Artificial Neural Networks Can be Used as Alternative Method to Estimate Loss Tooth Root Sizes for Prediction of Dental Implants

Yıl 2017, Cilt: 38 Sayı: 2, 385 - 395, 25.04.2017

Oğuzhan Görler Serkan Akkoyun

https://doi.org/10.17776/cumuscij.304902

Cited By: 1

Öz

The aim of this study was to investigate the
feasibility of estimation of canine root length and cervical width by an
artificial neural network method with an appropriate setting. We randomly
obtained 120 representative samples of routine panoramic radiographs by
computer tomography (CT). Of 120 samples, 96 (80%) were used in training phase
and 24 (20%) were used in test phase after a randomized selection. The intertuberal length (IL) of maxilla and canine root length (RL) and
canine root cervical width (CW) of the right canine tooth was measured and was
entered to a datum file. According to the results, the method is
convenient with this purpose. The mean square error values are lied between 2% and 4.4% for the estimations. This shows
that ANN is an alternative method for the prediction of canine root length and
cervical width. Since an ANN software, as a cost-effective tool that can be
purchased and after a training procedure that can be easily performed by an
experienced person, the ANN system can have a place in implant surgery after
further studies and optimization of software setting. An ANN software can be adapted to the dental implant surgery after
proper setting and training procedures that can be easily performed by an
experienced person and it can have a place in implant surgery after further
expanded studies and optimization of software setting.

Anahtar Kelimeler

Neural Network, Dental Implant, Canine, Dentistry

Dental İmplantlara Alternatif Değerlendirme Metodu Olarak Kayıp Dişlerin Boyutlarının Yapay Sinir Ağı Yöntemiyle Tahmini

Öz

Bu çalışmanın amacı, kanin diş kökü uzunluğu ve
servikal genişliğinin yapay bir sinir ağı yöntemi (ANN) ile tahmin edilmesinin
fizibilitesini araştırmaktır. Bilgisayarlı tomografilerin (BT) panoramic
görüntüleri ile rasgele 120 hasta değerlendirildi. 120 örnekten 96'sı (% 80)
eğitim fazında, 24'ü (% 20) randomize bir seçimden sonra test aşamasında
kullanıldı. Sağ maksiller kanin kök uzunluğu (RL), servikal genişliği (CW) ve intertuberal
uzunluk (IL) ölçülerek bir veri dosyasına oluşturuldu. Sonuçlara göre, yöntem
bu amaca uygun bulundu. Tahminler için ortalama karesel hata değerleri %2 ile
%4.4 arasında bulundu. Bu ANN yönteminin kanin kök boyu ve servikal genişliği
için alternatif bir yöntem olduğunu göstermektedir. Satın alınabilecek uygun
maliyetli bir araç olan ANN yazılımı ve sistemi, daha ileri çalışmalar ve
yazılım ayarlarının optimizasyonundan sonra dental implant ameliyatına adapte
edilebilir, implant ameliyatlarında implantların boy ve genişlikleri için
öngörü imkanı verebilir. 

Anahtar Kelimeler

Yapay Sinir Ağı Yöntemi, Dental İmplant, Kanin, Diş Hekimliği

Kaynakça

• [1]. B K Biwa’s, S Bag, S Pal, Bıomechanıcal Analysıs Of Normal And Implanted Tooth Usıng Bıtıng Force Measurement. International Journal of Engineering and Applied Sciences, August 2013. Vol. 4, No. 2.
• [2]. Nisand D, Renouard F. Short implant in limited bone volume. Periodontal 2000. 2014 Oct;66(1):72-96.
• [3]. Wen B, Chen J, Dard M, Cai Z. The Performance of Titanium-Zirconium Implants in the Elderly: A Biomechanical Comparative Study in the Minipig. Clin Implant Dent Relat Res. 2016 Dec;18(6):1200-1209.
• [4]. Esposito M, Hirsch J-M, Lekholm U, Thomsen P. Biological factors contributing to failures of osseointegrated oral implants (II) Etiopathogenesis. European Journal of Oral Sciences 1998;106: 721 – 64.
• [5]. Sahin S, Cehreli MC, Yalçin E. The influence of functional forces on the biomechanics of implant-supported prostheses--a review. J Dent. 2002 Sep-Nov;30(7-8):271-82.
• [6]. Filippo Amato, Alberto López, Eladia María Peña-Méndez, Petr Vaňhara, Aleš Hamp, Josef Havel, Artificial neural networks in medical diagnosis. J Appl Biomed. 11: 47–58, 2013.
• [7]. Stefan Raitha, Eric Per Vogela, Naeema Aneesa, Christine Keulb, Jan-Frederik Güthb, Daniel Edelhoffb, Horst Fischera, Artificial Neural Networks as a powerful numerical tool to classify specific features of a tooth based on 3D scan data. Computers in Biology and Medicine 80 (2017) 65–76.
• [8]. Suwadee Kositbowornchai, Sanphet Siriteptawee, Supattra Plermkamon, Sujin Bureerat, Danaipong Chetchotsak, An artificial neural network for detection of simulated dental caries, Int J CARS (2006) 1:91–96.
• [9]. Leyla Sadighpour, Susan Mir Mohammad Rezaei, Mojgan Paknejad, Fatemeh Jafaryand Pooya Aslani, The Application of an Artificial Neural Network to Support Decision Making in Edentulous Maxillary Implant Prostheses, Journal of Research and Practice in Dentistry, Vol. 2014 (2014), Article ID 369025, 10 pages.
• [10]. Karina Lopes Devito, Flávio de Souza Barbosa, and Waldir Neme Felippe Filho, An artificial multilayer perceptron neural network for diagnosis of proximal dental caries, Oral Surg Oral Med Oral Pathol Oral Radiol Endod 2008;106:879-884
• [11]. Georgios Papantonopoulos, Keiso Takahashi, Tasos Bountis, Bruno G. Loos, Artificial Neural Networks for the Diagnosis of Aggressive Periodontitis Trained by Immunologic Parameters, PLoS ONE 9(3), 2014, e89757.
• [12]. Ali Al Haidan, Osama Abu-Hammad, and Najla Dar-Odeh, Predicting Tooth Surface Loss Using Genetic Algorithms-Optimized Artificial Neural Networks, Computational and Mathematical Methods in Medicine Volume 2014, Article ID 106236, 7 pages.
• [13]. Haykin S. Neural networks: a comprehensive foundation. 2nd ed. New Jersey: Prentice-Hall; 1999.
• [14]. Hornik K, Stinchcombe M, White H. Multilayer feedforward networks are universal approximator. Neural Networks 1989;2:359–66.
• [15]. Neurosolutions, http://www.neurosolutions.com/.
• [16]. Levenberg KA. Method for the solution of certain non-linear problems in least squares. Q Appl Math 1944;2:164–8.
• [17]. Marquardt D. An algorithm for least-squares estimation of nonlinear parameters. SIAM J Appl Math 1963;11:431–41.
• [18]. Laura Gaviria, John Paul Salcido, Teja Guda, and Joo L. Ong. Current trends in dental implants. J Korean Assoc Oral Maxillofac Surg. 2014 Apr; 40(2): 50–60.
• [19]. Esposito M, Ardebili Y, Worthington HV.Interventions for replacing missing teeth: different types of dental implants. Cochrane Database Syst Rev. 2014 Jul 22;(7):CD003815.
• [20]. Deeb G, Antonos L, Tack S, Carrico C, Laskin D, Deeb JG. Is Cone Beam Computed Tomography Always Necessary for Dental Implant Placement? J Oral Maxillofac Surg. 2016 Nov 15. pii: S0278-2391(16)31172-7.
• [21]. Correa LR, Spin-Neto R, Stavropoulos A, Schropp L, da Silveira HE, Wenzel A. Planning of dental implant size with digital panoramic radiographs, CBCT-generated panoramic images, and CBCT cross-sectional images. Clin Oral Implants Res. 2014 Jun;25(6):690-5.
• [22]. Lofthag-Hansen S, Thilander-Klang A, Ekestubbe A, Helmrot E, Gröndahl K. Calculating effective dose on a cone beam computed tomography device: 3D Accuitomo and 3D Accuitomo FPD. Dentomaxillofac Radiol. 2008 Feb;37(2):72-9.
• [23]. Mesquita Júnior EJ, Vieta AI, Taba Júnior M, Faria PE. Correlation of radiographic analysis during initial planning and tactile perception during the placement of implants. Br J Oral Maxillofac Surg. 2016 Sep 12. pii: S0266-4356(16)30233-9.
• [24]. Cheng DC, Chen LW, Shen YW, Fuh LJ. Computer-assisted system on mandibular canal detection. Biomed Tech (Berl). 2016 Nov 18.
• [25]. Dau M, Edalatpour A, Schulze R, Al-Nawas B, Alshihri A, Kämmerer PW. Presurgical evaluation of bony implant sites using panoramic radiography and cone beam computed tomography - influence of medical education. Dentomaxillofac Radiol. 2016 Oct 19:20160081.
• [26]. Malina-Altzinger J, Damerau G, Grätz KW, Stadlinger PD. Evaluation of the maxillary sinus in panoramic radiography-a comparative study. Int J Implant Dent. 2015 Dec;1(1):17. Epub 2015 Jul 10.
• [27]. Wolff C, Mücke T, Wagenpfeil S, Kanatas A, Bissinger O, Deppe H. Do CBCT scans alter surgical treatment plans? Comparison of preoperative surgical diagnosis using panoramic versus cone-beam CT images. J Craniomaxillofac Surg. 2016 Oct;44(10):1700-1705.
• [28]. Jung YH, Cho BH. Assessment of maxillary third molars with panoramic radiography and cone-beam computed tomography. Imaging Sci Dent. 2015 Dec;45(4):233-40.
• [29]. Lopes LJ, Gamba TO, Bertinato JV, Freitas DQ. Comparison of panoramic radiography and CBCT to identify maxillary posterior roots invading the maxillary sinus. Dentomaxillofac Radiol. 2016;45(6):20160043.
• [30]. Val JE, Gómez-Moreno G, Ruiz-Linares M, Frutos JC, Gehrke SA, Calvo-Guirado JL. Effects of Surface Treatment Modification and Implant Design in Implants Placed Crestal and Subcrestally Applying Delayed Loading Protocol. J Craniofac Surg. 2016 Dec 14.
• [31]. Khojasteh A, Motamedian SR, Sharifzadeh N, Zadeh HH. The influence of initial alveolar ridge defect morphology on the outcome of implants in augmented atrophic posterior mandible: an exploratory retrospective study. Clin Oral Implants Res. 2016 Nov 2.
• [32]. Bucchi C, Borie E, Arias A, Dias FJ, Fuentes R. Radiopacity of alloplastic bone grafts measured with cone beam computed tomography: An analysis in rabbit calvaria. Bosn J Basic Med Sci. 2016 Nov 22.
• [33]. Chopra A, Mhapuskar AA, Marathe S, Nisa SU, Thopte S, Saddiwal R. Evaluation of Osseointegration in Implants using Digital Orthopantomogram and Cone Beam Computed Tomography. J Contemp Dent Pract. 2016 Nov 1;17(11):953-957.
• [34]. Varshowsaz M, Goorang S, Ehsani S, Azizi Z, Rahimian S. Comparison of Tissue Density in Hounsfield Units in Computed Tomography and Cone Beam Computed Tomography. J Dent (Tehran). 2016 Mar;13(2):108-115.
• [35]. Tadinada A, Jalali E, Al-Salman W, Jambhekar S, Katechia B, Almas K. Prevalence of bony septa, antral pathology, and dimensions of the maxillary sinus from a sinus augmentation perspective: A retrospective cone-beam computed tomography study. Imaging Sci Dent. 2016 Jun;46(2):109-15.
• [36]. Nart J, Barallat L, Jimenez D, Mestres J, Gómez A, Carrasco MA, Violant D, Ruíz-Magaz V. Radiographic and histological evaluation of deproteinized bovine bone mineral vs. deproteinized bovine bone mineral with 10% collagen in ridge preservation. A randomized controlled clinical trial. Clin Oral Implants Res. 2016 Jun 22.
• [37]. Umanjec-Korac S, Parsa A, Darvishan Nikoozad A, Wismeijer D, Hassan B. Accuracy of cone beam computed tomography in following simulated autogenous graft resorption in maxillary sinus augmentation procedure: an ex vivo study. Dentomaxillofac Radiol. 2016 May 26:20160092.