Ortodontide yapay zeka ve makine öğrenimi

Yıl 2019, , 517 - 523, 01.12.2019

Süleyman Kutalmış Büyük , Sedanur Hatal

https://doi.org/10.21601/ortadogutipdergisi.547782

Öz

Amaç: Bilgi teknolojisinin ortodontide klinik kullanımı son yıllarda önemli ölçüde artmıştır. Bu sistematik derlemenin amacı, ortodonti alanındaki yapay zeka ve makine öğreniminin bilimsel bir analizini yapmaktır.
Gereç ve Yöntem: 25 Eylül 2018 tarihinde ortodonti yapay zeka ve makine öğrenimi hakkında elektronik arama ve el ile arama işlemleri gerçekleştirilmiştir.
Bulgular: Toplam 107 çalışma bulunmuştur. Dokuz çalışma, duplikasyon nedeniyle hariç tutulmuştur. Yazım dili İngilizce olmayan ve konuyla ilgisi olmayan makaleler hariç tutulduktan sonra, bu sistematik derleme için 23 tam metin makale incelenmiştir. Bu sistematik derlemeye 3 makale daha eklenmiştir. On iki otomatik sefalometrik işaret belirleme, 6 ortodontik tanı ve tedavi sonuçları, 2 ortodontik diş çekimi kararı, 3 yüz çekiciliği, 1 headgear seçimi, 1 dokunmatik sterilizasyon sistemi ve 1 otomatik iskelet yaşı tayini bu sistematik derlemede yer almıştır.
Sonuçlar: Yapay zeka ve makine öğrenimi, esas olarak otomatik sefalometrik nokta belirleme, yüz çekiciliği ve ortodontik amaç için diş çekimi kararlarının belirlenmesine odaklanmaktadır. Yapay zekanın ortodontide kullanılmasının klinik olarak daha doğru ve hızlı sonuçlar elde edilmesi açısından önem taşımaktadır.

Anahtar Kelimeler

yapay zeka, makine öğrenmesi, ortodonti

Artificial intelligence and machine learning in orthodontics

Öz

Objective: The clinical use of information technology in orthodontics has increased significantly in recent years. The aim of this systematic review is to perform a scientific analysis of artificial intelligence and machine learning in orthodontics.
Methods: An electronic search and manual search were performed on September 25, 2018 about using artificial intelligence and machine learning in orthodontics.
Results: A total of 107 studies were found. Nine studies were excluded because of duplication. After exclusion of all the irrelevant and non-English articles, 23 full-text articles remained to be included in this systematic review. 3 additional articles were included in this systematic review. Twelve automatic cephalometric landmark determination, 6 orthodontic diagnosis and treatment outcomes, 2 orthodontic tooth extraction decision, 3 facial attractiveness, 1 headgear selection, 1 touchless sterilisation system and 1 automatic skeletal age determination studies were included in this systematic review.
Conclusions: Artificial intelligence and machine learning are mainly focused on determination of automatic cephalometric points, facial attractiveness and tooth extraction decisions for orthodontic purposes. The use of artificial intelligence in orthodontics is important in terms of obtaining more accurate and rapid results clinically.

Anahtar Kelimeler

artificial intelligence, machine learning, orthodontics

Kaynakça

• Khanna S. Artificial intelligence: contemporary applications and future compass. Int Dent J 2010; 60: 269-72.
• Murata S, Lee C, Tanikawa C, Date S. (2017, October). Towards a Fully Automated Diagnostic System for Orthodontic Treatment in Dentistry. In e-Science (e-Science), 2017 IEEE 13th International Conference on (pp. 1-8). IEEE.
• Leonardi R, Giordano D, Maiorana F, Spampinato C. Automatic cephalometric analysis. Angle Orthod 2008; 78: 145-51.
• Gupta A, Kharbanda OP, Sardana V, Balachandran R, Sardana HK. A knowledge-based algorithm for automatic detection of cephalometric landmarks on CBCT images. Int J Comput Assist Radiol Surg 2015; 10: 1737-52.
• Montúfar J, Romero M, Scougall-Vilchis RJ. Automatic 3-dimensional cephalometric landmarking based on active shape models in related projections. Am J Orthod Dentofacial Orthop 2018; 153: 449-458.
• Montúfar J, Romero M, Scougall-Vilchis RJ. Hybrid approach for automatic cephalometric landmark annotation on cone-beam computed tomography volumes. Am J Orthod Dentofacial Orthop 2018; 154: 140-150.
• 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.
• Niño-Sandoval TC, Guevara Pérez SV, González FA, Jaque RA, Infante-Contreras C. Use of automated learning techniques for predicting mandibular morphology in skeletal class I, II and III. Forensic Sci Int 2017; 281: 187.e1-187.e7.
• Niño-Sandoval TC, Guevara Perez SV, González FA, Jaque RA, Infante-Contreras C. An automatic method for skeletal patterns classification using craniomaxillary variables on a Colombian population. Forensic Sci Int 2016; 261: 159.e1-6.
• Tanikawa C, Yamamoto T, Yagi M, Takada K. Automatic recognition of anatomic features on cephalograms of preadolescent children. Angle Orthod 2010; 80: 812-20.
• Chen YT, Cheng KS, Liu JK. Improving cephalogram analysis through feature subimage extraction. IEEE Eng Med Biol Mag 1999; 18: 25-31.
• Lévy-Mandel AD, Venetsanopoulos AN, Tsotsos JK. Knowledge-based landmarking of cephalograms. Comput Biomed Res 1986; 19: 282-309.
• Rueda S, Alcañiz M. An approach for the automatic cephalometric landmark detection using mathematical morphology and active appearance models. Med Image Comput Comput Assist Interv 2006; 9: 159-66.
• Tanikawa C, Yagi M, Takada K. Automated cephalometry: system performance reliability using landmark-dependent criteria. Angle Orthod 2009; 79: 1037-46.
• Banumathi A, Raju S, Abhaikumar V. Diagnosis of dental deformities in cephalometry images using support vector machine. J Med Syst 2011; 35: 113-9.
• Patcas R, Bernini DAJ, Volokitin A, Agustsson E, Rothe R, Timofte R. Applying artificial intelligence to assess the impact of orthognathic treatment on facial attractiveness and estimated age. Int J Oral Maxillofac Surg 2019; 48: 77-83.
• Yu X, Liu B, Pei Y, Xu T. Evaluation of facial attractiveness for patients with malocclusion: a machine-learning technique employing Procrustes. Angle Orthod 2014; 84: 410-6.
• Wang X, Cai B, Cao Y, Zhou C, Yang L, Liu R, Long X, Wang W, Gao D, Bao B. Objective method for evaluating orthodontic treatment from the lay perspective: An eye-tracking study. Am J Orthod Dentofacial Orthop 2016; 150: 601-610.
• Xie X, Wang L, Wang A. Artificial neural network modeling for deciding if extractions are necessary prior to orthodontic treatment. Angle Orthod 2010; 80: 262-6.
• Spampinato C, Palazzo S, Giordano D, Aldinucci M, Leonardi R. Deep learning for automated skeletal bone age assessment in X-ray images. Med Image Anal 2017; 36: 41-51.
• Auconi P, Scazzocchio M, Cozza P, McNamara JA Jr, Franchi L. Prediction of Class III treatment outcomes through orthodontic data mining. Eur J Orthod 2015; 37: 257-67.
• Auconi P, Caldarelli G, Scala A, Ierardo G, Polimeni A. A network approach to orthodontic diagnosis. Orthod Craniofac Res 2011; 14: 189-97.
• Yagi M, Ohno H, Takada K. Decision-making system for orthodontic treatment planning based on direct implementation of expertise knowledge. Conf Proc IEEE Eng Med Biol Soc 2010;2010:2894-7.
• Mario MC, Abe JM, Ortega NR, Del Santo M Jr. Paraconsistent artificial neural network as auxiliary in cephalometric diagnosis. Artif Organs 2010; 34: E215-21.
• Kim BM, Kang BY, Kim HG, Baek SH. Prognosis prediction for Class III malocclusion treatment by feature wrapping method. Angle Orthod 2009; 79: 683-91.
• Noroozi H. Orthodontic treatment planning software. Am J Orthod Dentofacial Orthop 2006; 129: 834-7.
• Wan Hassan WN, Abu Kassim NL, Jhawar A, Shurkri NM, Kamarul Baharin NA, Chan CS. User acceptance of a touchless sterile system to control virtual orthodontic study models. Am J Orthod Dentofacial Orthop 2016; 149: 567-78.
• Akçam MO, Takada K. Fuzzy modelling for selecting headgear types. Eur J Orthod 2002; 24: 99-106.