maturation to determine skeletal age. Am J Orthod Dentofacial Orthop 2009; 136: 484.e1-6. [Crossref]
  • 33. Mito T, Sato K, Mitani H. Cervical vertebral bone age in girls. Am J Orthod Dentofacial Orthop 2002; 122: 380-5. [Crossref]
  • 34. Alhadlaq AM, Al-Maflehi NS. New model for cervical vertebral bone age estimation in boys. JKUDS 2013; 4: 1-5. [Crossref]
  • 35. Beit P, Peltomaki T, Schatzle M, Signorelli L, Patcas R. Evaluating the agreement of skeletal age assessment based on hand-wrist and cervical vertebrae radiography. Am J Orthod Dentofacial Orthop 2013; 144: 838-47. [Crossref]
  • 36. Baptista RS, Quaglio CL, Mourad LM, Hummel AD, Caetano CA, Or- tolani CLF, et al. A semi-automated method for bone age assess - ment using cervical vertebral maturation. Angle Orthod 2012; 82: 658-62 [Crossref]
  • 37. Öztemel E. Yapay Sinir Ağları, Papatya Yayıncılık, İstanbul. 2003. 38. Jung SK, Kim TW. New approach for the diagnosis of extractions with neural network machine learning, Am J Orthod Dentofacial Orthop 2016; 149: 127-33 [Crossref]
  • 39. Haykin S. Neural networks and learning machines. Prentice Hall, Inc., United States. 2009.p.1-46, 157-201.
  • 40. Umut I. Naïve Bayes. Available from: http://www.psgminer.com/ help/naive_bayes__.htm
  • 41. Al-Aidaroos KM, Bakar AA, Othman Z. Medical data classifica - tion with Naive Bayes approach. Inform Technol J 2012; 11: 1166. [Crossref]
  • 42. Moraes DR, Casati JPB, Rodrigues ELL. Analysis of polynomial be - havior of the C3 cervical concavity to bone age estimation using artificial neural networks. In 2013 ISSNIP Biosignals and Biorobot- ics Conference: Biosignals and Robotics for Better and Safer Living (BRC). IEEE 2013; 1-6. [Crossref]
  • 43. Santiago RC, Cunha AR, Júnior GC, Fernandes N, Campos MJS, Cos- ta LFM, et al. New software for cervical vertebral geometry assess- ment and its relationship to skeletal maturation-a pilot study. Den- tomaxillof Radiol 2014; 43: 20130238. [Crossref]

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