Scope and performance of artificial intelligence technology in orthodontic diagnosis, treatment planning, and clinical decision-making - A systematic review

Sanjeev B Khanagar, Ali Al-Ehaideb, Satish Vishwanathaiah, Prabhadevi C Maganur, Shankargouda Patil, Sachin Naik, Hosam A Baeshen, Sachin S Sarode, Sanjeev B Khanagar, Ali Al-Ehaideb, Satish Vishwanathaiah, Prabhadevi C Maganur, Shankargouda Patil, Sachin Naik, Hosam A Baeshen, Sachin S Sarode

Abstract

Background/purpose: In the recent years artificial intelligence (AI) has revolutionized in the field of dentistry. The aim of this systematic review was to document the scope and performance of the artificial intelligence based models that have been widely used in orthodontic diagnosis, treatment planning, and predicting the prognosis.

Materials and methods: The literature for this paper was identified and selected by performing a thorough search for articles in the electronic data bases like Pubmed, Medline, Embase, Cochrane, and Google scholar, Scopus and Web of science, Saudi digital library published over the past two decades (January 2000-February 2020). After applying the inclusion and exclusion criteria, 16 articles were read in full and critically analyzed. QUADAS-2 were adapted for quality analysis of the studies included.

Results: AI technology has been widely applied for identifying cephalometric landmarks, determining need for orthodontic extractions, determining the degree of maturation of the cervical vertebra, predicting the facial attractiveness after orthognathic surgery, predicting the need for orthodontic treatment, and orthodontic treatment planning. Most of these artificial intelligence models are based on either artificial neural networks (ANNs) or convolutional neural networks (CNNs).

Conclusion: The results from these reported studies are suggesting that these automated systems have performed exceptionally well, with an accuracy and precision similar to the trained examiners. These systems can simplify the tasks and provide results in quick time which can save the dentist time and help the dentist to perform his duties more efficiently. These systems can be of great value in orthodontics.

Keywords: Artificial intelligence; Artificial neural networks; Automated orthodontic diagnosis; Convolutional neural networks; Deep learning; Machine learning.

Conflict of interest statement

The authors have no conflicts of interest relevant to this article.

© 2020 Association for Dental Sciences of the Republic of China. Publishing services by Elsevier B.V.

Figures

Figure 1
Figure 1
Flow chart for screening and selection of articles.
Figure 2
Figure 2
Trends of research on AI in orthodontics.
Figure 3
Figure 3
Application of AI technology in orthodontics.
Figure 4
Figure 4
Assessment of individual risk of bias domains.
Figure 5
Figure 5
Applicability concerns.

References

    1. Nilsson N.J. Morgan Kaufmann Publishers Inc; 1998. Artificial intelligence: a new synthesis; p. 513.
    1. Katne T., Kanaparthi A., Srikanth Gotoor S., Muppirala S., Devaraju R., Gantala R. Artificial intelligence: demystifying dentistry – the future and beyond. Int J Contemp Med Surg Radiol. 2019;4:D6–D9.
    1. Redelmeier D.A., Shafir E. Medical decision making in situations that offer multiple alternatives. J Am Med Assoc. 1995;273:302–305.
    1. Luger G.F., Stubblefield W.A. Benjamin-Cummings Publishing Co., Inc.; USA: 1993. Artificial intelligence (2nd ed.): structures and strategies for complex problem-solving.
    1. Schaeffer J., Culberson J., Treloar N., Knight B., Lu P., Szafron D. A world championship caliber checkers program. Artif Intell. 1992;53:273–289.
    1. Makaremi M., Lacaule C., Mohammad-Djafari A. Deep learning and artificial intelligence for the determination of the cervical vertebra maturation degree from lateral radiography. Entropy. 2019;21:1222.
    1. Brickley M.R., Shepherd J.P., Armstrong R.A. Neural networks: a new technique for development of decision support systems in dentistry. J Dent. 1998;26:305–309.
    1. McGrath T.A., Alabousi M., Skidmore B. Recommendations for reporting of systematic reviews and meta-analyses of diagnostic test accuracy: a systematic review. Syst Rev. 2017;6:194.
    1. Whiting P.F., Rutjes A.W.S., Westwood M.E. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. 2011;155:529–536.
    1. Leonardi R., Giordano D., Maiorana F. An evaluation of cellular neural networks for the automatic identification of cephalometric landmarks on digital images. J Biomed Biotechnol. 2009:717102.
    1. Mario M.C., Abe J.M., Ortega N.R., Del Santo M., Jr. Paraconsistent artificial neural network as auxiliary in cephalometric diagnosis. Artif Organs. 2010;34:E215–E221.
    1. Arık S.Ö., Ibragimov B., Xing L. Fully automated quantitative cephalometry using convolutional neural networks. J Med Imaging. 2017;4:14501.
    1. Park J.H., Hwang H.W., Moon J.H. Automated identification of cephalometric landmarks: Part 1-Comparisons between the latest deep-learning methods YOLOV3 and SSD. Angle Orthod. 2019;89:903–909.
    1. Kunz F., Stellzig-Eisenhauer A., Zeman F., Boldt J. Artificial intelligence in orthodontics: evaluation of a fully automated cephalometric analysis using a customized convolutional neural network. J Orofac Orthop. 2020;81:52–68.
    1. Hwang H.W., Park J.H., Moon J.H. Automated identification of cephalometric landmarks: Part 2-Might it be better than human? Angle Orthod. 2020;90:69–76.
    1. Xie X., Wang L., Wang A. Artificial neural network modeling for deciding if extractions are necessary prior to orthodontic treatment. Angle Orthod. 2010;80:262–266.
    1. Jung S.K., Kim T.W. New approach for the diagnosis of extractions with neural network machine learning. Am J Orthod Dentofacial Orthop. 2016;149:127–133.
    1. Choi H.I., Jung S.K., Baek S.H. Artificial intelligent model with neural network machine learning for the diagnosis of orthognathic surgery. J Craniofac Surg. 2019;30:1986–1989.
    1. Kök H., Acilar A.M., İzgi M.S. Usage and comparison of artificial intelligence algorithms for determination of growth and development by cervical vertebrae stages in orthodontics. Prog Orthod. 2019;20:41.
    1. Lu C., Ko E.W., Liu L. Improving the video imaging prediction of postsurgical facial profiles with an artificial neural network. J Dent Sci. 2009;4:118–129.
    1. Patcas R., Bernini D.A.J., Volokitin A., Agustsson E., Rothe R., Timofte R. Applying artificial intelligence to assess the impact of orthognathic treatment on facial attractiveness and estimated age. Int J Oral Maxillofac Surg. 2019;48:77–83.
    1. Patcas R., Timofte R., Volokitin A. Facial attractiveness of cleft patients: a direct comparison between artificial-intelligence-based scoring and conventional rater groups. Eur J Orthod. 2019;41:428–433.
    1. Thanathornwong B. Bayesian-based decision support system for assessing the needs for orthodontic treatment. Healthc Inform Res. 2018;24:22–28.
    1. Li P., Kong D., Tang T. Orthodontic treatment planning based on artificial neural networks. Sci Rep. 2019;9:2037.
    1. Mary A.V., Mahendra J., John J., Moses J., Ebenezar A.V.R., Kesavan R. Assessing quality of life using the oral health impact profile (OHIP-14) in subjects with and without orthodontic treatment need in Chennai, Tamil nadu, India. J Clin Diagn Res. 2017;11:ZC78–ZC81.
    1. Luke L.S., Atchison K.A., White S.C. Consistency of patient classification in orthodontic diagnosis and treatment planning. Angle Orthod. 1998;68:513–520.
    1. Leonardi R., Giordano D., Maiorana F., Spampinato C. Automatic cephalometric analysis: a systematic review. Angle Orthod. 2008;78:145–151.
    1. Ribarevski R., Vig P., Vig K.D., Weyant R., O'Brien K. Consistency of orthodontic extraction decisions. Eur J Orthod. 1996;18:77–80.
    1. Dunbar A.C., Bearn D., McIntyre G. The influence of using digital diagnostic information on orthodontic treatment planning - a pilot study. J Healthc Eng. 2014;5:411–427.
    1. Alkhal H.A., Wong R.W., Rabie A.B. Correlation between chronological age, cervical vertebral maturation and fishman's skeletal maturity indicators in southern Chinese. Angle Orthod. 2008;78:591–596.
    1. Litsas G., Ari-Demirkaya A. Growth indicators in orthodontic patients. part 1: comparison of cervical vertebral maturation and hand-wrist skeletal maturation. Eur J Paediatr Dent. 2010;11:171–175.
    1. Navlani M., Makhija P.G. Evaluation of skeletal and dental maturity indicators and assessment of cervical vertebral maturation stages by height/width ratio of third cervical vertebra. J Pierre Fauchard Acad (India Section) 2013;27:73–80.
    1. Eichenberger M., Staudt C.B., Pandis N., Gnoinski W., Eliades T. Facial attractiveness of patients with unilateral cleft lip and palate and of controls assessed by laypersons and professionals. Eur J Orthod. 2014;36:284–289.

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