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Artıfıcıal Intellıgence in Oral Radıology

Yıl 2021, Cilt: 1 Sayı: 3, 78 - 83, 25.03.2022

Mehmet Emin Doğan Elif Meltem Aslan Öztürk

Öz

In the developing, changing and advancing technology, health and dentistry should also take its place.The entry of
artificial intelligence into dentistry, whose name we have heard often recently, has begun and progress has been
made.Considering that artificial intelligence facilitates the workflow that helps health and dentistry employees in their
work, it will be more preferred in the future and will enter our lives actively.The information we will obtain on this
subject will help us and enable us to reach diagnosis and diagnosis faster and more accurately.Oral radiologists have a
greater share in diagnosis, diagnosis and data storage in dentistry.For this reason, it is important for dentists and
especially oral radiologists to have an idea about artificial intelligence.The purpose of this review is to examine the
applications of artificial intelligence in dentistry, which has recently become a current issue, and to raise awareness
about this technology in dentists.

Anahtar Kelimeler

Artificial Intelligence Dentistry Cone Beam Computed Tomography

Kaynakça

  • 1.Russell SJ, Norvig P. Artificial Intelligence-A Modern Approach, Third Int. Edition. Pearson Education, Upper Saddle River, NJ, USA; 2010.
  • 2.Wong S, Al-Hasani H, Alam Z, Alam A. Artificial intelligence in radiology: how will we be affected? Eur Radiol 2019;29(1):141-3.
  • 3.Buyuk C. Diş Hekimliğinde Yapay Zeka. 2020;233-56.
  • 4.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. J Dent 2018;77:106-11.
  • 5.Poedjiastoeti W, Suebnukarn S. Application of convolutional neural network in the diagnosis of jaw tumors. Healthc Inform Res 2018;24(3):236-41.
  • 6.Kunz F, Stellzig-Eisenhauer A, Zeman F, Boldt J. Artificial intelligence in orthodontics. J Orofac Orthop 2020;81(1):52-68.
  • 7.Woo S-Y, Lee S-J, Yoo J-Y, Han J-J, Hwang S-J, Huh K-H, et al. Autonomous bone reposition around anatomical landmark for robot-assisted orthognathic surgery. J Craniomaxillofac Surg 2017;45(12):1980-8.
  • 8.Khanna SS, Dhaimade PA. Artificial intelligence: transforming dentistry today. Int J Appl Basic Med Res2017;6(3):161-7.
  • 9.Feeney L, Reynolds P, Eaton K, Harper J. A description of the new technologies used in transforming dental education. Br Dent J 2008;204(1):19- 28.
  • 10.Shan T, Tay F, Gu L. Application of artificial intelligence in dentistry. J Dent Res 2021;100(3):232-44.
  • 11.Moor J. The Dartmouth College artificial intelligence conference: The next fifty years. AI Mag 2006;27(4):87.
  • 12.Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial intelligence in surgery: promises and perils. Ann Surg2018;268(1):70.
  • 13.Khanagar SB, Al-Ehaideb A, Maganur PC, Vishwanathaiah S, Patil S, Baeshen HA, et al. Developments, application, and performance of artificial intelligence in dentistry–A systematic review. J Dent Sci 2021;16(1):508-22.
  • 14.Kositbowornchai S, Siriteptawee S, Plermkamon S, Bureerat S, Chetchotsak D. An artificial neural network for detection of simulated dental caries. Int J Comput Assist Radiol Surg2006;1(2):91-6.
  • 15.Hwang J-J, Azernikov S, Efros AA, Yu SX. Learning beyond human expertise with generative models for dental restorations. ArXiv:180400064. 2018.
  • 16.Kim K. Book Review: Deep Learning. Healthc Inform Res 2016;22:351.
  • 17.Brickley M, Shepherd J, Armstrong R. Neural networks: a new technique for development of decision support systems in dentistry. J Dent 1998;26(4):305-9.
  • 18.Hwang J-J, Jung Y-H, Cho B-H, Heo M-S. An overview of deep learning in the field of dentistry. Imaging Sci Dent2019;49(1):1-7.
  • 19.Tang A, Tam R, Cadrin-Chênevert A, Guest W, Chong J, Barfett J, et al. Canadian Association of Radiologists white paper on artificial intelligence in radiology. Can Assoc Radiol J 2018;69(2):120-35.
  • 20.Jha S, Topol EJ. Adapting to artificial intelligence: radiologists and pathologists as information specialists. Jama 2016;316(22):2353-4.
  • 21.Chan S, Siegel EL. Will machine learning end the viability of radiology as a thriving medical specialty? Br J Radiol 2019;92(1094):20180416.
  • 22.White SC, and Michael J. Pharoah. Oral radiology-E-Book: Principles and interpretation. Health Sciences 2014.
  • 23.Yaji A, Prasad S, Pai A. Artificial intelligence in dento-maxillofacial radiology. Acta Scientific Dental Sciences 2019;3(1):116-21.
  • 24.Katne T, Kanaparthi A, Gotoor S, Muppirala S, Devaraju R, Gantala R. Artificial intelligence: demystifying dentistry–the future and beyond. Int J Contemp Med Surg Radiol 2019;4(4):D6-D9.
  • 25.Flores A, Rysavy S, Enciso R, Okada K, editors. Non-invasive differential diagnosis of dental periapical lesions in cone-beam CT. 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro; 2009: IEEE.
  • 26.Hung K, Montalvao C, Tanaka R, Kawai T, Bornstein MM. The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review. Dentomaxillofac Radiol 2020;49(1):20190107.
  • 27.Krois J, Ekert T, Meinhold L, Golla T, Kharbot B, Wittemeier A, et al. Deep learning for the radiographic detection of periodontal bone loss. Sci Rep 2019;9(1):1-6.
  • 28.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 tomography–a clinical study. Int Endod J 2015;48(6):582-92.
  • 29.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.
  • 30.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. Dentomaxillofac Radiol 2021;50(6):20200172.
  • 31.Suzuki K. Overview of deep learning in medical imaging. Radiol Phys Technol 2017;10(3):257-73.
  • 32.Miki Y, Muramatsu C, Hayashi T, Zhou X, Hara T, Katsumata A, et al. Classification of teeth in cone-beam CT using deep convolutional neural network. Comput Biol Med 2017;80:24-9.
  • 33.Tekin By. Bitewing Ağiz İçi Radyografik Görüntülerde Derin Öğrenme İle Diş Segmentasyonu 2021.
  • 34.Oktay AB, editor Tooth detection with convolutional neural networks. 2017 Medical Technologies National Congress (TIPTEKNO); 2017: IEEE.
  • 35.Çelik Ö, Odabaş A, BAYRAKDAR İŞ, Bilgir E, AKKOCA F. Derin öğrenme yöntemi ile panoramik radyografiden diş eksikliklerinin tespiti: Bir yapay zekâ pilot çalışması. Selcuk Dent J 2019;6(4):168-72.
  • 36.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 networkbased computer-assisted diagnosis system: a preliminary study. Dentomaxillofac Radiol 2019;48(1):20170344
  • 37.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. J Periodontal Implant Sci 2018;48(2):114-23.
  • 38.Orhan K, Bayrakdar I, Ezhov M, Kravtsov A, Özyürek T. Evaluation of artificial intelligence for detecting periapical pathosis on cone‐beam computed tomography scans. Int Endod J 2020;53(5):680-9.
  • 39.Jaskari J, Sahlsten J, Järnstedt J, Mehtonen H, Karhu K, Sundqvist O, et al. Deep learning method for mandibular canal segmentation in dental cone beam computed tomography volumes. Sci Rep 2020;10(1):1-8.
  • 40.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 Radiol2020;36(4):337-43.
  • 41.Li Q, Chen K, Han L, Zhuang Y, Li J, Lin J. Automatic tooth roots segmentation of cone beam computed tomography image sequences using Unet and RNN. J Xray Sci Technol 2020;28(5):905-22.
  • 42.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. Dentomaxillofac Radiol 2019;48(3):20180218.
  • 43.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 Surg Oral Med Oral Pathol Oral Radiol2020;130(4):464-9.
  • 44.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. Dentomaxillofac Radiol 2019;48(6):20190019.
  • 45.Jurczyszyn K, Kozakiewicz M. Differential diagnosis of leukoplakia versus lichen planus of the oral mucosa based on digital texture analysis in intraoral photography. Adv Clin Exp Med2019;28(11):1469-76.
  • 46.Kim DW, Lee S, Kwon S, Nam W, Cha I-H, Kim HJ. Deep learning-based survival prediction of oral cancer patients. Sci Rep 2019;9(1):1-10.
  • 47.Saintigny P, Zhang L, Fan Y-H, El-Naggar AK, Papadimitrakopoulou VA, Feng L, et al. Gene expression profiling predicts the development of oraln cancer. Cancer Prev Res 2011;4(2):218-29.

Oral Radyolojide Yapay Zeka

Yıl 2021, Cilt: 1 Sayı: 3, 78 - 83, 25.03.2022

Mehmet Emin Doğan Elif Meltem Aslan Öztürk

Öz

Gelişen, değişen ve ilerleyen teknolojide sağlık ve diş hekimliğinin de yerini alması gerekmektedir. Son
dönemlerde adını sık sık duyduğumuz yapay zekânın diş hekimliğine girişi başlamış ve ilerlemeler kaydedilmiştir.
Yapay zekânın sağlık ve diş hekimliği çalışanlarına çalışmalarında yardımcı olduğu iş akışını kolaylaştırdığı
düşünüldüğünde ilerde daha çok tercih edileceği ve hayatımıza aktif olarak girecektir. Bu konu da elde edeceğimiz
bilgiler bize yardımcı olacak ve teşhis ve tanıya daha hızlı ve yanlışsız ulaşmamızı sağlayacaktır. Diş hekimliğinde
tanı, teşhis ve veri depolamasında oral radyologların payı daha fazladır. Bu sebepledir ki diş hekimlerin ve özellikle
oral radyologların yapay zekâ hakkında fikir sahibi olması önem arz etmektedir. Bu derlemenin amacı son dönemlerde
güncel bir konu haline gelmiş olan yapay zekânın diş hekimliği alanındaki uygulamalarını incelemek ve diş
hekimlerinde bu teknoloji hakkında farkındalık oluşturmaktır.

Anahtar Kelimeler

Yapay Zekâ Diş Hekimliği Konik Işınlı Bilgisayarlı Tomografi.

Kaynakça

  • 1.Russell SJ, Norvig P. Artificial Intelligence-A Modern Approach, Third Int. Edition. Pearson Education, Upper Saddle River, NJ, USA; 2010.
  • 2.Wong S, Al-Hasani H, Alam Z, Alam A. Artificial intelligence in radiology: how will we be affected? Eur Radiol 2019;29(1):141-3.
  • 3.Buyuk C. Diş Hekimliğinde Yapay Zeka. 2020;233-56.
  • 4.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. J Dent 2018;77:106-11.
  • 5.Poedjiastoeti W, Suebnukarn S. Application of convolutional neural network in the diagnosis of jaw tumors. Healthc Inform Res 2018;24(3):236-41.
  • 6.Kunz F, Stellzig-Eisenhauer A, Zeman F, Boldt J. Artificial intelligence in orthodontics. J Orofac Orthop 2020;81(1):52-68.
  • 7.Woo S-Y, Lee S-J, Yoo J-Y, Han J-J, Hwang S-J, Huh K-H, et al. Autonomous bone reposition around anatomical landmark for robot-assisted orthognathic surgery. J Craniomaxillofac Surg 2017;45(12):1980-8.
  • 8.Khanna SS, Dhaimade PA. Artificial intelligence: transforming dentistry today. Int J Appl Basic Med Res2017;6(3):161-7.
  • 9.Feeney L, Reynolds P, Eaton K, Harper J. A description of the new technologies used in transforming dental education. Br Dent J 2008;204(1):19- 28.
  • 10.Shan T, Tay F, Gu L. Application of artificial intelligence in dentistry. J Dent Res 2021;100(3):232-44.
  • 11.Moor J. The Dartmouth College artificial intelligence conference: The next fifty years. AI Mag 2006;27(4):87.
  • 12.Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial intelligence in surgery: promises and perils. Ann Surg2018;268(1):70.
  • 13.Khanagar SB, Al-Ehaideb A, Maganur PC, Vishwanathaiah S, Patil S, Baeshen HA, et al. Developments, application, and performance of artificial intelligence in dentistry–A systematic review. J Dent Sci 2021;16(1):508-22.
  • 14.Kositbowornchai S, Siriteptawee S, Plermkamon S, Bureerat S, Chetchotsak D. An artificial neural network for detection of simulated dental caries. Int J Comput Assist Radiol Surg2006;1(2):91-6.
  • 15.Hwang J-J, Azernikov S, Efros AA, Yu SX. Learning beyond human expertise with generative models for dental restorations. ArXiv:180400064. 2018.
  • 16.Kim K. Book Review: Deep Learning. Healthc Inform Res 2016;22:351.
  • 17.Brickley M, Shepherd J, Armstrong R. Neural networks: a new technique for development of decision support systems in dentistry. J Dent 1998;26(4):305-9.
  • 18.Hwang J-J, Jung Y-H, Cho B-H, Heo M-S. An overview of deep learning in the field of dentistry. Imaging Sci Dent2019;49(1):1-7.
  • 19.Tang A, Tam R, Cadrin-Chênevert A, Guest W, Chong J, Barfett J, et al. Canadian Association of Radiologists white paper on artificial intelligence in radiology. Can Assoc Radiol J 2018;69(2):120-35.
  • 20.Jha S, Topol EJ. Adapting to artificial intelligence: radiologists and pathologists as information specialists. Jama 2016;316(22):2353-4.
  • 21.Chan S, Siegel EL. Will machine learning end the viability of radiology as a thriving medical specialty? Br J Radiol 2019;92(1094):20180416.
  • 22.White SC, and Michael J. Pharoah. Oral radiology-E-Book: Principles and interpretation. Health Sciences 2014.
  • 23.Yaji A, Prasad S, Pai A. Artificial intelligence in dento-maxillofacial radiology. Acta Scientific Dental Sciences 2019;3(1):116-21.
  • 24.Katne T, Kanaparthi A, Gotoor S, Muppirala S, Devaraju R, Gantala R. Artificial intelligence: demystifying dentistry–the future and beyond. Int J Contemp Med Surg Radiol 2019;4(4):D6-D9.
  • 25.Flores A, Rysavy S, Enciso R, Okada K, editors. Non-invasive differential diagnosis of dental periapical lesions in cone-beam CT. 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro; 2009: IEEE.
  • 26.Hung K, Montalvao C, Tanaka R, Kawai T, Bornstein MM. The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review. Dentomaxillofac Radiol 2020;49(1):20190107.
  • 27.Krois J, Ekert T, Meinhold L, Golla T, Kharbot B, Wittemeier A, et al. Deep learning for the radiographic detection of periodontal bone loss. Sci Rep 2019;9(1):1-6.
  • 28.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 tomography–a clinical study. Int Endod J 2015;48(6):582-92.
  • 29.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.
  • 30.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. Dentomaxillofac Radiol 2021;50(6):20200172.
  • 31.Suzuki K. Overview of deep learning in medical imaging. Radiol Phys Technol 2017;10(3):257-73.
  • 32.Miki Y, Muramatsu C, Hayashi T, Zhou X, Hara T, Katsumata A, et al. Classification of teeth in cone-beam CT using deep convolutional neural network. Comput Biol Med 2017;80:24-9.
  • 33.Tekin By. Bitewing Ağiz İçi Radyografik Görüntülerde Derin Öğrenme İle Diş Segmentasyonu 2021.
  • 34.Oktay AB, editor Tooth detection with convolutional neural networks. 2017 Medical Technologies National Congress (TIPTEKNO); 2017: IEEE.
  • 35.Çelik Ö, Odabaş A, BAYRAKDAR İŞ, Bilgir E, AKKOCA F. Derin öğrenme yöntemi ile panoramik radyografiden diş eksikliklerinin tespiti: Bir yapay zekâ pilot çalışması. Selcuk Dent J 2019;6(4):168-72.
  • 36.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 networkbased computer-assisted diagnosis system: a preliminary study. Dentomaxillofac Radiol 2019;48(1):20170344
  • 37.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. J Periodontal Implant Sci 2018;48(2):114-23.
  • 38.Orhan K, Bayrakdar I, Ezhov M, Kravtsov A, Özyürek T. Evaluation of artificial intelligence for detecting periapical pathosis on cone‐beam computed tomography scans. Int Endod J 2020;53(5):680-9.
  • 39.Jaskari J, Sahlsten J, Järnstedt J, Mehtonen H, Karhu K, Sundqvist O, et al. Deep learning method for mandibular canal segmentation in dental cone beam computed tomography volumes. Sci Rep 2020;10(1):1-8.
  • 40.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 Radiol2020;36(4):337-43.
  • 41.Li Q, Chen K, Han L, Zhuang Y, Li J, Lin J. Automatic tooth roots segmentation of cone beam computed tomography image sequences using Unet and RNN. J Xray Sci Technol 2020;28(5):905-22.
  • 42.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. Dentomaxillofac Radiol 2019;48(3):20180218.
  • 43.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 Surg Oral Med Oral Pathol Oral Radiol2020;130(4):464-9.
  • 44.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. Dentomaxillofac Radiol 2019;48(6):20190019.
  • 45.Jurczyszyn K, Kozakiewicz M. Differential diagnosis of leukoplakia versus lichen planus of the oral mucosa based on digital texture analysis in intraoral photography. Adv Clin Exp Med2019;28(11):1469-76.
  • 46.Kim DW, Lee S, Kwon S, Nam W, Cha I-H, Kim HJ. Deep learning-based survival prediction of oral cancer patients. Sci Rep 2019;9(1):1-10.
  • 47.Saintigny P, Zhang L, Fan Y-H, El-Naggar AK, Papadimitrakopoulou VA, Feng L, et al. Gene expression profiling predicts the development of oraln cancer. Cancer Prev Res 2011;4(2):218-29.
Toplam 47 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Diş Hekimliği
Bölüm Derlemeler
Yazarlar

Mehmet Emin Doğan Bu kişi benim

Elif Meltem Aslan Öztürk

Yayımlanma Tarihi 25 Mart 2022
Yayımlandığı Sayı Yıl 2021 Cilt: 1 Sayı: 3

Kaynak Göster

APA Doğan, M. E., & Aslan Öztürk, E. M. (2022). Oral Radyolojide Yapay Zeka. HRU International Journal of Dentistry and Oral Research, 1(3), 78-83.
AMA Doğan ME, Aslan Öztürk EM. Oral Radyolojide Yapay Zeka. HRU Int J Dent Oral Res. Mart 2022;1(3):78-83.
Chicago Doğan, Mehmet Emin, ve Elif Meltem Aslan Öztürk. “Oral Radyolojide Yapay Zeka”. HRU International Journal of Dentistry and Oral Research 1, sy. 3 (Mart 2022): 78-83.
EndNote Doğan ME, Aslan Öztürk EM (01 Mart 2022) Oral Radyolojide Yapay Zeka. HRU International Journal of Dentistry and Oral Research 1 3 78–83.
IEEE M. E. Doğan ve E. M. Aslan Öztürk, “Oral Radyolojide Yapay Zeka”, HRU Int J Dent Oral Res, c. 1, sy. 3, ss. 78–83, 2022.
ISNAD Doğan, Mehmet Emin - Aslan Öztürk, Elif Meltem. “Oral Radyolojide Yapay Zeka”. HRU International Journal of Dentistry and Oral Research 1/3 (Mart 2022), 78-83.
JAMA Doğan ME, Aslan Öztürk EM. Oral Radyolojide Yapay Zeka. HRU Int J Dent Oral Res. 2022;1:78–83.
MLA Doğan, Mehmet Emin ve Elif Meltem Aslan Öztürk. “Oral Radyolojide Yapay Zeka”. HRU International Journal of Dentistry and Oral Research, c. 1, sy. 3, 2022, ss. 78-83.
Vancouver Doğan ME, Aslan Öztürk EM. Oral Radyolojide Yapay Zeka. HRU Int J Dent Oral Res. 2022;1(3):78-83.
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