Imaging Science in Dentistry

Korean Academy of Oral and Maxillofacial Radiology (대한영상치의학회)
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Clinical applications and performance of intelligent systems in dental and maxillofacial radiology: A review

  • Nagi, Ravleen (Department of Oral Medicine and Radiology, Swami Devi Dyal Hospital and Dental College) ;
  • Aravinda, Konidena (Department of Oral Medicine and Radiology, Swami Devi Dyal Hospital and Dental College) ;
  • Rakesh, N (Department of Oral Medicine and Radiology, Faculty of Dental Sciences, M.S. Ramaiah University of Applied Sciences) ;
  • Gupta, Rajesh (Department of Oral Medicine and Radiology, Swami Devi Dyal Hospital and Dental College) ;
  • Pal, Ajay (Department of Oral Medicine and Radiology, Swami Devi Dyal Hospital and Dental College) ;
  • Mann, Amrit Kaur (Department of Oral Medicine and Radiology, Swami Devi Dyal Hospital and Dental College)
  • Received : 2019.10.19
  • Accepted : 2020.02.12
  • Published : 2020.06.30
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Abstract

Intelligent systems(i.e., artificial intelligence), particularly deep learning, are machines able to mimic the cognitive functions of humans to perform tasks of problem-solving and learning. This field deals with computational models that can think and act intelligently, like the human brain, and construct algorithms that can learn from data to make predictions. Artificial intelligence is becoming important in radiology due to its ability to detect abnormalities in radiographic images that are unnoticed by the naked human eye. These systems have reduced radiologists' workload by rapidly recording and presenting data, and thereby monitoring the treatment response with a reduced risk of cognitive bias. Intelligent systems have an important role to play and could be used by dentists as an adjunct to other imaging modalities in making appropriate diagnoses and treatment plans. In the field of maxillofacial radiology, these systems have shown promise for the interpretation of complex images, accurate localization of landmarks, characterization of bone architecture, estimation of oral cancer risk, and the assessment of metastatic lymph nodes, periapical pathologies, and maxillary sinus pathologies. This review discusses the clinical applications and scope of intelligent systems such as machine learning, artificial intelligence, and deep learning programs in maxillofacial imaging.

Keywords

References

  1. Wong SH, Al-Hasani H, Alam Z, Alam A. Artificial intelligence in radiology: how will be affected? Eur Radiol 2019; 29: 141-3. https://doi.org/10.1007/s00330-018-5644-3
  2. Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJ. Artificial intelligence in radiology. Nat Rev Cancer 2018; 18: 500-10. https://doi.org/10.1038/s41568-018-0016-5
  3. Lee JH, Kim DH, Jeong SN, Choi SH. Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm. J Periodontal Implant Sci 2018; 48: 114-23. https://doi.org/10.5051/jpis.2018.48.2.114
  4. Lee JH, Kim DH, Jeong SN, Choi SH. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J Dent 2018; 77: 106-11. https://doi.org/10.1016/j.jdent.2018.07.015
  5. Vinayahalingam S, Xi T, Berge S, Maal T, de Jong G. Automated detection of third molars and mandibular nerve by deep learning. Sci Rep 2019; 9: 9007. https://doi.org/10.1038/s41598-019-45487-3
  6. 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: 20180218. https://doi.org/10.1259/dmfr.20180218
  7. 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. Dentomaxillofac Radiol 2019; 48: 20180051. https://doi.org/10.1259/dmfr.20180051
  8. 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; 8: 8995. https://doi.org/10.1038/s41598-018-27349-6
  9. 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 Surg Oral Med Oral Pathol Oral Radiol 2019; 127: 458-63. https://doi.org/10.1016/j.oooo.2018.10.002
  10. Kim DW, Lee S, Kwon S, Nam W, Cha IH, Kim HJ. Deep learning-based survival prediction of oral cancer patients. Sci Rep 2019; 9: 6994. https://doi.org/10.1038/s41598-019-43372-7
  11. Das S, Dey A, Pal A, Roy N. Applications of artificial intelligence in machine learning: review and prospect. Int J Comput Appl 2015; 115: 31-41.
  12. Sharma D, Kumar N. A review on machine learning algorithms, tasks and applications. Int J Adv Res Comput Eng Technol 2017; 6: 1548-52.
  13. Chan S, Siegel EL. Will machine learning end the viability of radiology as a thriving medical specialty? Br J Radiol 2019; 92: 20180416. https://doi.org/10.1259/bjr.20180416
  14. Hopfield JJ. Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci U S A 1982; 79: 2554-8. https://doi.org/10.1073/pnas.79.8.2554
  15. Doi K. Computer aided diagnosis in medical imaging: historical review, current status, and future potential. Comput Med Imag Graph 2007; 31: 198-211. https://doi.org/10.1016/j.compmedimag.2007.02.002
  16. Dhyan P. Unsupervised learning. In: Wilson RA, Keil FC. The MIT encyclopedia of the cognitive sciences. Cambridge, Mass: MIT Press; 1999. p. 1-7.
  17. Zhang Z, Sejdic E. Radiological images and machine learning: trends, perspectives, and prospects. Comput Biol Med 2019;108: 354-70. https://doi.org/10.1016/j.compbiomed.2019.02.017
  18. Shiraishi J, Li Q, Appelbaum D, Doi K. Computer-aided diagnosis and artificial intelligence in clinical imaging. Semin Nucl Med 2011; 41: 449-62. https://doi.org/10.1053/j.semnuclmed.2011.06.004
  19. Yanger RR. Fuzzy logics and artificial intelligence. Fuzzy Sets Syst 1997; 90: 193-8. https://doi.org/10.1016/S0165-0114(97)00086-9
  20. Axer H, Jantzen J, Keyserlingk DG, Berks G. The application of fuzzy-based methods to central nerve fiber imaging. Artif Intell Med 2003; 29: 225-39. https://doi.org/10.1016/S0933-3657(02)00071-4
  21. Doi K, MacMahon H, Katsuragawa S, Nishikawa RM, Jiang Y. Computer-aided diagnosis in radiology: potential and pitfalls. Euro J Radiol 1999; 31: 97-109. https://doi.org/10.1016/S0720-048X(99)00016-9
  22. 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. Sci Rep 2019; 9: 3840. https://doi.org/10.1038/s41598-019-40414-y
  23. Devito KL, de Souza Barbosa F, Felippe Filho WN. An artificial multilayer perceptron neural network for diagnosis of proximal dental caries. Oral Surg Oral Med Oral Pathol Oral Radiol Endod 2008; 106: 879-84. https://doi.org/10.1016/j.tripleo.2008.03.002
  24. Naik M, de Ataide ID, Fernandes M, Lambor R. Future of endodontics. Int J Curr Res 2016; 8: 25610-6.
  25. Murata M, Ariji Y, Ohashi Y, Kawai T, Fukuda M, Funakoshi T, et al. Deep learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography. Oral Radiol 2019; 35: 301-7. https://doi.org/10.1007/s11282-018-0363-7
  26. Kim Y, Lee KJ, Sunwoo L, Choi D, Nam CM, Cho J, et al. Deep learning in diagnosis of maxillary sinusitis using conventional radiography. Invest Radiol 2019; 54: 7-15. https://doi.org/10.1097/RLI.0000000000000503
  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: 8495. https://doi.org/10.1038/s41598-019-44839-3
  28. Cevidanes LH, Hajati AK, Paniagua B, Lim PF, Walker DG, Palconet G, et al. Quantification of condylar resorption in temporomandibular joint osteoarthritis. Oral Surg Oral Med Oral Pathol Oral Radiol Endod 2010; 110: 110-7. https://doi.org/10.1016/j.tripleo.2010.01.008
  29. de Dumast P, Mirabel C, Paniagua B, Yatabe M, Ruellas A, Tubau N, et al. SVA: shape variation analyzer. Proc SPIE Int Soc Opt Eng 2018; 10578. pii: 105782H.
  30. Vandewiele G, De Backere F, Lannoye K, Vanden Berghe M, Janssens O, Van Hoecke S, et al. A decision support system to follow up and diagnose primary headache patients using semantically enriched data. BMC Med Inform Decis Mak 2018; 18: 98. https://doi.org/10.1186/s12911-018-0679-6
  31. Krawczyk B, Simic D, Simic S, Wozniak M. Automatic diagnosis of primary headaches by machine learning methods. Cent Eur J Med 2013; 8: 157-65.
  32. Bogduk N. Cervicogenic headache: anatomic basis and pathophysiologic mechanisms. Curr Pain Headache Rep 2001; 5: 382-6. https://doi.org/10.1007/s11916-001-0029-7
  33. 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 Sjogren's syndrome on CT images. Dentomaxillofac Radiol 2019; 48: 20190019. https://doi.org/10.1259/dmfr.20190019
  34. Lerner H, Mouhyi J, Admakin O, Mangano F. Artificial intelligence in fixed implant prosthodontics: a retrospective study of 106 implant-supported monolithic zirconia crowns inserted in the posterior jaws of 90 patients. BMC Oral Health 2020; 20: 80. https://doi.org/10.1186/s12903-020-1062-4
  35. Vecsei B, Joos-Kovacs G, Borbely J, Hermann P. Comparison of the accuracy of direct and indirect three-dimensional digitizing processes for CAD/CAM systems - an in vitro study. J Prosthodont Res 2017; 61: 177-84. https://doi.org/10.1016/j.jpor.2016.07.001
  36. Panesar S, Cagle Y, Chander D, Morey J, Fernandez-Miranda J, Kliot M. Artificial intelligence and future of surgical robotics. Ann Surg 2019; 270: 223-6. https://doi.org/10.1097/SLA.0000000000003262
  37. Bouletreau P, Makaremi M, Ibrahim B, Louvrier A, Sigaux N. Artificial intelligence: applications in orthognathic surgery. J Stomatol Oral Maxillofac Surg 2019; 120: 347-54. https://doi.org/10.1016/j.jormas.2019.06.001
  38. Dalitz GD. Age determination of adult human remains by teeth examination. J Forensic Sci Soc 1962; 3: 11-21. https://doi.org/10.1016/S0015-7368(62)70094-0
  39. Bewes J, Low A, Morphett A, Pate FD, Henneberg M. Artificial intelligence for sex determination of skeletal remains: application of a deep learning artificial neural network to human skulls. J Forensic Leg Med 2019; 62: 40-3. https://doi.org/10.1016/j.jflm.2019.01.004
  40. Gross GW, Boone JM, Bishop DM. Pediatric skeletal age: determination with neural networks. Radiology 1995; 195: 689-95. https://doi.org/10.1148/radiology.195.3.7753995
  41. Avuclu E, Basciftci F. Novel approaches to determine age and gender from dental X-ray images by using multiplayer perceptron neural networks and image processing techniques. Chaos Solitons Fractals 2019; 120: 127-38. https://doi.org/10.1016/j.chaos.2019.01.023
  42. Attaran M. The rise of 3-D printing: the advantages of additive manufacturing over traditional manufacturing. Bus Horiz 2017; 60: 677-88. https://doi.org/10.1016/j.bushor.2017.05.011
  43. Khanna SS, Dhaimade PA. Artificial intelligence: transforming dentistry today. Indian J Basic Appl Med Res 2017; 6: 161-7.

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