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A brief introduction to concepts and applications of artificial intelligence in dental imaging

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Abstract

This report aims to summarize the fundamental concepts of Artificial Intelligence (AI), and to provide a non-exhaustive overview of AI applications in dental imaging, comprising diagnostics, forensics, image processing and image reconstruction. AI has arguably become the hottest topic in radiology in recent years owing to the increased computational power available to researchers, the continuing collection of digital data, as well as the development of highly efficient algorithms for machine learning and deep learning. It is now feasible to develop highly robust AI applications that make use of the vast amount of data available to us, and that keep learning and improving over time.

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Fig. 1
Fig. 2

Reproduced from Poedjiastoeti and Suebnukarn under a Creative Commons Attribution Non-Commercial License [12]

Fig. 3

Adapted from Murata et al. with permission by the Japanese Society for Oral and Maxillofacial Radiology and Springer Nature Singapore [13]

Fig. 4

Reproduced from Lee et al. under a Creative Commons Attribution Non-Commercial License [16]

Fig. 5

Adapted from Krois et al. under a Creative Commons Attribution 4.0 International License [17]

Fig. 6

Adapted from De Tobel et al. with the author’s permission [20]

Fig. 7

Adapted from Chen et al. under a Creative Commons Attribution 4.0 International License [23]

Fig. 8

Adapted from Hu et al. with permission from John Wiley and Sons [25]

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References

  1. Recht M, Bryan RN. Artificial Intelligence: threat or boon to radiologists? J Am Coll Radiol. 2017;14:1476–80. https://doi.org/10.1016/j.jacr.2017.07.007.

    Article  PubMed  Google Scholar 

  2. Kohli M, Prevedello LM, Filice RW, Geis JR. Implementing machine learning in radiology practice and research. AJR Am J Roentgenol. 2017;208:754–60. https://doi.org/10.2214/AJR.16.17224.

    Article  PubMed  Google Scholar 

  3. Clarke AM, Friedrich J, Tartaglia EM, Marchesotti S, Senn W, Herzog MH. Human and machine learning in non-Markovian decision making. PLoS One. 2015;10:e0123105. https://doi.org/10.1371/journal.pone.0123105.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Plis SM, Hjelm DR, Salakhutdinov R, Allen EA, Bockholt HJ, Long JD, et al. Deep learning for neuroimaging: a validation study. Front Neurosci. 2014;8:229. https://doi.org/10.3389/fnins.2014.00229.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Houssami N, Lee CI, Buist DSM, Tao D. Artificial intelligence for breast cancer screening: opportunity or hype? Breast. 2017;36:31–3. https://doi.org/10.1016/j.breast.2017.09.003.

    Article  PubMed  Google Scholar 

  6. Lakhani P, Sundaram B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology. 2017;284:574–82. https://doi.org/10.1148/radiol.2017162326.

    Article  PubMed  Google Scholar 

  7. Lee H, Troschel FM, Tajmir S, Fuchs G, Mario J, Fintelmann FJ, et al. Pixel-level deep segmentation: artificial intelligence quantifies muscle on computed tomography for body morphometric analysis. J Digit Imaging. 2017;30:487–98. https://doi.org/10.1007/s10278-017-9988-z.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Olczak J, Fahlberg N, Maki A, Razavian AS, Jilert A, Stark A, et al. Artificial intelligence for analyzing orthopedic trauma radiographs. Acta Orthop. 2017;88:581–6. https://doi.org/10.1080/17453674.2017.1344459.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Prevedello LM, Erdal BS, Ryu JL, Little KJ, Demirer M, Qian S, et al. Automated critical test findings identification and online notification system using artificial intelligence in imaging. Radiology. 2017;285:923–31. https://doi.org/10.1148/radiol.2017162664.

    Article  PubMed  Google Scholar 

  10. Hwang JJ, Jung YH, Cho BH, Heo MS. An overview of deep learning in the field of dentistry. Imaging Sci Dent. 2019;49:1–7. https://doi.org/10.5624/isd.2019.49.1.1.

    Article  PubMed  PubMed Central  Google Scholar 

  11. 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;49:20190107. https://doi.org/10.1259/dmfr.20190107.

    Article  PubMed  Google Scholar 

  12. Poedjiastoeti W, Suebnukarn S. Application of convolutional neural network in the diagnosis of jaw tumors. Healthc Inform Res. 2018;24:236–41. https://doi.org/10.4258/hir.2018.24.3.236.

    Article  PubMed  PubMed Central  Google Scholar 

  13. 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. 2018;35:301–7. https://doi.org/10.1007/s11282-018-0363-7.

    Article  PubMed  Google Scholar 

  14. Kavitha MS, Ganesh Kumar P, Park SY, Huh KH, Heo MS, Kurita T, et al. Automatic detection of osteoporosis based on hybrid genetic swarm fuzzy classifier approaches. Dentomaxillofac Radiol. 2016;45:20160076. https://doi.org/10.1259/dmfr.20160076.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Chu P, Bo C, Liang X, Yang J, Megalooikonomou V, Yang F, Huang B, Li X, Ling H. Using octuplet siamese network for osteoporosis analysis on dental panoramic radiographs. Conf Proc IEEE Eng Med Biol Soc. 2018;2018:2579–82. https://doi.org/10.1109/EMBC.2018.8512755.

    Article  Google Scholar 

  16. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  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. Sci Rep. 2019;9:8495. https://doi.org/10.1038/s41598-019-44839-3.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Johari M, Esmaeili F, Andalib A, Garjani S, Saberkari H. Detection of vertical root fractures in intact and endodontically treated premolar teeth by designing a probabilistic neural network: an ex vivo study. Dentomaxillofac Radiol. 2017;46:20160107. https://doi.org/10.1259/dmfr.20160107.

    Article  PubMed  PubMed Central  Google Scholar 

  19. 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.

    Article  PubMed  Google Scholar 

  20. 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. J Forensic Odontostomatol. 2017;2:42–544.

    Google Scholar 

  21. Pauwels R, Jacobs R, Singer SR, Mupparapu M. CBCT-based bone quality assessment: are Hounsfield units applicable? Dentomaxillofac Radiol. 2015;44:20140238. https://doi.org/10.1259/dmfr.20140238.

    Article  PubMed  Google Scholar 

  22. Zhang J, Liu M, Wang L, Chen S, Yuan P, Li J, et al. Joint craniomaxillofacial bone segmentation and landmark digitization by context-guided fully convolutional networks. Med Image Comput Comput Assist Interv. 2017;10434:720–8. https://doi.org/10.1007/978-3-319-66185-8_81.

    Article  PubMed  PubMed Central  Google Scholar 

  23. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  24. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60–88. https://doi.org/10.1016/j.media.2017.07.005.

    Article  PubMed  Google Scholar 

  26. Hu Z, Jiang C, Sun F, Zhang Q, Ge Y, Yang Y, et al. Artifact correction in low-dose dental CT imaging using Wasserstein generative adversarial networks. Med Phys. 2019;46:1686–96. https://doi.org/10.1002/mp.13415.

    Article  PubMed  Google Scholar 

  27. Pauwels R, Oliveira-Santos C, Oliveira ML, Watanabe PCA, Araújo Faria V, Jacobs R, et al. Artefact reduction in cone beam CT through deep learning: a pilot study using neural networks in the projection domain. In: 22nd International congress of DentoMaxilloFacial radiology, Philadelphia, PA, USA, 2019.

  28. Geis JR, Brady AP, Wu CC, Spencer J, Ranschaert E, Jaremko JL, et al. Ethics of artificial intelligence in radiology: summary of the Joint European and North American Multisociety Statement. Radiology. 2019;293:436–40. https://doi.org/10.1148/radiol.2019191586.

    Article  PubMed  Google Scholar 

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Acknowledgements

R. Pauwels is supported by the European Union Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie Grant agreement number 754513 and by Aarhus University Research Foundation (AIAS-COFUND).

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  1. Aarhus Institute of Advanced Studies (AIAS), Aarhus University, Høegh-Guldbergs Gade 6B, 8000-C, Aarhus, Denmark

    Ruben Pauwels

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Pauwels, R. A brief introduction to concepts and applications of artificial intelligence in dental imaging. Oral Radiol 37, 153–160 (2021). https://doi.org/10.1007/s11282-020-00468-5

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