DOI:

10.37988/1811-153X_2020_3_72

Artificial neural networks in dental and maxillofacial radiology: a review

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Authors

  • A.A. Muraev 1, PhD in Medical Sciences, full professor of the Oral and maxillofacial surgery Department
  • N.A. Guseynov 1, resident at the Oral and maxillofacial surgery Department
  • P.A. Tsay 2, student
  • I.A. Kibardin 2, student
  • D.V. Burenchev 3, PhD in Medical Sciences, chief researcher
  • S.S. Ivanov 1, resident at the Oral and maxillofacial surgery Department
  • N.Yu. Oborotistov 4, PhD in medical Sciences, assistant professor of the Orthodontics Department
  • M.A. Matuta 5, postgraduate at the General and pediatric dentistry Department
  • N.S. Grachev 6, PhD in Medical Sciences, head of the Oncology and pediatric surgery Department, general manager of the Head and neck surgery and reconstructive plastic surgery Team, vice-director of the Oncology, radiology and nuclear medicine Institute
  • S.S. Larin 6, PhD in Biology, vice-director of the of Molecular and experimental medicine Institute
  • 1 RUDN University, 117198, Moscow, Russia
  • 2 MIPT, 141701, Dolgoprudny, Russia
  • 3 Center for Diagnostics and Telemedicine, 109029, Moscow, Russia
  • 4 Moscow State University of Medicine and Dentistry, 127473, Moscow, Russia
  • 5 Stavropol State Medical University, 355017, Stavropol, Russia
  • 6 Dmitry Rogachev National Medical Research Center of Pediatric Hematology Oncology and Immunology, 117997, Moscow, Russia

Abstract

Radiology is a huge and most intellectually intensive field of medicine, so the use of artificial intelligence (AI) is still too far from the full analysis of images and successfully solved only basic, routine tasks. This review article presents modern possibilities of machine vision based on artificial neural networks (ANS) in radiation diagnostics, in particular in dentistry and maxillofacial surgery. The result of searching for articles by keywords (from 12.04.2020) shows an increase in the number of publications by an order of magnitude: from 58 articles per year on average in 2000—2015 to 945 in 2019. The main application of the neural network was found in the recognition of anatomical objects on X-rays: cortical and spongy layer of the jaw bones, the lower jaw canal, maxillary sinus, teeth, root canals; pathological formations and processes: periapical inflammatory changes, including cysts, tumors, bone resorption in periodontitis, fractures of the root of teeth, etc. The main application of the neural network is in the recognition of the anatomical objects on X-rays. Modern neural networks are trained and work with two-dimensional (2D) images: orthopantomogram, teleradiograms in forward and lateral projections, ultrasound images, and with three-dimensional (3D) data of computer tomography and magnetic resonance tomography. Special attention is paid to 2D and 3D cephalometric analysis. In addition to the analysis of theoretical research, the article also considers the mechanisms of neural network integration into the treatment process and assessment of their real benefits for practicing doctors, further prospects for the development of neural network approaches. Conclusion. Introduction of machine vision technologies on the basis of deep neural network convolution for radiological diagnostics in dentistry (and medicine in general) is a promising direction that allows to automate and speed up processing and recognition of initial data and, possibly, to reduce the number of errors associated with the human factor. However, at present, ANS cannot and should not replace highly specialized doctors when making a diagnosis and drawing up a treatment plan; it is recommended to use them as independent expert systems.

Key words:

artificial intelligence, artificial neural network, deep learning, radiography, radiology, cone-beam computed tomography, cephalometric analysis, orthodontics, maxillofacial surgery, lateral ceph, frontal ceph

For Citation

[1]
Muraev A.A., Guseynov N.A., Tsay P.A., Kibardin I.A., Burenchev D.V., Ivanov S.S., Oborotistov N.Yu., Matuta M.A., Grachev N.S., Larin S.S. Artificial neural networks in dental and maxillofacial radiology: a review. Clinical Dentistry (Russia).  2020; 3 (95): 72—80. DOI: 10.37988/1811-153X_2020_3_72

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Published on

September 15, 2020