Applications of artificial intelligence and machine learning in orthodontics: a scoping review

Yashodhan M Bichu, Ismaeel Hansa, Aditi Y Bichu, Pratik Premjani, Carlos Flores-Mir, Nikhilesh R Vaid, Yashodhan M Bichu, Ismaeel Hansa, Aditi Y Bichu, Pratik Premjani, Carlos Flores-Mir, Nikhilesh R Vaid

Abstract

Introduction: This scoping review aims to provide an overview of the existing evidence on the use of artificial intelligence (AI) and machine learning (ML) in orthodontics, its translation into clinical practice, and what limitations do exist that have precluded their envisioned application.

Methods: A scoping review of the literature was carried out following the PRISMA-ScR guidelines. PubMed was searched until July 2020.

Results: Sixty-two articles fulfilled the inclusion criteria. A total of 43 out of the 62 studies (69.35%) were published this last decade. The majority of these studies were from the USA (11), followed by South Korea (9) and China (7). The number of studies published in non-orthodontic journals (36) was more extensive than in orthodontic journals (26). Artificial Neural Networks (ANNs) were found to be the most commonly utilized AI/ML algorithm (13 studies), followed by Convolutional Neural Networks (CNNs), Support Vector Machine (SVM) (9 studies each), and regression (8 studies). The most commonly studied domains were diagnosis and treatment planning-either broad-based or specific (33), automated anatomic landmark detection and/or analyses (19), assessment of growth and development (4), and evaluation of treatment outcomes (2). The different characteristics and distribution of these studies have been displayed and elucidated upon therein.

Conclusion: This scoping review suggests that there has been an exponential increase in the number of studies involving various orthodontic applications of AI and ML. The most commonly studied domains were diagnosis and treatment planning, automated anatomic landmark detection and/or analyses, and growth and development assessment.

Keywords: Artificial intelligence; Machine learning; Orthodontics.

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
PRISMA flow diagram of the scoping review

References

    1. Asiri SN, Tadlock LP, Schneiderman E, Buschang PH. Applications of artificial intelligence and machine learning in orthodontics. APOS Trends Orthod. 2020;10(1):17–24. doi: 10.25259/APOS_117_2019.
    1. Haugeland J. Artificial intelligence: the very idea. Cambridge: Massachusetts Insitute of Technology; 1985.
    1. Morris CG. Academic Press Dictionary of Science Technology. San Diego: Academic Press; 1996.
    1. Luger GF. Artificial intelligence: structures and strategies for complex problem solving. Harlow: Pearson Education; 2005.
    1. McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys. 1943;5(4):115–133. doi: 10.1007/BF02478259.
    1. Negnevitsky M. Artificial intelligence: a guide to intelligent systems. Pearson Education: Canada; 2005.
    1. Faber J, Faber C, Faber P. Artificial intelligence in orthodontics. APOS Trends Orthod. 2019;9(4):201–205. doi: 10.25259/APOS_123_2019.
    1. Could Artificial Intelligence Transform Healthcare? Morgan Stanley. Available from: . Accessed 12 Dec 2020.
    1. Michael W, Haugeland J. Artificial intelligence: the very idea. Technol Culture. 1987;28(4):905–922.
    1. Schwartz WB, Patil RS, Szolovits P. Artificial intelligence in medicine. Where do we stand? N Engl J Med. 1987;316(11):685–688. doi: 10.1056/NEJM198703123161109.
    1. Bishop CM. Pattern recognition and machine learning. New York: Springer; 2006.
    1. Ko C, Tanikawa C, Wu T, Pastewait Matthew, Jackson C B, Kwon JJ, Lee Y, Lian C, Wang Li, Shen D Machine learning in orthodontics: application review. Embracing novel technologies in dentistry and orthodontics- Forty-sixth Annual Moyers Symposium and the Forty-fourth Annual International Conference on Craniofacial Research March 1-3, 2019, Ann Arbor, Michigan.
    1. Sumathi S, Sivanandam S. Introduction to data mining principles. Introduction to Data Mining and its Applications. Studies in Computational Intelligence. Berlin, Heidelberg: Springer; 2006. pp. 1–20.
    1. Mitchell TM. Machine learning and data mining. Commun ACM. 1999;42:1–13. doi: 10.1145/319382.319388.
    1. Breiman L. Statistical modeling: the two cultures (with comments and a rejoinder by the author) Stat Sci. 2001;16:199–231. doi: 10.1214/ss/1009213726.
    1. Vaid N. Scoping studies: should there be more in orthodontic literature? APOS Trends Orthod. 2019;9(3):124–125. doi: 10.25259/APOS_118_2019.
    1. Arksey H, O'Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. 2005;8(1):19–32. doi: 10.1080/1364557032000119616.
    1. Suhail Y, Upadhyay M, Chhibber A, Kshitiz Machine learning for the diagnosis of orthodontic extractions: a computational analysis using ensemble learning. Bioengineering. 2020;7:55. doi: 10.3390/bioengineering7020055.
    1. Muraev AA, Tsai P, Kibardin I, Oborotistov N, Shirayeva T, Ivanov S, Ivanov S, Guseynov N, Aleshina O, Bosykh Y, Safyanova E, Andreischev A, Rudoman S, Dolgalev A, Matyuta M, Karagodsky V, Tuturov N. Frontal cephalometric landmarking: humans vs artificial neural networks. Int J Comput Dent. 2020;23(2):139–148.
    1. Kök H, Acilar AM, İzgi MS. Usage and comparison of artificial intelligence algorithms for determination of growth and development by cervical vertebrae stages in orthodontics. Prog Orthod. 2019;20(1):41. doi: 10.1186/s40510-019-0295-8.
    1. Choi HI, Jung SK, Baek SH, Lim WH, Ahn SJ, Yang IH, Kim TW. Artificial intelligent model with neural network machine learning for the diagnosis of orthognathic surgery. J Craniofac Surg. 2019;30(7):1986–1989. doi: 10.1097/SCS.0000000000005650.
    1. Shoukri B, Prieto JC, Ruellas A, Yatabe M, Sugai J, Styner M, Zhu H, Huang C, Paniagua B, Aronovich S, Ashman L, Benavides E, de Dumast P, Ribera NT, Mirabel C, Michoud L, Allohaibi Z, Ioshida M, Bittencourt L, Fattori L, Gomes LR, Cevidanes L. Minimally invasive approach for diagnosing TMJ osteoarthritis. J Dent Res. 2019;98(10):1103–1111. doi: 10.1177/0022034519865187.
    1. Li P, Kong D, Tang T, Su D, Yang P, Wang H, Zhao Z, Liu Y. Orthodontic treatment planning based on artificial neural networks. Sci Rep. 2019;9(1):2037. doi: 10.1038/s41598-018-38439-w.
    1. Niño-Sandoval TC, Guevara Pérez SV, González FA, Jaque RA, Infante-Contreras C. Use of automated learning techniques for predicting mandibular morphology in skeletal class I, II and III. Forensic Sci Int. 2017;281:187.e1–187.e7. doi: 10.1016/j.forsciint.2017.10.004.
    1. Jung SK, Kim TW. New approach for the diagnosis of extractions with neural network machine learning. Am J Orthod Dentofacial Orthop. 2016;149(1):127–133. doi: 10.1016/j.ajodo.2015.07.030.
    1. Aksakalli S, Demir A, Selek M, Tasdemir S. Temperature increase during orthodontic bonding with different curing units using an infrared camera. Acta Odontol Scand. 2014;72(1):36–41. doi: 10.3109/00016357.2013.794954.
    1. Mario MC, Abe JM, Ortega NR, Del Santo M., Jr Paraconsistent artificial neural network as auxiliary in cephalometric diagnosis. Artif Organs. 2010;34(7):E215–E221. doi: 10.1111/j.1525-1594.2010.00994.x.
    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(2):262–266. doi: 10.2319/111608-588.1.
    1. Akdenur B, Okkesum S, Kara S, Günes S. Correlation- and covariance-supported normalization method for estimating orthodontic trainer treatment for clenching activity. Proc Inst Mech Eng H. 2009;223(8):991–1001. doi: 10.1243/09544119JEIM619.
    1. Lux CJ, Stellzig A, Volz D, Jäger W, Richardson A, Komposch G. A neural network approach to the analysis and classification of human craniofacial growth. Growth Dev Aging. 1998;62(3):95–106.
    1. Ma Q, Kobayashi E, Fan B, Nakagawa K, Sakuma I, Masamune K, Suenaga H. Automatic 3D landmarking model using patch-based deep neural networks for CT image of oral and maxillofacial surgery. Int J Med Robot. 2020;16(3):e2093. doi: 10.1002/rcs.2093.
    1. Yu HJ, Cho SR, Kim MJ, Kim WH, Kim JW, Choi J. Automated skeletal classification with lateral cephalometry based on artificial intelligence. J Dent Res. 2020;99(3):249–256. doi: 10.1177/0022034520901715.
    1. Chung M, Lee M, Hong J, Park S, Lee J, Lee J, Yang IH, Lee J, Shin YG. Pose-aware instance segmentation framework from cone beam CT images for tooth segmentation. Comput Biol Med. 2020;120:103720. doi: 10.1016/j.compbiomed.2020.103720.
    1. Lee KS, Jung SK, Ryu JJ, Shin SW, Choi J. Evaluation of transfer learning with deep convolutional neural networks for screening osteoporosis in dental panoramic radiographs. J Clin Med. 2020;9(2):392. doi: 10.3390/jcm9020392.
    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(1):52-68. English. doi: 10.1007/s00056-019-00203-8. Epub 2019 Dec 18.
    1. Patcas R, Timofte R, Volokitin A, Agustsson E, Eliades T, Eichenberger M, Bornstein MM. Facial attractiveness of cleft patients: a direct comparison between artificial-intelligence-based scoring and conventional rater groups. Eur J Orthod. 2019;41(4):428–433. doi: 10.1093/ejo/cjz007.
    1. Nishimoto S, Sotsuka Y, Kawai K, Ishise H, Kakibuchi M. Personal computer-based cephalometric landmark detection with deep learning, using cephalograms on the internet. J Craniofac Surg. 2019;30(1):91–95. doi: 10.1097/SCS.0000000000004901.
    1. Patcas R, Bernini DAJ, 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(1):77–83. doi: 10.1016/j.ijom.2018.07.010.
    1. Spampinato C, Palazzo S, Giordano D, Aldinucci M, Leonardi R. Deep learning for automated skeletal bone age assessment in X-ray images. Med Image Anal. 2017;36:41–51. doi: 10.1016/j.media.2016.10.010.
    1. Yeom SH, Na JS, Jung HD, Cho HJ, Choi YJ, Lee JS. Computational analysis of airflow dynamics for predicting collapsible sites in the upper airways: machine learning approach. J Appl Physiol (1985) 2019;127(4):959–973. doi: 10.1152/japplphysiol.01033.2018.
    1. Hwang JJ, Lee JH, Han SS, Kim YH, Jeong HG, Choi YJ, Park W. Strut analysis for osteoporosis detection model using dental panoramic radiography. Dentomaxillofac Radiol. 2017;46(7):20170006. doi: 10.1259/dmfr.20170006.
    1. Wang X, Cai B, Cao Y, Zhou C, Yang L, Liu R, Long X, Wang W, Gao D, Bao B. Objective method for evaluating orthodontic treatment from the lay perspective: an eye-tracking study. Am J Orthod Dentofacial Orthop. 2016;150(4):601–610. doi: 10.1016/j.ajodo.2016.03.028.
    1. Mortaheb P, Rezaeian M. Metal artifact reduction and segmentation of dental computerized tomography images using least square support vector machine and mean shift algorithm. J Med Signals Sens. 2016;6(1):1–11. doi: 10.4103/2228-7477.175867.
    1. Niño-Sandoval TC, Guevara Perez SV, González FA, Jaque RA, Infante-Contreras C. An automatic method for skeletal patterns classification using craniomaxillary variables on a Colombian population. Forensic Sci Int. 2016261:159.e1-159.e6. doi: 10.1016/j.forsciint.2015.12.025. Epub 2015 Dec 25.
    1. Yu X, Liu B, Pei Y, Xu T. Evaluation of facial attractiveness for patients with malocclusion: a machine-learning technique employing Procrustes. Angle Orthod. 2014;84(3):410–416. doi: 10.2319/071513-516.1.
    1. Kim BM, Kang BY, Kim HG, Baek SH. Prognosis prediction for class III malocclusion treatment by feature wrapping method. Angle Orthod. 2009;79(4):683–691. doi: 10.2319/071508-371.1.
    1. Bianchi J, de Oliveira Ruellas AC, Gonçalves JR, Paniagua B, Prieto JC, Styner M, Li T, Zhu H, Sugai J, Giannobile W, Benavides E, Soki F, Yatabe M, Ashman L, Walker D, Soroushmehr R, Najarian K, Cevidanes LHS. Osteoarthritis of the temporomandibular joint can be diagnosed earlier using biomarkers and machine learning. Sci Rep. 2020;10(1):8012. doi: 10.1038/s41598-020-64942-0.
    1. Zhang SJ, Meng P, Zhang J, Jia P, Lin J, Wang X, Chen F, Wei X. Machine learning models for genetic risk assessment of infants with non-syndromic orofacial cleft. Genomics Proteomics Bioinformatics. 2018;16(5):354–364. doi: 10.1016/j.gpb.2018.07.005.
    1. Sorihashi Y, Stephens CD, Takada K. An inference modeling of human visual judgment of sagittal jaw-base relationships based on cephalometry: part II. Am J Orthod Dentofacial Orthop. 2000;117(3):303–311. doi: 10.1016/s0889-5406(00)70235-6.
    1. Tolpadi AA, Stone ML, Carass A, Prince JL, Gomez AD. Inverse biomechanical modeling of the tongue via machine learning and synthetic training data. Proc SPIE Int Soc Opt Eng. 2018;10576:1057606. doi: 10.1117/12.2296927.
    1. Laurenziello M, Montaruli G, Gallo C, Tepedino M, Guida L, Perillo L, Troiano G, Lo Muzio L, Ciavarella D. Determinants of maxillary canine impaction: retrospective clinical and radiographic study. J Clin Exp Dent. 2017;9(11):e1304–e1309. doi: 10.4317/jced.54095.
    1. Allareddy V, Wang T, Rampa S, Caplin J, Nalliah R, Badheka A, Allareddy V. Prevalence and predictors of C. difficile infections in hospitalized patients with major surgical procedures in the USA: analysis using traditional and machine learning methods. Am J Surg. 2019;218(3):661–662. doi: 10.1016/j.amjsurg.2018.11.014.
    1. Thanathornwong B. Bayesian-based decision support system for assessing the needs for orthodontic treatment. Healthc Inform Res. 2018;24(1):22–28. doi: 10.4258/hir.2018.24.1.22.
    1. Nieri M, Crescini A, Rotundo R, Baccetti T, Cortellini P, Pini Prato GP. Factors affecting the clinical approach to impacted maxillary canines: a Bayesian network analysis. Am J Orthod Dentofacial Orthop. 2010;137(6):755–762. doi: 10.1016/j.ajodo.2008.08.028.
    1. Stephens C, Mackin N. The validation of an orthodontic expert system rule-base for fixed appliance treatment planning. Eur J Orthod. 1998;20(5):569–578. doi: 10.1093/ejo/20.5.569.
    1. Stephens CD, Mackin N, Sims-Williams JH. The development and validation of an orthodontic expert system. Br J Orthod. 1996;23(1):1–9. doi: 10.1179/bjo.23.1.1.
    1. Brown ID, Adams SR, Stephens CD, Erritt SJ, Sims-Williams JH. The initial use of a computer-controlled expert system in the treatment planning of class II division 1 malocclusion. Br J Orthod. 1991;18(1):1–7. doi: 10.1179/bjo.18.1.1.
    1. Montúfar J, Romero M, Scougall-Vilchis RJ. Hybrid approach for automatic cephalometric landmark annotation on cone-beam computed tomography volumes. Am J Orthod Dentofacial Orthop. 2018;154(1):140–150. doi: 10.1016/j.ajodo.2017.08.028.
    1. Montúfar J, Romero M, Scougall-Vilchis RJ. Automatic 3-dimensional cephalometric landmarking based on active shape models in related projections. Am J Orthod Dentofacial Orthop. 2018;153(3):449–458. doi: 10.1016/j.ajodo.2017.06.028.
    1. Gupta A, Kharbanda OP, Sardana V, Balachandran R, Sardana HK. Accuracy of 3D cephalometric measurements based on an automatic knowledge-based landmark detection algorithm. Int J Comput Assist Radiol Surg. 2016;11(7):1297–1309. doi: 10.1007/s11548-015-1334-7.
    1. Gupta A, Kharbanda OP, Sardana V, Balachandran R, Sardana HK. A knowledge-based algorithm for automatic detection of cephalometric landmarks on CBCT images. Int J Comput Assist Radiol Surg. 2015;10(11):1737–1752. doi: 10.1007/s11548-015-1173-6.
    1. Auconi P, Scazzocchio M, Cozza P, McNamara JA, Jr, Franchi L. Prediction of class III treatment outcomes through orthodontic data mining. Eur J Orthod. 2015;37(3):257–267. doi: 10.1093/ejo/cju038.
    1. Akçam MO, Takada K. Fuzzy modelling for selecting headgear types. Eur J Orthod. 2002;24(1):99–106. doi: 10.1093/ejo/24.1.99.
    1. Vucinić P, Trpovski Z, Sćepan I. Automatic landmarking of cephalograms using active appearance models. Eur J Orthod. 2010;32(3):233–241. doi: 10.1093/ejo/cjp099.
    1. Rueda S, Alcañiz M. An approach for the automatic cephalometric landmark detection using mathematical morphology and active appearance models. Med Image Comput Comput Assist Interv. 2006;9(Pt 1):159–166. doi: 10.1007/11866565_20.
    1. Ribera NT, de Dumast P, Yatabe M, Ruellas A, Ioshida M, Paniagua B, Styner M, Gonçalves JR, Bianchi J, Cevidanes L, Prieto JC. Shape variation analyzer: a classifier for temporomandibular joint damaged by osteoarthritis. Proc SPIE Int Soc Opt Eng. 2019;10950:1095021. doi: 10.1117/12.2506018.
    1. de Dumast P, Mirabel C, Cevidanes L, Ruellas A, Yatabe M, Ioshida M, Ribera NT, Michoud L, Gomes L, Huang C, Zhu H, Muniz L, Shoukri B, Paniagua B, Styner M, Pieper S, Budin F, Vimort JB, Pascal L, Prieto JC. A web-based system for neural network based classification in temporomandibular joint osteoarthritis. Comput Med Imaging Graph. 2018;67:45–54. doi: 10.1016/j.compmedimag.2018.04.009.
    1. Takada K, Yagi M, Horiguchi E. Computational formulation of orthodontic tooth-extraction decisions. Part I: to extract or not to extract. Angle Orthod. 2009;79(5):885–891. doi: 10.2319/081908-436.1.
    1. Grau V, Alcañiz M, Juan MC, Monserrat C, Knoll C. Automatic localization of cephalometric Landmarks. J Biomed Inform. 2001;34(3):146–156. doi: 10.1006/jbin.2001.1014.
    1. Kim H, Shim E, Park J, Kim YJ, Lee U, Kim Y. Web-based fully automated cephalometric analysis by deep learning. Comput Methods Programs Biomed. 2020;194:105513. doi: 10.1016/j.cmpb.2020.105513.
    1. Chen S, Wang L, Li G, Wu TH, Diachina S, Tejera B, Kwon JJ, Lin FC, Lee YT, Xu T, Shen D, Ko CC. Machine learning in orthodontics: introducing a 3D auto-segmentation and auto-landmark finder of CBCT images to assess maxillary constriction in unilateral impacted canine patients. Angle Orthod. 2020;90(1):77–84. doi: 10.2319/012919-59.1.
    1. Skotko BG, Macklin EA, Muselli M, Voelz L, McDonough ME, Davidson E, Allareddy V, Jayaratne YS, Bruun R, Ching N, Weintraub G, Gozal D, Rosen D. A predictive model for obstructive sleep apnea and Down syndrome. Am J Med Genet A. 2017;173(4):889–896. doi: 10.1002/ajmg.a.38137.
    1. Auconi P, Caldarelli G, Scala A, Ierardo G, Polimeni A. A network approach to orthodontic diagnosis. Orthod Craniofac Res. 2011;14(4):189–197. doi: 10.1111/j.1601-6343.2011.01523.x.
    1. Tanikawa C, Yagi M, Takada K. Automated cephalometry: system performance reliability using landmark-dependent criteria. Angle Orthod. 2009;79(6):1037–1046. doi: 10.2319/092908-508R.1.
    1. Rudolph DJ, Sinclair PM, Coggins JM. Automatic computerized radiographic identification of cephalometric landmarks. Am J Orthod Dentofacial Orthop. 1998;113(2):173–179. doi: 10.1016/s0889-5406(98)70289-6.
    1. Ed-Dhahraouy M, Riri H, Ezzahmouly M, Bourzgui F, El Moutaoukkil A. A new methodology for automatic detection of reference points in 3D cephalometry: a pilot study. Int Orthod. 2018;16(2):328–337. doi: 10.1016/j.ortho.2018.03.013.
    1. Neelapu BC, Kharbanda OP, Sardana V, Gupta A, Vasamsetti S, Balachandran R, Sardana HK. Automatic localization of three-dimensional cephalometric landmarks on CBCT images by extracting symmetry features of the skull. Dentomaxillofac Radiol. 2018;47(2):20170054. doi: 10.1259/dmfr.20170054.
    1. Tanikawa C, Yamamoto T, Yagi M, Takada K. Automatic recognition of anatomic features on cephalograms of preadolescent children. Angle Orthod. 2010;80(5):812–820. doi: 10.2319/092909-474.1.
    1. Hammond RM, Freer TJ. Application of a case-based expert system to orthodontic diagnosis and treatment planning. Aust Orthod J. 1997;14(4):229–234.
    1. Gandedkar NH, Vaid NR, Darendeliler MA, Premjani P, Ferguson DJ. The last decade in orthodontics: a scoping review of the hits, misses and the near misses! Semin in Orthod. Vol. 2019;25(4):339–355.
    1. Vaid NR, Hansa I, Bichu Y. Smartphone applications used in orthodontics: a scoping review of scholarly literature. J World Fed Orthod. 2020;9(3S):S67–S73. doi: 10.1016/j.ejwf.2020.08.007.
    1. Lévy-Mandel AD, Venetsanopoulos AN, Tsotsos JK. Knowledge-based landmarking of cephalograms. Comput Biomed Res. 1986;19(3):282–309. doi: 10.1016/0010-4809(86)90023-6.
    1. Brodie AG. Behavior of normal and abnormal facial growth patterns. Am J Orthod Dent Orthop. 1941;27:633–647.
    1. Broadbent BH. The face of the normal child. Angle Orthod. 1937;7:183–208.
    1. Ricketts RM. A principle of arcial growth of the mandible. Angle Orthod. 1972;42(4):368–386. doi: 10.1043/0003-3219(1972)042<0368:APOAGO>;2.

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