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

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

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  • 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 
    PubMed 

    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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