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Artificial Intelligence In Dentistry

Year 2021, Volume: 1 Issue: 2, 26 - 33, 18.08.2021

Abstract

References

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  • 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
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Diş Hekimliğinde Yapay Zeka

Year 2021, Volume: 1 Issue: 2, 26 - 33, 18.08.2021

Abstract

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.

References

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  • 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
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  • 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,
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  • 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.
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  • 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.
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  • 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.
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  • 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).
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There are 80 citations in total.

Details

Primary Language Turkish
Subjects Artificial Intelligence (Other)
Journal Section Reviews
Authors

Hande Sağlam

Tuğba Arı

İbrahim Şevki Bayrakdar

Elif Bilgir

Mehmet Uğurlu

Özer Çelik

Kaan Orhan

Publication Date August 18, 2021
Published in Issue Year 2021 Volume: 1 Issue: 2

Cite

Vancouver Sağlam H, Arı T, Bayrakdar İŞ, Bilgir E, Uğurlu M, Çelik Ö, Orhan K. Diş Hekimliğinde Yapay Zeka. JAIHS. 2021;1(2):26-33.