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. 2020 Jan;49(1):20190107.
doi: 10.1259/dmfr.20190107. Epub 2019 Aug 14.

The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review

Affiliations

The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review

Kuofeng Hung et al. Dentomaxillofac Radiol. 2020 Jan.

Abstract

Objectives: To investigate the current clinical applications and diagnostic performance of artificial intelligence (AI) in dental and maxillofacial radiology (DMFR).

Methods: Studies using applications related to DMFR to develop or implement AI models were sought by searching five electronic databases and four selected core journals in the field of DMFR. The customized assessment criteria based on QUADAS-2 were adapted for quality analysis of the studies included.

Results: The initial electronic search yielded 1862 titles, and 50 studies were eventually included. Most studies focused on AI applications for an automated localization of cephalometric landmarks, diagnosis of osteoporosis, classification/segmentation of maxillofacial cysts and/or tumors, and identification of periodontitis/periapical disease. The performance of AI models varies among different algorithms.

Conclusion: The AI models proposed in the studies included exhibited wide clinical applications in DMFR. Nevertheless, it is still necessary to further verify the reliability and applicability of the AI models prior to transferring these models into clinical practice.

Keywords: artificial intelligence; computer-assisted; dentistry; diagnostic imaging; radiography.

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Figures

Figure 1.
Figure 1.
PRISMA flowchart illustrating the study selection process
Figure 2.
Figure 2.
The distribution of the included studies published from November 1992 to January 2019, and the proportion of different image modalities (2D/ 3D imaging) used to develop AI models
Figure 3.
Figure 3.
Pie chart for the clinical applications of AI proposed in the included studies

References

    1. Wong SH, Al-Hasani H, Alam Z, Alam A. Artificial intelligence in radiology: how will we be affected? Eur Radiol 2019; 29: 141–3. doi: 10.1007/s00330-018-5644-3 - DOI - PubMed
    1. Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial intelligence in surgery: promises and perils. Ann Surg 2018; 268: 70–6. doi: 10.1097/SLA.0000000000002693 - DOI - PMC - PubMed
    1. Stone P, Brooks R, Brynjolfsson E, Calo R, Etzioni O, Hager G et al. . Artificial Intelligence and Life in 2030. One Hundred Year Study on Artificial Intelligence: Report of the 2015-2016 Study Panel. Stanford, CA: Stanford University; 2016. http://ai100stanfordedu/2016-report.
    1. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. . Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol 2017; 2: 230–43. doi: 10.1136/svn-2017-000101 - DOI - PMC - PubMed
    1. Fazal MI, Patel ME, Tye J, Gupta Y. The past, present and future role of artificial intelligence in imaging. Eur J Radiol 2018; 105: 246–50. doi: 10.1016/j.ejrad.2018.06.020 - DOI - PubMed

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