Artificial Intelligence In Dentistry

Yıl 2021, Cilt: 1 Sayı: 2, 26 - 33, 18.08.2021

Hande Sağlam , Tuğba Arı , İbrahim Şevki Bayrakdar , Elif Bilgir , Mehmet Uğurlu , Özer Çelik , Kaan Orhan

Öz

Diş Hekimliğinde Yapay Zeka

Öz

Teknolojik anlamdaki değişiklikler tıp ve diş hekimliği alanında büyük değişimler yaratmıştır. Bu değişime sebep olan en önemli yeniliklerden biri de yapay zekâ teknolojisidir. Tıp ve diş hekimliği alanında hasta sağlık hizmetlerine önemli katkıları ve hekimlere sağladığı kolaylıklar sayesinde gittikçe daha çok tercih edileceği düşünülmektedir. İşlem hızındaki artış, hesaplama gücü, depolama kapasitesi, farklı görevleri yerine getirme yeteneği ve gelişmiş grafik işlem birimleri ve bilgisayarların satın alınabilirliği ile tıpta ve özellikle radyolojide yeni bir dönemin başlangıcı kabul edilmektedir Diş hekimliği alanında da başlayan bu yeni dönem, hastalıkların erken teşhisinin yapılması ve önlenmesinde büyük katkı ortaya koyacaktır. Bu derlemenin amacı yaşadığımız dönem ve gelecek için son derece önemli bir noktada olan yapay zekâ teknolojisinin diş hekimliği alanındaki uygulamalarını anlatmaktır.

Anahtar Kelimeler

yapay zeka, diş hekimliği, sinir ağları, evrişimli sinir ağları, derin öğrenme

Kaynakça

• 1.Russell S, Norvig P. Artificial Intelligence-A Modern Approach 3rd ed. M. Hirsch, ed. New Jersey: Pearson Education, Inc; 2010,
• 2.Wong S, Al-Hasani H, Alam Z, Alam A. Artificial intelligence in radiology: how will we be affected? European radiology. 2019;29(1):141-3.
• 3.Khanna SS, Dhaimade PA. Artificial intelligence: transforming dentistry today. Indian J Basic Appl Med Res 2017;6(3):161-7.
• 4.Feeney L, Reynolds P, Eaton K, Harper J. A description of the new technologies used in transforming dental education. British Dental Journal. 2008;204(1): 19-28
• 5.Shan T, Tay F, Gu L. Application of Artificial Intelligence in Dentistry. Journal of dental research 2020:00220345209691 15.
• 6.Salagare S, Prasad R. An overview of internet of dental things: new frontier in advanced dentistry. Wireless Personal Communications. 2020;110(3): 1345-71.
• 7.Moor J. The Dartmouth College artificial intelligence conference: The next fifty years. Ai Magazine 2006;27(4):87-.
• 8.Khanagar SB, Al-Ehaideb A, Maganur PC, Vishwanathaiah S, Patil S, Baeshen HA, et al Developments, application, and performance of artificial intelligence in dentistryA systematic review. Journal of dental sciences, 2020,
• 9.Kositbowornchai S, Siriteptawee S, Plermkamon S, Bureerat S, Chetchotsak D. An artificial neural network for Sağlam et al.
• 10.Khajanchi A. Artificial neural networks: the next intelligence. USC, Technology Commercalization Alliance usc edu/org/techalliance/Anthology2003/Final_Khajanch pdf. 2003.
• 11.Brickley M, Shepherd J, Armstrong R. Neural networks: a new technique for development of decision support systems in dentistry. Journal of dentistry. 1998;26(4):305-9.
• 12.Hwang J-J, Jung Y-H, Cho B-H, Heo M-S. An overview of deep learning in the field of dentistry. Imaging science in dentistry. 2019;49(1):1.
• 13.Burt JR, Torosdagli N, Khosravan N, RaviPrakash H, Mortazi A, Tissavirasingham F, et al. Deep learning beyond cats and dogs: recent advances in diagnosing breast cancer with deep neural networks. The British journal of radiology. 2018;91(1089):20170545.
• 14. Corbella S, Srinivas S, Cabitza F. Applications of deep learning in dentistry. Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology. 2020.
• 15.White SC, Pharoah MJ. Oral radiology-E-Book: Principles and interpretation: Elsevier Health Sciences; 2014.
• 16.Katne T, Kanaparthi A, Srikanth Gotoor S, Muppirala S, Devaraju R, Gantala R. Artificial intelligence: demystifying dentistrythe future and beyond. Int J Contemp Med Surg Radiol. 2019;4:D6-D9.
• 17.Krois J, Ekert T, Meinhold L, Golla T, Kharbot B, Wittemeier A, et al. Deep learning for the radiographic detection of periodontal bone loss. Scientific reports. 2019;9(1):1-6.
• 18.Davies A, Mannocci F, Mitchell P, Andiappan M, Patel S. The detection of periapical pathoses in root filled teeth using single and parallax periapical radiographs versus cone beam computed tomographya clinical study. International endodontic journal. 2015;48(6):582-92.
• 19.Chen H, Zhang K, Lyu P, Li H, Zhang L, Wu J, et al. A deep learning approach to automatic teeth detection and numbering based on object detection in dental periapical films. Scientific reports. 2019;9(1):1-11.
• 20. Tuzoff DV, Tuzova LN, Bornstein MM, Krasnov AS, Kharchenko MA, Nikolenko SI, et al. Tooth detection and numbering in panoramic radiographs using convolutional neural networks. Dentomaxillofacial Radiology. 2019;48(4):20180051.
• 21.Orhan K, Bayrakdar I, Ezhov M, Kravtsov A, Özyürek T. Evaluation of artificial intelligence for detecting periapical pathosis on cone-beam computed tomography scans. International endodontic journal. 2020;53(5):680-9.
• 22.Kılıc MC, Bayrakdar IS, Çelik Ö, Bilgir E, Orhan K, Aydın OB, et al. Artificial intelligence system for automatic deciduous tooth detection and numbering in panoramic radiographs. Dentomaxillofacial Radiology. 2021;50:20200172.
• 23.Lee J-H, Han S-S, Kim YH, Lee C, Kim I. Application of a fully deep convolutional neural network to the automation of tooth segmentation on panoramic radiographs. Oral surgery, oral medicine, oral pathology and oral radiology. 2020; 129(6):635-42.
• 24.Leite AF, Van Gerven A, Willems H, Beznik T, Lahoud P, Gaêta-Araujo H, et al. Artificial intelligence- driven novel tool for tooth detection and segmentation on panoramic radiographs. Clinical Oral Investigations. 2020:1-11.
• 25.Hiraiwa T, Ariji Y, Fukuda M, Kise Y, Nakata K, Katsumata A, et al. A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography. Dentomaxillofacial Radiology. 2019;48(3):20180218.
• 26.Li Q, Chen K, Han L, Zhuang Y, Li J, Lin J. Automatic tooth roots segmentation of cone beam computed tomography image sequences using U-net and RNN. Journal of X-Ray Science and Technology. 2020;28(5):905- 22.
• 27.Fukuda M, Inamoto K, Shibata N, Ariji Y, Yanashita Y, Kutsuna S, et al. Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic
• radiography. Oral radiology. 2019:1-7.
• 28.Kuwada C, Ariji Y, Fukuda M, Kise Y, Fujita H, Katsumata A, et al. Deep learning systems for detecting and classifying the presence of impacted supernumerary teeth in the maxillary incisor region on panoramic radiographs. Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology. 2020;130(4):464-9.
• 29.Kise Y, Ikeda H, Fujii T, Fukuda M, Ariji Y, Fujita H, et al. Preliminary study on the application of deep learning system to diagnosis of Sjögren's syndrome on CT images. Dentomaxillofacial Radiology. 2019;48(6):20190019.
• 30. Ariji Y, Sugita Y, Nagao T, Nakayama A, Fukuda M, Kise Y, et al. CT evaluation of extranodal extension of cervical lymph node metastases in patients with oral
• squamous cell carcinoma using deep learning classification. Oral radiology. 2020;36(2):148-55. 31.Lee J-S, Adhikari S, Liu L, Jeong H-G, Kim H, Yoon S-
• J. Osteoporosis detection in panoramic radiographs using a deep convolutional neural network-based computer- assisted diagnosis system: a preliminary study. Dentomaxillofacial Radiology. 2019;48(1):20170344.
• 32.Lee K-S, Jung S-K, Ryu J-J, Shin S-W, Choi J. Evaluation of transfer learning with deep convolutional neural networks for screening osteoporosis in dental panoramic radiographs. Journal of clinical medicine. 2020;9(2):392.
• 33.Xie X, Wang L, Wang A. Artificial neural network modeling for deciding if extractions are necessary prior to orthodontic treatment. The Angle orthodontist. 2010;80(2):262-6.
• 34.Birnbaum NS, Aaronson HB. Dental impressions using 3D digital scanners: virtual becomes reality. Compend contin educ dent. 2008;29(8):494-6.
• 35.Mackin N, Sims-Williams J, Stephens C. Artificial intelligence in the dental surgery: an orthodontic expert system, a dental tool of tomorrow. Dental update. 1991;18(8):341-3.
• 36.Jung S-K, Kim T-W. New approach for the diagnosis of extractions with neural network machine learning. American Journal of Orthodontics and Dentofacial Orthopedics. 2016;149(1):127-33.
• 37.Kunz F, Stellzig-Eisenhauer A, Zeman F, Boldt J. Artificial intelligence in orthodontics. Journal of Orofacial Orthopedics/Fortschritte der Kieferorthopädie. 2020;81(1):52-68.38.Hwang H-W, Park J-H, Moon J-H, Yu Y, Kim H, Her S-B, et al. Automated identification of cephalometric landmarks: Part 2-Might it be better than human? The Angle Orthodontist. 2020;90(1):69-76.
• 39.Yu H, Cho S, Kim M, Kim W, Kim J, Choi J. Automated skeletal classification with lateral cephalometry based on artificial intelligence. Journal of dental research. 2020;99(3):249-56.
• 40.Choi H-I, Jung S-K, Baek S-H, Lim WH, Ahn S-J, Yang I-H, et al. Artificial intelligent model with neural network machine learning for the diagnosis of orthognathic surgery. Journal of Craniofacial Surgery. 2019;30(7):1986-9.
• 41.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. The Angle Orthodontist. 2004;74(1):118-24.
• 42.Kök H, Acilar AM, İzgi MS. Usage and comparison of artificial intelligence algorithms for determination of growth and development by cervical vertebrae stages in orthodontics. Progress in orthodontics. 2019;20(1):1-10.
• 43.Ruppin J, Popovic A, Strauss M, Spüntrup E, Steiner A, Stoll C. Evaluation of the accuracy of three different computer-aided surgery systems in dental implantology: optical tracking vs. stereolithographic splint systems. Clinical oral implants research. 2008;19(7):709-16.
• 44. Widmann G. Image-guided surgery and medical robotics in the cranial area. Biomedical imaging and intervention journal. 2007;3(1).
• 45.Zhang W, Li J, Li Z-B, Li Z. Predicting postoperative facial swelling following impacted mandibular third molars extraction by using artificial neural networks evaluation. Scientific reports. 2018;8(1):1-9.
• 46.Orhan K, Bilgir E, Bayrakdar IS, Ezhov M, Gusarev M, Shumilov E. Evaluation Of Artificial Intelligence For Detecting Impacted Third Molars On Cone-Beam Computed Tomography Scans. Journal of Stomatology, Oral and Maxillofacial Surgery. 2020.
• 47. Vinayahalingam S, Xi T, Bergé S, Maal T, de Jong G. Automated detection of third molars and mandibular nerve by deep learning. Scientific reports. 2019;9(1):1-7.
• 48. Yoo J-H, Yeom H-G, Shin W, Yun JP, Lee JH, Jeong SH, et al. Deep learning based prediction of extraction difficulty for mandibular third molars. Scientific Reports. 2021;11(1):1-9.
• 49.Le Y, Verdonschot E. Performance of diagnostic systems in occlusal caries detection compared. Community Dentistry and Oral Epidemiology. 1994;22(3):187-91.
• 50.Pine CM, Jaap J. Dynamics of and diagnostic methods for detecting small carious lesions. Caries research. 1996;30(6):381-8.
• 51.Ten Cate J. What dental diseases are we facing in the new millennium: some aspects of the research agenda. Caries research. 2001;35(Suppl. 1):2-5.
• 52.Akpata E, Farid M, Al-Saif K, Roberts E. Cavitation at radiolucent areas on proximal surfaces of posterior teeth. Caries research. 1996;30(5):313-6.
• 53.Heaven T, Weems R, Firestone A. The use of a computer-based image analysis program for the diagnosis approximal caries from bitewing radiographs. Caries research. 1994;28(1):55-8.
• 54.Duncan RC, Heaven T, Weems Ra, Firestone Ar, Greer Df, Patel Jr. Using computers to diagnose and plan treatment of approval caries detected in radiographs. The Journal of the American Dental Association. 1995;126(7):873-82.
• 55. Wenzel A. Computer-automated caries detection in digital bitewings: consistency of a program and its influence on observer agreement. Caries research. 2001;35(1):12-20.
• 56.Devito KL, de Souza Barbosa F, Felippe Filho WN. An artificial multilayer perceptron neural network for diagnosis of proximal dental caries. Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology, and Endodontology. 2008;106(6):879-84.
• 57.Schwendicke F, Rossi J, Göstemeyer G, Elhennawy K, Cantu A, Gaudin R, et al. Cost-effectiveness of Artificial Intelligence for Proximal Caries Detection. Journal of Dental Research. 2020:0022034520972335.
• 58.Cantu AG, Gehrung S, Krois J, Chaurasia A, Rossi JG, Gaudin R, et al. Detecting caries lesions of different radiographic extension on bitewings using deep learning. Journal of Dentistry. 2020; 100:103425.
• 59.Lee J-H, Kim D-H, Jeong S-N, Choi S-H. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. Journal of dentistry. 2018;77:106-11.
• 60.Lang NP, Adler R, Joss A, Nyman S. Absence of bleeding on probing an indicator of periodontal stability. Journal of clinical periodontology. 1990;17(10):714-21.
• 61.Fadel HT, Abu-Hammad O, Ghulam OA, Dar-Odeh N. Are Artificial Neural Networks Useful for Predicting Overhanging Dental Restorations? A Cross-sectional Study. World. 2020;11(2):100.
• 62. Vera V, Corchado E, Redondo R, Sedano J, Garcia AE. Applying soft computing techniques to optimise a dental milling process. Neurocomputing. 2013;109:94-104.
• 63.Raith S, Vogel EP, Anees N, Keul C, Güth J-F, Edelhoff D, et al. 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. 2017:80:65-76.
• 64. Vecsei B, Joós-Kovács G, Borbély J, Hermann P. Comparison of the accuracy of direct and indirect three- dimensional digitizing processes for CAD/CAM systemsan in vitro study. Journal of prosthodontic research. 2017;61(2):177-84.
• 65.Kikuchi H, Ikeda M, Araki K. Evaluation of a virtual reality simulation system for porcelain fused to metal crown preparation at Tokyo Medical and Dental University. Journal of dental education. 2013;77(6):782-92.
• 66. Toshihito T, Kazunori N, Tomoya G, Tomoaki M, Kazunori I. Deep learning-based detection of dental prostheses and restorations. Scientific Reports (Nature Publisher Group). 2021;11(1).
• 67.Lee J-H, Lee J-S, Choi J-K, Kweon H-I, Kim Y-T, Choi S-H. National dental policies and socio-demographic factors affecting changes in the incidence of periodontal treatments in Korean: a nationwide population-based retrospective cohort study from 20022013. BMC Oral Health. 2016;16(1):1-9.
• 68. Furman E, Jasinevicius TR, Bissada NF, Victoroff KZ, Skillicorn R, Buchner M. Virtual reality distraction for pain control during periodontal scaling and root planing procedures. The Journal of the American Dental Association. 2009;140(12):1508-16.
• 69.Sohmura T, Kusumoto N, Otani T, Yamada S. Wakabayashi K, Yatani H. CAD/CAM fabrication and clinical application of surgical template and bone model in oral implant surgery. Clinical oral implants research. 2009;20(1):87-93.
• 70.Lee J-H, Kim D-h, Jeong S-N, Choi S-H. Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm. Journal of periodontal & implant science. 2018;48(2):114.
• 71.Alalharith DM, Alharthi HM, Alghamdi WM, Alsenbel YM, Aslam N, Khan IU, et al. A Deep Learning- Based Approach for the Detection of Early Signs of Gingivitis in Orthodontic Patients Using Faster Region- Based Convolutional Neural Networks. International Journal of Environmental Research and Public Health. 2020;17(22):8447.
• 72.Lim K, Moles D, Downer M, Speight P. Opportunistic screening for oral cancer and precancer in general dental practice: results of a demonstration study. British dental journal. 2003;194(9):497-502.
• 73. Rosmai MD, Sameemii AK, Basir A, Mazlipahiv IS, Norzaidi M. The use of artificial intelligence to identify people at risk of oral cancer: empirical evidence in Malaysian University. International Journal of Scientific Research in Education. 2010;3(1):10-20.
• 74.Ilhan B, Lin K, Guneri P, Wilder-Smith P. Improving oral cancer outcomes with imaging and artificial intelligence. Journal of dental research. 2020;99(3):241-8.
• 75.Kar A, Wreesmann VB, Shwetha V, Thakur S, Rao VU, Arakeri G, et al. Improvement of oral cancer screening quality and reach: The promise of Artificial Intelligence. Journal of Oral Pathology & Medicine. 2020;49(8):727-30.
• 76.Ariji Y, Fukuda M, Kise Y, Nozawa M, Yanashita Y, Fujita H, et al. Contrast-enhanced computed tomography image assessment of cervical lymph node metastasis in patients with oral cancer by using a deep learning system of artificial intelligence. Oral surgery, oral medicine, oral pathology and oral radiology. 2019;127(5):458-63.
• 77.De Tobel J, Radesh P, Vandermeulen D, Thevissen PW. An automated technique to stage lower third molar development on panoramic radiographs for age estimation: a pilot study. The Journal of forensic odonto-stomatology. 2017;35(2):42.
• 78.Patil V, Vineetha R, Vatsa S, Shetty DK, Raju A, Naik N, et al. Artificial neural network for gender determination using mandibular morphometric parameters: A comparative retrospective study. Cogent Engineering. 2020;7(1): 1723783.
• 79.Niño-Sandoval TC, Pérez SVG, González FA, Jaque RA, Infante-Contreras C. Use of automated learning techniques for predicting mandibular morphology in skeletal class I, II and III. Forensic science international. 2017;281:187.el-.e7.