Acessibilidade / Reportar erro

Technology-enhanced simulation-based learning in orthodontic education: A scoping review

ABSTRACT

Introduction:

Technology-enhanced simulations seem to be effective in dentistry, as they can support dental students to improve competencies in simulated environments. However, implementation of this technology in orthodontic education has not been reviewed.

Objective:

This scoping review aimed to comprehensively summarize the use of technology-enhanced simulations in orthodontic practice.

Methods:

A systematic search was conducted to identify literature on technology-enhanced simulation-based learning in orthodontic education published from 2000 to 2021. The search was conducted up to September 2021 to identify articles from Scopus, Embase, PubMed, ProQuest Dissertations & Theses Global, Google Scholar and the reference lists of identified articles.

Results:

The search identified 177 articles. Following the inclusion and exclusion criteria, 16 articles of 14 digital simulators were included in this review. The findings demonstrated an increasing use of technology-enhanced simulations in orthodontic education. They were designed in several formats, including three-dimensional virtual format, augmented reality, virtual reality, automaton, haptic, and scenario-based simulations. These simulations were implemented in varied areas of orthodontics including diagnosis and treatment planning, bracket positioning, orthodontic procedures, facial landmark, removable appliance and cephalometric tracing. Most included articles demonstrated the development process without outcome evaluation. Six studies provided outcome evaluations at reaction or learning levels. None of them provide the evaluation at behaviour and results levels.

Conclusion:

Insufficient evidence has been generated to demonstrate the effectiveness of technology-enhanced simulations in orthodontic education. However, high-fidelity computer-based simulations together with robust design research should be required to confirm educational impact in orthodontic education.

Keywords:
Dental education; Orthodontics; Serious game; Simulation; Technology-enhanced learning

RESUMO

Introdução:

Simulações aprimoradas por tecnologia parecem ser eficazes em Odontologia, pois podem ajudar os estudantes a melhorar suas competências em ambientes simulados. No entanto, a implementação dessa tecnologia na educação ortodôntica ainda não foi revisada.

Objetivo:

A presente revisão de escopo teve como objetivo resumir, de forma abrangente, o uso de simulações aprimoradas por tecnologia na prática ortodôntica.

Métodos:

Uma busca sistemática foi realizada para identificar publicações de 2000 a 2021 sobre aprendizado na educação ortodôntica baseado em simulação aprimorada por tecnologia. A busca foi realizada até setembro de 2021 para identificar artigos na Scopus, Embase, PubMed, ProQuest Dissertations & Theses Global, Google Scholar e nas listas de referências dos artigos identificados.

Resultados:

A busca identificou 177 artigos. Foram incluídos nessa revisão 16 artigos, com 14 simulações digitais, que atenderam aos critérios de inclusão e exclusão. Os resultados demonstraram um uso crescente de simulações aprimoradas por tecnologia na educação ortodôntica. Elas foram projetadas em vários formatos, incluindo formato tridimensional virtual, realidade aumentada, realidade virtual, autômato, háptico e simulações baseadas em cenários. Essas simulações foram implementadas em diversas áreas da Ortodontia, incluindo diagnóstico e planejamento de tratamento, posicionamento de braquetes, procedimentos ortodônticos, identificação de pontos de referência faciais, aparelhos removíveis e traçados cefalométricos. A maioria dos artigos incluídos demonstrou o processo de desenvolvimento, sem avaliar os resultados. Seis estudos forneceram avaliações de resultados em níveis de reação ou aprendizado. Nenhum deles forneceu a avaliação em níveis de comportamento e resultados.

Conclusão:

Não foram geradas evidências suficientes para demonstrar a eficácia das simulações aprimoradas por tecnologia na educação ortodôntica. No entanto, simulações de alta fidelidade baseadas em computador, juntamente com pesquisas robustas de design, são necessárias para confirmar o impacto educacional na área ortodôntica.

Palavras-chave:
Educação odontológica; Ortodontia; Jogo sério; Simulação; Aprendizagem aprimorada por tecnologia

INTRODUCTION

The COVID-19 pandemic has widely impacted a variety of areas, including the educational field. In this context, emphasis on hybrid learning (presential/distance) is unavoidable to minimize the risk of infection. The concerns of physical distancing have been raised in healthcare education, including orthodontic practice. Unlike other educational areas, the emphasis of orthodontic education is to improve psychomotor skills, in addition to cognitive and affective domains.11 Afify AR, Zawawi KH, Othman HI, Al-Dharrab AA. Correlation of psychomotor skills and didactic performance among dental students in Saudi Arabia. Adv Med Educ Pract. 2013 Oct;4:223-6. Technology-enhanced simulation-based learning can be designed in varied formats, such as digital simulators, augmented reality (AR), virtual reality (VR) and serious games, to enhance knowledge and skills in dental practice.22 Perry S, Bridges SM, Burrow MF. A review of the use of simulation in dental education. Simul Healthc. 2015 Feb;10(1):31-7.

3 Joda T, Gallucci GO, Wismeijer D, Zitzmann NU. Augmented and virtual reality in dental medicine: a systematic review. Comput Biol Med. 2019 May;108:93-100.
-44 Sipiyaruk K, Hatzipanagos S, Reynolds PA, Gallagher JE. Serious games and the COVID-19 pandemic in dental education: an integrative review of the literature. Computers. 2021;10(4):42. All of these options should be considered for orthodontic training.

Orthodontic practice requires knowledge and skills in various areas, such as anatomy of head and neck, growth and development, physiology and biomechanics of tooth movement.55 Graber LW, Vanarsdall RL, Vig KWL, Huang GJ. Orthodontics: current principles and techniques. 6th ed. St. Louis: Elsevier; 2016.,66 Proffit WR, Fields HW, Larson B, Sarver DM. Contemporary orthodontics. 6th ed. St. Louis: Elsevier; 2018. Furthermore, the learning outcomes of orthodontic programs generally require varied psychomotor skills, such as wire bending, bracket positioning, and tooth stripping for interproximal reduction.66 Proffit WR, Fields HW, Larson B, Sarver DM. Contemporary orthodontics. 6th ed. St. Louis: Elsevier; 2018. Residents are also required to be competent in the affective domain, to communicate with patients and their guardians or to deal with orthodontics-related psychological concerns among patients.77 Athanasiou AE, Darendeliler MA, Eliades T, Hägg U, Larson BE, Pirttiniemi P, et al. World Federation of Orthodontists (WFO) guidelines for postgraduate orthodontic education. World J Orthod. 2009;10(2):153-66. Therefore, learning and practice in laboratories followed by clinical settings should be necessarily designed for all orthodontic postgraduate programs.

The COVID-19 outbreak seems to have negative impact on orthodontic practice, evidenced by a delay of orthodontic treatment caused by the lockdown or quarantine.88 Morosan H. Orthodontic treatment in times of Covid-19. J Med Life. 2021;14(2):205-9.,99 Suri S, Vandersluis YR, Kochhar AS, Bhasin R, Abdallah MN. Clinical orthodontic management during the COVID-19 pandemic. Angle Orthod. 2020 Jul;90(4):473-84. There is evidence demonstrating a decrease in the number of new-patient visits during the pandemic.1010 Yavan MA. Effects of the COVID-19 pandemic on new patient visits for orthodontic treatment: a comparison of 2020 and the previous 3 years. J World Fed Orthod. 2021 Sep;10(3):127-31. Patients may feel unsafe or experience restrictions to attend the orthodontic appointments.1111 Cotrin P, Peloso RM, Oliveira RC, Oliveira RCG, Pini NIP, Valarelli FP, et al. Impact of coronavirus pandemic in appointments and anxiety/concerns of patients regarding orthodontic treatment. Orthod Craniofac Res. 2020 Nov;23(4):455-61.,1212 Umeh OD, Utomi IL, Isiekwe IG, Aladenika ET. Impact of the coronavirus disease 2019 pandemic on orthodontic patients and their attitude to orthodontic treatment. Am J Orthod Dentofacial Orthop. 2021 May;159(5):e399-409. They may also have financial problems related to the pandemic.1212 Umeh OD, Utomi IL, Isiekwe IG, Aladenika ET. Impact of the coronavirus disease 2019 pandemic on orthodontic patients and their attitude to orthodontic treatment. Am J Orthod Dentofacial Orthop. 2021 May;159(5):e399-409. A survey in 69 dental schools found a restriction of clinical practice during the pandemic, as only urgent or emergency services were permitted.1313 Quinn B, Field J, Gorter R, Akota I, Manzanares MC, Paganelli C, et al. COVID-19: the immediate response of European academic dental institutions and future implications for dental education. Eur J Dent Educ. 2020 Nov;24(4):811-4. This situation seems to be a major challenge for orthodontic education, where training in clinical settings is highly required.1414 Artese F. Covid 19 pandemic unveiling the opportunities and challenges in orthodontic training. Dental Press J Orthod. 2020;25(3):7-8. The consideration of appropriate substitutes to clinical practice should be required to ensure that graduates will be able to achieve the expected learning goals.

Unlike a cognitive domain, which can be replaced by an online format or a hybrid learning model, orthodontic practice requires clinical techniques learning, where technology-enhanced simulation-based learning can play an important role to offer substitutes or supplements to clinical practice. There is evidence reporting that digital simulators can enhance psychomotor skills, preparing dental undergraduates for practice in clinical settings.1515 Higgins D, Hayes M, Taylor J, Wallace J. A scoping review of simulation-based dental education. MedEdPublish. 2020; 9:36.,1616 Perry S, Bridges SM, Burrow MF. A conceptual model for clinical psychomotor skill development in an era of simulated and virtual reality. Eur J Dent Educ. 2022 May;26(2):263-76. These technology-enhanced simulations can be applied in various fields of dental education, including prosthodontics, endodontics, maxillofacial surgery, periodontology, radiology, pediatric dentistry and orthodontics.1717 Moussa R, Alghazaly A, Althagafi N, Eshky R, Borzangy S. Effectiveness of virtual reality and interactive simulators on dental education outcomes: systematic review. Eur J Dent. 2022 Feb;16(1):14-31.

Despite the high setup costs and trained staff required, technology-enhanced simulators can be considered more effective than traditional simulations, in terms of unlimited training sessions with objective and repetitive feedback,22 Perry S, Bridges SM, Burrow MF. A review of the use of simulation in dental education. Simul Healthc. 2015 Feb;10(1):31-7. resulting in higher efficiency of teaching and learning in dental education.

Although technology-enhanced simulation-based learning has been used to assist dental education, no review of its implementation into clinical training of orthodontic practice could be found. Consequently, this scoping review was conducted to comprehensively analyze empirical studies of the use of technology-enhanced simulation-based learning in orthodontic practice. The knowledge and understanding retrieved from this review would be supportive for dental educators to systematically and comprehensively consider the design and implementation of technology-enhanced simulations to provide optimum settings for teaching and training in orthodontic education.

METHODS

REVIEW DESIGN

A scoping review of the literature was considered as the most appropriate method to synthesize the use of technology-enhanced simulation-based learning. The purposes of this type of review are to identify key concepts, characteristics, available evidence and research gaps of an interesting topic.1818 Munn Z, Peters MDJ, Stern C, Tufanaru C, McArthur A, Aromataris E. Systematic re-view or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Med Res Methodol. 2018 Nov;18(1):143. This design is also appropriate for a complex issue, especially when it has not been yet comprehensively reviewed.1919 Sucharew H, Macaluso M. Progress notes: methods for research evidence synthesis: the scoping review approach. J Hosp Med. 2019 Jul;14(7):416-8. The review process comprises six stages, as follows: 1) identify research questions or purposes, 2) identify relevant literature through systematic searches, 3) select articles in accordance with inclusion and exclusion criteria, 4) analyze the data retrieved from the identified evidence, 5) collate, summarize, and report results; and 6) consult external stakeholders for further suggestions or insights to the review (optional).2020 Levac D, Colquhoun H, O'Brien KK. Scoping studies: advancing the methodology. Implement Sci. 2010 Sep;5:69. This report follows the PRISMAS checklist for scoping reviews.2121 Tricco AC, Lillie E, Zarin W, O'Brien KK, Colquhoun H, Levac D, et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. 2018 Oct;169(7):467-73.

RESEARCH QUESTIONS

This scoping review sought to answer the following questions:

  • » What was the trend in the current use of technology-enhanced simulation-based learning in orthodontic education?

  • » What types of technology-enhanced simulation-based learning were made available to orthodontic education?

  • » What were educational outcomes of available technology-enhanced simulation-based learning in orthodontic training?

SEARCH STRATEGY

Literature search was performed across four databases, including Scopus, Embase, PubMed, and ProQuest Dissertations & Theses Global. Google Scholar and the reference lists of identified articles were also screened for relevant literature. Search terms was developed following the PICOS strategy,2222 Methley AM, Campbell S, Chew-Graham C, McNally R, Cheraghi-Sohi S. PICO, PICOS and SPIDER: a comparison study of specificity and sensitivity in three search tools for qualitative systematic reviews. BMC Health Serv Res. 2014 Nov;14:579. including: Population = ‘Orthodontic student’ and ‘Orthodontist’; Intervention = ‘Simulation’, ‘Virtual reality’, ‘Augmented reality’, and ‘Video game’; Comparison = ‘No intervention’ and ‘Traditional approach’; Outcome = ‘Knowledge’, ‘Skill’, and ‘Competency’; and Study = ‘Any type of studies’. However, only ‘Population’, ‘Intervention’, and ‘Outcomes’ were implemented, as well as ‘orthodontic’ and ‘orthodontics’ were used instead of ‘Orthodontic student’ and ‘Orthodontist’, to extend the search, covering as many as available publications. As several articles identified from the initial search were technical reports demonstrating only the development process of simulations, the terms ‘training’, ‘education’, ‘learning’, and ‘teaching’ were considered for ‘Outcome’, rather than ‘Knowledge’, ‘Skill’, and ‘Competency’. Moreover, the search terms for ‘Outcome’ were still required to enhance the emphasis on the use of technology-enhanced simulation on educational purposes, rather than its use as a process of orthodontic treatment. The search process was iteratively performed and adjusted to ensure its robustness before conducting the final search.2323 Snyder H. Literature review as a research methodology: An overview and guidelines. J Bus Res. 2019 Nov;104: 333-9. The last search was conducted on September 30, 2021.

INCLUSION AND EXCLUSION CRITERIA

All types of empirical studies of technology-enhanced simulation-based learning in orthodontic education published from January 2000 to September 2021 were included in this review. Grey literature was also expected to cover technology-enhanced simulation in orthodontic education wherever possible; however, the references were excluded if fail to include technology-enhanced simulations or were not used for teaching or training orthodontic professionals or residents. They were also not included if not available in full-text.

STUDY SELECTION AND DATA EXTRACTION

All identified articles were screened by two researchers (KS and PK) to consider whether or not they were eligible for this review. Any disagreement on the decision was resolved by discussion with the other researcher (PS). The table of data extraction was developed following an iterative testing in extracting information from the included articles, with the discussion among researchers based on the research questions and literature review. The data were extracted covering authors, year of publications, learning topics, types and concepts of simulations, research objectives, study design and data collection methods, as well as reported educational outcomes of the simulations (Table 1). The data from included articles were extracted by a researcher experienced with systematic reviews (KS). The data extraction was then reviewed by another researcher (PS) to confirm the validity. Disagreement was settled by discussion among researchers (KS, PK, and PS) to achieve a consensus.

Table 1:
Information extracted from the included articles.

RESULTS

LITERATURE IDENTIFIED FROM THE SEARCH

The search conducted across the four databases identified 170 articles. Google Scholar and the reference lists of identified articles were also screened, and seven papers were further identified. After that, 33 duplicates were removed and 144 titles and abstracts were reviewed. One hundred and eight articles were excluded, as they were reviews and/or not relevant to technology-enhanced simulations. Thirty-six full-texts were then assessed, and twenty articles were excluded: ten were technology-enhanced simulations used for only orthodontic treatment, rather than for training purposes; three were interventions that were not considered as technology-enhanced simulations; three were not related to orthodontics; two were reviews; one was a traditional simulation; and one was not available in full-text. The article selection process is presented in Figure 1.

Figure 1:
A flow diagram presenting the articles selection process for this review.

CHARACTERISTICS OF INCLUDED ARTICLES

The sixteen articles included in this scoping review comprised nine journal articles,2424 Rodrigues MAF, Silva WB, Barbosa Neto ME, Gillies DF, Ribeiro IMMP. An interactive simulation system for training and treatment planning in orthodontics. Comput Graph. 2007 Oct;31(5): 688-97.

25 Naser-ud-Din S. Introducing Scenario Based Learning interactive to postgraduates in UQ Orthodontic Program. Eur J Dent Educ. 2015 Aug;19(3):169-76.

26 Rao GKL, Srinivasa AC, Iskandar YHP, Mokhtar N. Identification and analysis of photometric points on 2D facial images: a machine learning approach in orthodontics. Health Technol. 2019 Mar;9(5):715-24.

27 Sakowitz SM, Inglehart MR, Ramaswamy V, Edwards S, Shoukri B, Sachs S, et al. A comparison of two-dimensional prediction tracing and a virtual reality patient methods for diagnosis and treatment planning of orthognathic cases in dental students: a randomized preliminary study. Virtual Real. 2020;24(3):399-409.

28 Gredes T, Pricop-Jeckstadt M, Mereti E, Botzenhart U. Survey of student attitudes toward digital technology in practical technical dental education using the AR-Demonstrator-App. J Dent Educ. 2022 Jan;86(1):12-20.

29 Ho ACH, Liao C, Lu J, Shan Z, Gu M, Bridges SM, et al. 3-Dimensional simulations and student learning in orthodontic education. Eur J Dent Educ. 2022 Aug;26(3):435-45.

30 Lo YC, Chen GA, Liu YC, Chen YH, Hsu JT, Yu JH. Prototype of augmented reality technology for orthodontic bracket positioning: an in vivo study. Appl Sci. 2021;11(5):2315.

31 Sytek L, Inglehart MR, Ramaswamy V, Aronovich S, Edwards S, Kim-Berman H. Comparisons of orthodontic residents' performance and attitudes using 2D, 3D, and virtual reality surgical simulation methods. J Dent Educ. 2021 Aug;85(8):1415-26.
-3232 Ye F, Liu L, Yan B, Zhao X, Hao A. Orthodontic simulation system with force feedback for training complete bracket placement procedures. Virtual Real Intell Hardw. 2021 Aug;3(4):261-73. six conference papers,3333 Rodrigues MAF, Silva WB, Barbosa RG, Ribeiro IMMP, Neto MEB. J-Ortho: an open-source orthodontic treatment simulator. Proceedings of the 2006 ACM Symposium on Applied Computing; Dijon, France. Dijon: Association for Computing Machinery; 2006. p. 245-9.

34 Rodrigues MAF, Rocha RS, Silva WB. Interactive and accurate collision detection in virtual orthodontics. Proceedings of the 14th Eurographics conference on Virtual Environments; Eindhoven, The Netherlands. Eindhoven: Eurographics Association; 2008. p. 65-72.

35 Sinthanayothin C, Tharanont W. Orthodontics treatment simulation by teeth segmentation and setup. Proceedings of 2008 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology; 14-17 May 2008; Krabi, Thailand. Krabi: IEEE; 2008. p. 81-4.

36 Yaqi M, Zhongke L. Computer aided orthodontics treatment by virtual segmentation and adjustment. Proceedings of 2010 International Conference on Image Analysis and Signal Processing; 9-11 April 2010; Zhejiang, China. Zhejiang: IEEE; 2010. p. 336-9.

37 Rao GKL, Mokhtar NB, Iskandar YHP, editors. An integration of augmented reality technology for orthodontic education: case of bracket positioning. Proceedings of 2017 IEEE Conference on e-Learning, e-Management and e-Services (IC3e); 16-17 Nov. 2017; Miri, Ma-laysia. Miri: IEEE; 2017. p. 7-11.
-3838 Rao GKL, Mokhtar N, Iskandar YHP, Channarayapatna Srinivasa A. Learning orthodontic cephalometry through augmented reality: a conceptual machine learning validation approach. Proceedings of 2018 International Conference on Electrical Engineering and Informat-ics (ICELTICs); Banda Aceh, Indonesia. Banda Aceh: IEEE; 2018. p. 133-8. and one PhD thesis.3939 Kumar Y. Automated virtual treatment planning in orthodontics: modeling and algo-rithms. Minnesota: University of Minnesota; 2012. Two articles were experimental designs comparing intervention and conventional approaches,2727 Sakowitz SM, Inglehart MR, Ramaswamy V, Edwards S, Shoukri B, Sachs S, et al. A comparison of two-dimensional prediction tracing and a virtual reality patient methods for diagnosis and treatment planning of orthognathic cases in dental students: a randomized preliminary study. Virtual Real. 2020;24(3):399-409.,3030 Lo YC, Chen GA, Liu YC, Chen YH, Hsu JT, Yu JH. Prototype of augmented reality technology for orthodontic bracket positioning: an in vivo study. Appl Sci. 2021;11(5):2315. and one of them was a randomized control trial.2727 Sakowitz SM, Inglehart MR, Ramaswamy V, Edwards S, Shoukri B, Sachs S, et al. A comparison of two-dimensional prediction tracing and a virtual reality patient methods for diagnosis and treatment planning of orthognathic cases in dental students: a randomized preliminary study. Virtual Real. 2020;24(3):399-409. Two studies used only a questionnaire survey design to gather user perception toward the use of technology-enhanced simulations.2525 Naser-ud-Din S. Introducing Scenario Based Learning interactive to postgraduates in UQ Orthodontic Program. Eur J Dent Educ. 2015 Aug;19(3):169-76.,2828 Gredes T, Pricop-Jeckstadt M, Mereti E, Botzenhart U. Survey of student attitudes toward digital technology in practical technical dental education using the AR-Demonstrator-App. J Dent Educ. 2022 Jan;86(1):12-20. Two articles reported the used of mixed methods design, where focus group discussion and semi-structured interview were used to collect qualitative data.2929 Ho ACH, Liao C, Lu J, Shan Z, Gu M, Bridges SM, et al. 3-Dimensional simulations and student learning in orthodontic education. Eur J Dent Educ. 2022 Aug;26(3):435-45.,3131 Sytek L, Inglehart MR, Ramaswamy V, Aronovich S, Edwards S, Kim-Berman H. Comparisons of orthodontic residents' performance and attitudes using 2D, 3D, and virtual reality surgical simulation methods. J Dent Educ. 2021 Aug;85(8):1415-26. Nine articles and one thesis demonstrated the development and validation process of computer-based simulations, with no report of data collection process.2424 Rodrigues MAF, Silva WB, Barbosa Neto ME, Gillies DF, Ribeiro IMMP. An interactive simulation system for training and treatment planning in orthodontics. Comput Graph. 2007 Oct;31(5): 688-97.,2626 Rao GKL, Srinivasa AC, Iskandar YHP, Mokhtar N. Identification and analysis of photometric points on 2D facial images: a machine learning approach in orthodontics. Health Technol. 2019 Mar;9(5):715-24.,3232 Ye F, Liu L, Yan B, Zhao X, Hao A. Orthodontic simulation system with force feedback for training complete bracket placement procedures. Virtual Real Intell Hardw. 2021 Aug;3(4):261-73.

33 Rodrigues MAF, Silva WB, Barbosa RG, Ribeiro IMMP, Neto MEB. J-Ortho: an open-source orthodontic treatment simulator. Proceedings of the 2006 ACM Symposium on Applied Computing; Dijon, France. Dijon: Association for Computing Machinery; 2006. p. 245-9.

34 Rodrigues MAF, Rocha RS, Silva WB. Interactive and accurate collision detection in virtual orthodontics. Proceedings of the 14th Eurographics conference on Virtual Environments; Eindhoven, The Netherlands. Eindhoven: Eurographics Association; 2008. p. 65-72.

35 Sinthanayothin C, Tharanont W. Orthodontics treatment simulation by teeth segmentation and setup. Proceedings of 2008 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology; 14-17 May 2008; Krabi, Thailand. Krabi: IEEE; 2008. p. 81-4.

36 Yaqi M, Zhongke L. Computer aided orthodontics treatment by virtual segmentation and adjustment. Proceedings of 2010 International Conference on Image Analysis and Signal Processing; 9-11 April 2010; Zhejiang, China. Zhejiang: IEEE; 2010. p. 336-9.

37 Rao GKL, Mokhtar NB, Iskandar YHP, editors. An integration of augmented reality technology for orthodontic education: case of bracket positioning. Proceedings of 2017 IEEE Conference on e-Learning, e-Management and e-Services (IC3e); 16-17 Nov. 2017; Miri, Ma-laysia. Miri: IEEE; 2017. p. 7-11.

38 Rao GKL, Mokhtar N, Iskandar YHP, Channarayapatna Srinivasa A. Learning orthodontic cephalometry through augmented reality: a conceptual machine learning validation approach. Proceedings of 2018 International Conference on Electrical Engineering and Informat-ics (ICELTICs); Banda Aceh, Indonesia. Banda Aceh: IEEE; 2018. p. 133-8.
-3939 Kumar Y. Automated virtual treatment planning in orthodontics: modeling and algo-rithms. Minnesota: University of Minnesota; 2012. When considering the year of publication, over the 15-year period (2006 to 2020), only 11 publications were found introducing simulations (9 approaches),2424 Rodrigues MAF, Silva WB, Barbosa Neto ME, Gillies DF, Ribeiro IMMP. An interactive simulation system for training and treatment planning in orthodontics. Comput Graph. 2007 Oct;31(5): 688-97.

25 Naser-ud-Din S. Introducing Scenario Based Learning interactive to postgraduates in UQ Orthodontic Program. Eur J Dent Educ. 2015 Aug;19(3):169-76.

26 Rao GKL, Srinivasa AC, Iskandar YHP, Mokhtar N. Identification and analysis of photometric points on 2D facial images: a machine learning approach in orthodontics. Health Technol. 2019 Mar;9(5):715-24.
-2727 Sakowitz SM, Inglehart MR, Ramaswamy V, Edwards S, Shoukri B, Sachs S, et al. A comparison of two-dimensional prediction tracing and a virtual reality patient methods for diagnosis and treatment planning of orthognathic cases in dental students: a randomized preliminary study. Virtual Real. 2020;24(3):399-409.,3333 Rodrigues MAF, Silva WB, Barbosa RG, Ribeiro IMMP, Neto MEB. J-Ortho: an open-source orthodontic treatment simulator. Proceedings of the 2006 ACM Symposium on Applied Computing; Dijon, France. Dijon: Association for Computing Machinery; 2006. p. 245-9.

34 Rodrigues MAF, Rocha RS, Silva WB. Interactive and accurate collision detection in virtual orthodontics. Proceedings of the 14th Eurographics conference on Virtual Environments; Eindhoven, The Netherlands. Eindhoven: Eurographics Association; 2008. p. 65-72.

35 Sinthanayothin C, Tharanont W. Orthodontics treatment simulation by teeth segmentation and setup. Proceedings of 2008 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology; 14-17 May 2008; Krabi, Thailand. Krabi: IEEE; 2008. p. 81-4.

36 Yaqi M, Zhongke L. Computer aided orthodontics treatment by virtual segmentation and adjustment. Proceedings of 2010 International Conference on Image Analysis and Signal Processing; 9-11 April 2010; Zhejiang, China. Zhejiang: IEEE; 2010. p. 336-9.

37 Rao GKL, Mokhtar NB, Iskandar YHP, editors. An integration of augmented reality technology for orthodontic education: case of bracket positioning. Proceedings of 2017 IEEE Conference on e-Learning, e-Management and e-Services (IC3e); 16-17 Nov. 2017; Miri, Ma-laysia. Miri: IEEE; 2017. p. 7-11.

38 Rao GKL, Mokhtar N, Iskandar YHP, Channarayapatna Srinivasa A. Learning orthodontic cephalometry through augmented reality: a conceptual machine learning validation approach. Proceedings of 2018 International Conference on Electrical Engineering and Informat-ics (ICELTICs); Banda Aceh, Indonesia. Banda Aceh: IEEE; 2018. p. 133-8.
-3939 Kumar Y. Automated virtual treatment planning in orthodontics: modeling and algo-rithms. Minnesota: University of Minnesota; 2012. which nearly a half of them were design to facilitate orthodontic practice as a main purpose, although not applicable for training. Moreover, five articles had already been made available within the 10-month period in 2021.2828 Gredes T, Pricop-Jeckstadt M, Mereti E, Botzenhart U. Survey of student attitudes toward digital technology in practical technical dental education using the AR-Demonstrator-App. J Dent Educ. 2022 Jan;86(1):12-20.

29 Ho ACH, Liao C, Lu J, Shan Z, Gu M, Bridges SM, et al. 3-Dimensional simulations and student learning in orthodontic education. Eur J Dent Educ. 2022 Aug;26(3):435-45.

30 Lo YC, Chen GA, Liu YC, Chen YH, Hsu JT, Yu JH. Prototype of augmented reality technology for orthodontic bracket positioning: an in vivo study. Appl Sci. 2021;11(5):2315.

31 Sytek L, Inglehart MR, Ramaswamy V, Aronovich S, Edwards S, Kim-Berman H. Comparisons of orthodontic residents' performance and attitudes using 2D, 3D, and virtual reality surgical simulation methods. J Dent Educ. 2021 Aug;85(8):1415-26.
-3232 Ye F, Liu L, Yan B, Zhao X, Hao A. Orthodontic simulation system with force feedback for training complete bracket placement procedures. Virtual Real Intell Hardw. 2021 Aug;3(4):261-73.

CHARACTERISTICS OF TECHNOLOGY-ENHANCED SIMULATIONS INCLUDED IN THIS REVIEW

Of those 16 identified articles, 14 technology-enhanced simulations were introduced. Five simulations were designed in a three-dimensional (3D) format, as reported in seven articles;2424 Rodrigues MAF, Silva WB, Barbosa Neto ME, Gillies DF, Ribeiro IMMP. An interactive simulation system for training and treatment planning in orthodontics. Comput Graph. 2007 Oct;31(5): 688-97.,2929 Ho ACH, Liao C, Lu J, Shan Z, Gu M, Bridges SM, et al. 3-Dimensional simulations and student learning in orthodontic education. Eur J Dent Educ. 2022 Aug;26(3):435-45.,3333 Rodrigues MAF, Silva WB, Barbosa RG, Ribeiro IMMP, Neto MEB. J-Ortho: an open-source orthodontic treatment simulator. Proceedings of the 2006 ACM Symposium on Applied Computing; Dijon, France. Dijon: Association for Computing Machinery; 2006. p. 245-9.

34 Rodrigues MAF, Rocha RS, Silva WB. Interactive and accurate collision detection in virtual orthodontics. Proceedings of the 14th Eurographics conference on Virtual Environments; Eindhoven, The Netherlands. Eindhoven: Eurographics Association; 2008. p. 65-72.

35 Sinthanayothin C, Tharanont W. Orthodontics treatment simulation by teeth segmentation and setup. Proceedings of 2008 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology; 14-17 May 2008; Krabi, Thailand. Krabi: IEEE; 2008. p. 81-4.
-3636 Yaqi M, Zhongke L. Computer aided orthodontics treatment by virtual segmentation and adjustment. Proceedings of 2010 International Conference on Image Analysis and Signal Processing; 9-11 April 2010; Zhejiang, China. Zhejiang: IEEE; 2010. p. 336-9.,3939 Kumar Y. Automated virtual treatment planning in orthodontics: modeling and algo-rithms. Minnesota: University of Minnesota; 2012. four simulations adopted an AR technology;2828 Gredes T, Pricop-Jeckstadt M, Mereti E, Botzenhart U. Survey of student attitudes toward digital technology in practical technical dental education using the AR-Demonstrator-App. J Dent Educ. 2022 Jan;86(1):12-20.,3030 Lo YC, Chen GA, Liu YC, Chen YH, Hsu JT, Yu JH. Prototype of augmented reality technology for orthodontic bracket positioning: an in vivo study. Appl Sci. 2021;11(5):2315.,3737 Rao GKL, Mokhtar NB, Iskandar YHP, editors. An integration of augmented reality technology for orthodontic education: case of bracket positioning. Proceedings of 2017 IEEE Conference on e-Learning, e-Management and e-Services (IC3e); 16-17 Nov. 2017; Miri, Ma-laysia. Miri: IEEE; 2017. p. 7-11.,3838 Rao GKL, Mokhtar N, Iskandar YHP, Channarayapatna Srinivasa A. Learning orthodontic cephalometry through augmented reality: a conceptual machine learning validation approach. Proceedings of 2018 International Conference on Electrical Engineering and Informat-ics (ICELTICs); Banda Aceh, Indonesia. Banda Aceh: IEEE; 2018. p. 133-8. two employed a VR technology to support immersive learning;2727 Sakowitz SM, Inglehart MR, Ramaswamy V, Edwards S, Shoukri B, Sachs S, et al. A comparison of two-dimensional prediction tracing and a virtual reality patient methods for diagnosis and treatment planning of orthognathic cases in dental students: a randomized preliminary study. Virtual Real. 2020;24(3):399-409.,3131 Sytek L, Inglehart MR, Ramaswamy V, Aronovich S, Edwards S, Kim-Berman H. Comparisons of orthodontic residents' performance and attitudes using 2D, 3D, and virtual reality surgical simulation methods. J Dent Educ. 2021 Aug;85(8):1415-26. one was scenario-based simulation;2525 Naser-ud-Din S. Introducing Scenario Based Learning interactive to postgraduates in UQ Orthodontic Program. Eur J Dent Educ. 2015 Aug;19(3):169-76. one was automation of facial landmark identification;2626 Rao GKL, Srinivasa AC, Iskandar YHP, Mokhtar N. Identification and analysis of photometric points on 2D facial images: a machine learning approach in orthodontics. Health Technol. 2019 Mar;9(5):715-24. and one reported the application of haptic technology.3232 Ye F, Liu L, Yan B, Zhao X, Hao A. Orthodontic simulation system with force feedback for training complete bracket placement procedures. Virtual Real Intell Hardw. 2021 Aug;3(4):261-73. The identified computer-based simulations were implemented in varied topics in orthodontic education, covering diagnosis and treatment planning,2424 Rodrigues MAF, Silva WB, Barbosa Neto ME, Gillies DF, Ribeiro IMMP. An interactive simulation system for training and treatment planning in orthodontics. Comput Graph. 2007 Oct;31(5): 688-97.,2727 Sakowitz SM, Inglehart MR, Ramaswamy V, Edwards S, Shoukri B, Sachs S, et al. A comparison of two-dimensional prediction tracing and a virtual reality patient methods for diagnosis and treatment planning of orthognathic cases in dental students: a randomized preliminary study. Virtual Real. 2020;24(3):399-409.,2929 Ho ACH, Liao C, Lu J, Shan Z, Gu M, Bridges SM, et al. 3-Dimensional simulations and student learning in orthodontic education. Eur J Dent Educ. 2022 Aug;26(3):435-45.,3131 Sytek L, Inglehart MR, Ramaswamy V, Aronovich S, Edwards S, Kim-Berman H. Comparisons of orthodontic residents' performance and attitudes using 2D, 3D, and virtual reality surgical simulation methods. J Dent Educ. 2021 Aug;85(8):1415-26.,3333 Rodrigues MAF, Silva WB, Barbosa RG, Ribeiro IMMP, Neto MEB. J-Ortho: an open-source orthodontic treatment simulator. Proceedings of the 2006 ACM Symposium on Applied Computing; Dijon, France. Dijon: Association for Computing Machinery; 2006. p. 245-9.

34 Rodrigues MAF, Rocha RS, Silva WB. Interactive and accurate collision detection in virtual orthodontics. Proceedings of the 14th Eurographics conference on Virtual Environments; Eindhoven, The Netherlands. Eindhoven: Eurographics Association; 2008. p. 65-72.

35 Sinthanayothin C, Tharanont W. Orthodontics treatment simulation by teeth segmentation and setup. Proceedings of 2008 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology; 14-17 May 2008; Krabi, Thailand. Krabi: IEEE; 2008. p. 81-4.
-3636 Yaqi M, Zhongke L. Computer aided orthodontics treatment by virtual segmentation and adjustment. Proceedings of 2010 International Conference on Image Analysis and Signal Processing; 9-11 April 2010; Zhejiang, China. Zhejiang: IEEE; 2010. p. 336-9.,3939 Kumar Y. Automated virtual treatment planning in orthodontics: modeling and algo-rithms. Minnesota: University of Minnesota; 2012. orthodontic bracket positioning,3030 Lo YC, Chen GA, Liu YC, Chen YH, Hsu JT, Yu JH. Prototype of augmented reality technology for orthodontic bracket positioning: an in vivo study. Appl Sci. 2021;11(5):2315.,3232 Ye F, Liu L, Yan B, Zhao X, Hao A. Orthodontic simulation system with force feedback for training complete bracket placement procedures. Virtual Real Intell Hardw. 2021 Aug;3(4):261-73.,3737 Rao GKL, Mokhtar NB, Iskandar YHP, editors. An integration of augmented reality technology for orthodontic education: case of bracket positioning. Proceedings of 2017 IEEE Conference on e-Learning, e-Management and e-Services (IC3e); 16-17 Nov. 2017; Miri, Ma-laysia. Miri: IEEE; 2017. p. 7-11. orthodontic cases and procedures in orthodontic practice,2525 Naser-ud-Din S. Introducing Scenario Based Learning interactive to postgraduates in UQ Orthodontic Program. Eur J Dent Educ. 2015 Aug;19(3):169-76. facial landmark,2626 Rao GKL, Srinivasa AC, Iskandar YHP, Mokhtar N. Identification and analysis of photometric points on 2D facial images: a machine learning approach in orthodontics. Health Technol. 2019 Mar;9(5):715-24. removable orthodontic appliance,2828 Gredes T, Pricop-Jeckstadt M, Mereti E, Botzenhart U. Survey of student attitudes toward digital technology in practical technical dental education using the AR-Demonstrator-App. J Dent Educ. 2022 Jan;86(1):12-20. and cephalometric tracing.3838 Rao GKL, Mokhtar N, Iskandar YHP, Channarayapatna Srinivasa A. Learning orthodontic cephalometry through augmented reality: a conceptual machine learning validation approach. Proceedings of 2018 International Conference on Electrical Engineering and Informat-ics (ICELTICs); Banda Aceh, Indonesia. Banda Aceh: IEEE; 2018. p. 133-8. Four simulations, reported in six articles, were designed to facilitate orthodontic treatment procedures as a main purpose;2424 Rodrigues MAF, Silva WB, Barbosa Neto ME, Gillies DF, Ribeiro IMMP. An interactive simulation system for training and treatment planning in orthodontics. Comput Graph. 2007 Oct;31(5): 688-97.,3333 Rodrigues MAF, Silva WB, Barbosa RG, Ribeiro IMMP, Neto MEB. J-Ortho: an open-source orthodontic treatment simulator. Proceedings of the 2006 ACM Symposium on Applied Computing; Dijon, France. Dijon: Association for Computing Machinery; 2006. p. 245-9.

34 Rodrigues MAF, Rocha RS, Silva WB. Interactive and accurate collision detection in virtual orthodontics. Proceedings of the 14th Eurographics conference on Virtual Environments; Eindhoven, The Netherlands. Eindhoven: Eurographics Association; 2008. p. 65-72.

35 Sinthanayothin C, Tharanont W. Orthodontics treatment simulation by teeth segmentation and setup. Proceedings of 2008 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology; 14-17 May 2008; Krabi, Thailand. Krabi: IEEE; 2008. p. 81-4.
-3636 Yaqi M, Zhongke L. Computer aided orthodontics treatment by virtual segmentation and adjustment. Proceedings of 2010 International Conference on Image Analysis and Signal Processing; 9-11 April 2010; Zhejiang, China. Zhejiang: IEEE; 2010. p. 336-9.,3939 Kumar Y. Automated virtual treatment planning in orthodontics: modeling and algo-rithms. Minnesota: University of Minnesota; 2012. however, they could be applied for training dental students or residents in orthodontic education.

EDUCATIONAL OUTCOMES OF TECHNOLOGY-ENHANCED SIMULATIONS INCLUDED IN THIS REVIEW

Two experimental studies demonstrated cognitive improvement of participants in orthodontic diagnostics and treatment planning after interacting with the simulations.2727 Sakowitz SM, Inglehart MR, Ramaswamy V, Edwards S, Shoukri B, Sachs S, et al. A comparison of two-dimensional prediction tracing and a virtual reality patient methods for diagnosis and treatment planning of orthognathic cases in dental students: a randomized preliminary study. Virtual Real. 2020;24(3):399-409.,3030 Lo YC, Chen GA, Liu YC, Chen YH, Hsu JT, Yu JH. Prototype of augmented reality technology for orthodontic bracket positioning: an in vivo study. Appl Sci. 2021;11(5):2315. In addition, orthodontic residents and orthodontists tended to have positive perceptions toward the use of simulations.2525 Naser-ud-Din S. Introducing Scenario Based Learning interactive to postgraduates in UQ Orthodontic Program. Eur J Dent Educ. 2015 Aug;19(3):169-76.,2828 Gredes T, Pricop-Jeckstadt M, Mereti E, Botzenhart U. Survey of student attitudes toward digital technology in practical technical dental education using the AR-Demonstrator-App. J Dent Educ. 2022 Jan;86(1):12-20.,2929 Ho ACH, Liao C, Lu J, Shan Z, Gu M, Bridges SM, et al. 3-Dimensional simulations and student learning in orthodontic education. Eur J Dent Educ. 2022 Aug;26(3):435-45.,3131 Sytek L, Inglehart MR, Ramaswamy V, Aronovich S, Edwards S, Kim-Berman H. Comparisons of orthodontic residents' performance and attitudes using 2D, 3D, and virtual reality surgical simulation methods. J Dent Educ. 2021 Aug;85(8):1415-26. They believed that they could gain confidence in orthodontic treatment procedures with the simulation.2525 Naser-ud-Din S. Introducing Scenario Based Learning interactive to postgraduates in UQ Orthodontic Program. Eur J Dent Educ. 2015 Aug;19(3):169-76. Participants were also likely to report high acceptance of the simulation in improving diagnostic competence.2929 Ho ACH, Liao C, Lu J, Shan Z, Gu M, Bridges SM, et al. 3-Dimensional simulations and student learning in orthodontic education. Eur J Dent Educ. 2022 Aug;26(3):435-45. There were eight simulations, reported in ten publications, which were designed for the improvement of cognitive domain in orthodontic practice.2424 Rodrigues MAF, Silva WB, Barbosa Neto ME, Gillies DF, Ribeiro IMMP. An interactive simulation system for training and treatment planning in orthodontics. Comput Graph. 2007 Oct;31(5): 688-97.,2626 Rao GKL, Srinivasa AC, Iskandar YHP, Mokhtar N. Identification and analysis of photometric points on 2D facial images: a machine learning approach in orthodontics. Health Technol. 2019 Mar;9(5):715-24.,3232 Ye F, Liu L, Yan B, Zhao X, Hao A. Orthodontic simulation system with force feedback for training complete bracket placement procedures. Virtual Real Intell Hardw. 2021 Aug;3(4):261-73.

33 Rodrigues MAF, Silva WB, Barbosa RG, Ribeiro IMMP, Neto MEB. J-Ortho: an open-source orthodontic treatment simulator. Proceedings of the 2006 ACM Symposium on Applied Computing; Dijon, France. Dijon: Association for Computing Machinery; 2006. p. 245-9.

34 Rodrigues MAF, Rocha RS, Silva WB. Interactive and accurate collision detection in virtual orthodontics. Proceedings of the 14th Eurographics conference on Virtual Environments; Eindhoven, The Netherlands. Eindhoven: Eurographics Association; 2008. p. 65-72.

35 Sinthanayothin C, Tharanont W. Orthodontics treatment simulation by teeth segmentation and setup. Proceedings of 2008 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology; 14-17 May 2008; Krabi, Thailand. Krabi: IEEE; 2008. p. 81-4.

36 Yaqi M, Zhongke L. Computer aided orthodontics treatment by virtual segmentation and adjustment. Proceedings of 2010 International Conference on Image Analysis and Signal Processing; 9-11 April 2010; Zhejiang, China. Zhejiang: IEEE; 2010. p. 336-9.

37 Rao GKL, Mokhtar NB, Iskandar YHP, editors. An integration of augmented reality technology for orthodontic education: case of bracket positioning. Proceedings of 2017 IEEE Conference on e-Learning, e-Management and e-Services (IC3e); 16-17 Nov. 2017; Miri, Ma-laysia. Miri: IEEE; 2017. p. 7-11.

38 Rao GKL, Mokhtar N, Iskandar YHP, Channarayapatna Srinivasa A. Learning orthodontic cephalometry through augmented reality: a conceptual machine learning validation approach. Proceedings of 2018 International Conference on Electrical Engineering and Informat-ics (ICELTICs); Banda Aceh, Indonesia. Banda Aceh: IEEE; 2018. p. 133-8.
-3939 Kumar Y. Automated virtual treatment planning in orthodontics: modeling and algo-rithms. Minnesota: University of Minnesota; 2012. However, no evidence of learning outcome evaluations has been provided in these articles.

Three articles reported technology-enhanced simulations designed for the enhancement of psychomotor skills in orthodontic practice. One article reported the implementation of haptic technologies into an orthodontic simulation, in which users were allowed to perform the required steps of orthodontic bracket placement on a virtual patient.3232 Ye F, Liu L, Yan B, Zhao X, Hao A. Orthodontic simulation system with force feedback for training complete bracket placement procedures. Virtual Real Intell Hardw. 2021 Aug;3(4):261-73. However, the article had not yet demonstrated the evidence of its educational impact on the improvement of learner competence. Two articles reported the application of AR for training orthodontic bracket placement.3030 Lo YC, Chen GA, Liu YC, Chen YH, Hsu JT, Yu JH. Prototype of augmented reality technology for orthodontic bracket positioning: an in vivo study. Appl Sci. 2021;11(5):2315.,3737 Rao GKL, Mokhtar NB, Iskandar YHP, editors. An integration of augmented reality technology for orthodontic education: case of bracket positioning. Proceedings of 2017 IEEE Conference on e-Learning, e-Management and e-Services (IC3e); 16-17 Nov. 2017; Miri, Ma-laysia. Miri: IEEE; 2017. p. 7-11. However, only one study performed a data collection process and presented the enhancement in the accuracy of bracket placement with the intervention, when compared with the conventional approach.3030 Lo YC, Chen GA, Liu YC, Chen YH, Hsu JT, Yu JH. Prototype of augmented reality technology for orthodontic bracket positioning: an in vivo study. Appl Sci. 2021;11(5):2315. These articles supported the implementation of simulations in improving psychomotor skills in orthodontic education.

Overall, the included articles tended to support the use of technology-enhanced simulations in teaching and training orthodontic residents or orthodontists. When considering the 16 included publications according to the Kirkpatrick model,4040 Kirkpatrick D, Kirkpatrick J. Transferring learning to behavior: using the four levels to improve performance. San Francisco: Berrett-Koehler Publishers; 2005. two experiments reported positive learning outcomes from the evaluation at a learning level.2727 Sakowitz SM, Inglehart MR, Ramaswamy V, Edwards S, Shoukri B, Sachs S, et al. A comparison of two-dimensional prediction tracing and a virtual reality patient methods for diagnosis and treatment planning of orthognathic cases in dental students: a randomized preliminary study. Virtual Real. 2020;24(3):399-409.,3030 Lo YC, Chen GA, Liu YC, Chen YH, Hsu JT, Yu JH. Prototype of augmented reality technology for orthodontic bracket positioning: an in vivo study. Appl Sci. 2021;11(5):2315. Four studies positively demonstrated the outcome evaluation focusing on a reaction level.2525 Naser-ud-Din S. Introducing Scenario Based Learning interactive to postgraduates in UQ Orthodontic Program. Eur J Dent Educ. 2015 Aug;19(3):169-76.,2828 Gredes T, Pricop-Jeckstadt M, Mereti E, Botzenhart U. Survey of student attitudes toward digital technology in practical technical dental education using the AR-Demonstrator-App. J Dent Educ. 2022 Jan;86(1):12-20.,2929 Ho ACH, Liao C, Lu J, Shan Z, Gu M, Bridges SM, et al. 3-Dimensional simulations and student learning in orthodontic education. Eur J Dent Educ. 2022 Aug;26(3):435-45.,3131 Sytek L, Inglehart MR, Ramaswamy V, Aronovich S, Edwards S, Kim-Berman H. Comparisons of orthodontic residents' performance and attitudes using 2D, 3D, and virtual reality surgical simulation methods. J Dent Educ. 2021 Aug;85(8):1415-26. Ten articles were published without reporting the outcome evaluation in terms of orthodontic education.2424 Rodrigues MAF, Silva WB, Barbosa Neto ME, Gillies DF, Ribeiro IMMP. An interactive simulation system for training and treatment planning in orthodontics. Comput Graph. 2007 Oct;31(5): 688-97.,2626 Rao GKL, Srinivasa AC, Iskandar YHP, Mokhtar N. Identification and analysis of photometric points on 2D facial images: a machine learning approach in orthodontics. Health Technol. 2019 Mar;9(5):715-24.,3232 Ye F, Liu L, Yan B, Zhao X, Hao A. Orthodontic simulation system with force feedback for training complete bracket placement procedures. Virtual Real Intell Hardw. 2021 Aug;3(4):261-73.

33 Rodrigues MAF, Silva WB, Barbosa RG, Ribeiro IMMP, Neto MEB. J-Ortho: an open-source orthodontic treatment simulator. Proceedings of the 2006 ACM Symposium on Applied Computing; Dijon, France. Dijon: Association for Computing Machinery; 2006. p. 245-9.

34 Rodrigues MAF, Rocha RS, Silva WB. Interactive and accurate collision detection in virtual orthodontics. Proceedings of the 14th Eurographics conference on Virtual Environments; Eindhoven, The Netherlands. Eindhoven: Eurographics Association; 2008. p. 65-72.

35 Sinthanayothin C, Tharanont W. Orthodontics treatment simulation by teeth segmentation and setup. Proceedings of 2008 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology; 14-17 May 2008; Krabi, Thailand. Krabi: IEEE; 2008. p. 81-4.

36 Yaqi M, Zhongke L. Computer aided orthodontics treatment by virtual segmentation and adjustment. Proceedings of 2010 International Conference on Image Analysis and Signal Processing; 9-11 April 2010; Zhejiang, China. Zhejiang: IEEE; 2010. p. 336-9.

37 Rao GKL, Mokhtar NB, Iskandar YHP, editors. An integration of augmented reality technology for orthodontic education: case of bracket positioning. Proceedings of 2017 IEEE Conference on e-Learning, e-Management and e-Services (IC3e); 16-17 Nov. 2017; Miri, Ma-laysia. Miri: IEEE; 2017. p. 7-11.

38 Rao GKL, Mokhtar N, Iskandar YHP, Channarayapatna Srinivasa A. Learning orthodontic cephalometry through augmented reality: a conceptual machine learning validation approach. Proceedings of 2018 International Conference on Electrical Engineering and Informat-ics (ICELTICs); Banda Aceh, Indonesia. Banda Aceh: IEEE; 2018. p. 133-8.
-3939 Kumar Y. Automated virtual treatment planning in orthodontics: modeling and algo-rithms. Minnesota: University of Minnesota; 2012. None of the articles performed the evaluation of learning outcomes at behaviour and results levels.

DISCUSSION

This scoping review was conducted to comprehensively analyze evidence of the use of technology-enhanced simulation-based learning in orthodontic practice. An increase in publications of technology-enhanced simulations in orthodontic education was found. In addition, the recent publications were more likely to report evidence supporting positive impact of technology-enhanced simulations as learning or training tools in orthodontic education. This rising trend was also found in the use of serious games in dental education,44 Sipiyaruk K, Hatzipanagos S, Reynolds PA, Gallagher JE. Serious games and the COVID-19 pandemic in dental education: an integrative review of the literature. Computers. 2021;10(4):42. which could be resulting from the advancement of 3D modeling and computer graphic technologies. The findings retrieved from included articles demonstrated positive impact of technology-enhanced simulations in orthodontic education in terms of both cognitive and psychomotor skills. These results were consistent with the impact of digital simulations, including VR and AR, in other areas of dental education.1515 Higgins D, Hayes M, Taylor J, Wallace J. A scoping review of simulation-based dental education. MedEdPublish. 2020; 9:36.,4141 Huang TK, Yang CH, Hsieh YH, Wang JC, Hung CC. Augmented reality (AR) and virtual reality (VR) applied in dentistry. Kaohsiung J Med Sci. 2018 Apr;34(4):243-8. Technology-enhanced simulations can be considered as very supportive in dentistry, including orthodontics, in which the integration of knowledge and hand skills is required for most of the dental treatments. Consequently, although limited, the existing evidence suggests the design and implementation of technology-enhanced simulations in orthodontic education.

Several key strengths of technology-enhanced simulations should be considered. Firstly, they allow users to perform required tasks repetitively until the expected outcomes are achieved.4242 Vincent M, Joseph D, Amory C, Paoli N, Ambrosini P, Mortier É, et al. Contribution of haptic simulation to analogic training environment in restorative dentistry. J Dent Educ. 2020 Mar;84(3):367-76.,4343 Bukhary DM, Alshali RZ. A simulation model used in teaching denture border adjustment: Randomized controlled trial. J Dent Educ. 2022 Jan;86(1):98-106. In addition, with immediate feedback, those simulations can also support users to conduct self-directed learning, and therefore the needs of one-to-one support from dental instructors can be reduced.4444 Zafar S, Lai Y, Sexton C, Siddiqi A. Virtual reality as a novel educational tool in pre-clinical paediatric dentistry training: students' perceptions. Int J Paediatr Dent. 2020 Nov;30(6):791-7. The concept of task repetition in improving learning competencies can be explained by the ‘role of failure’.4545 Gee JP. Learning and games. In: Salen K, editor. The ecology of games: connecting youth, games, and learning. Cambridge: MIT Press; 2008. p. 21-40. Although this model is a game-based theory, it could well explain the learning process within the simulations. Students are required to rethink or reperform their tasks based on feedback received from the failure, which will lead to the improvement of knowledge and skills. Simulators and VR can simulate learning situations, where learners can improve their knowledge and skills in safe environment.4646 Towers A, Field J, Stokes C, Maddock S, Martin N. A scoping review of the use and application of virtual reality in pre-clinical dental education. Br Dent J. 2019 Mar;226(5):358-66.,4747 Li Y, Ye H, Ye F, Liu Y, Lv L, Zhang P, et al. The current situation and future prospects of simulators in dental education. J Med Internet Res. 2021 Apr;23(4):e23635. This could reduce a risk of treatment, leading to the enhancement of patient safety in orthodontic practice.

The COVID-19 pandemic promoted negative impact on orthodontic training. With a decrease in the number of dental patients and treatment visits due to either fear and anxiety of COVID19 infection or financial problems,1010 Yavan MA. Effects of the COVID-19 pandemic on new patient visits for orthodontic treatment: a comparison of 2020 and the previous 3 years. J World Fed Orthod. 2021 Sep;10(3):127-31.,1212 Umeh OD, Utomi IL, Isiekwe IG, Aladenika ET. Impact of the coronavirus disease 2019 pandemic on orthodontic patients and their attitude to orthodontic treatment. Am J Orthod Dentofacial Orthop. 2021 May;159(5):e399-409. orthodontic residents may not be able to gain sufficient experience of clinical training. Academic staff and educators are required to consider appropriate replacements for orthodontic training in clinical settings. In addition to serious gaming,44 Sipiyaruk K, Hatzipanagos S, Reynolds PA, Gallagher JE. Serious games and the COVID-19 pandemic in dental education: an integrative review of the literature. Computers. 2021;10(4):42.,4848 Sipiyaruk K, Gallagher JE, Hatzipanagos S, Reynolds PA. A rapid review of serious games: From healthcare education to dental education. Eur J Dent Educ. 2018 Nov;22(4):243-57. technology-enhanced simulations should be considered to support residents in improving their orthodontic competence, as suggested according to the findings of this review. They could improve their knowledge and skills through repetitive tasks of learning activities within the simulations, which could prepare them for orthodontic training in clinical settings leading to the enhancement of patient safety. In addition, any orthodontic skills that may be insufficient from clinical training can be fulfilled with high-fidelity computer-based simulations.

When considering the Kirkpatrick model,4040 Kirkpatrick D, Kirkpatrick J. Transferring learning to behavior: using the four levels to improve performance. San Francisco: Berrett-Koehler Publishers; 2005. the common types of the outcome evaluation of the articles included in this scoping review appeared to be similar to research in other areas of dental education, which were reaction and learning levels.1515 Higgins D, Hayes M, Taylor J, Wallace J. A scoping review of simulation-based dental education. MedEdPublish. 2020; 9:36.,4141 Huang TK, Yang CH, Hsieh YH, Wang JC, Hung CC. Augmented reality (AR) and virtual reality (VR) applied in dentistry. Kaohsiung J Med Sci. 2018 Apr;34(4):243-8. There seemed to be no research evaluating the outcome at behaviour and results levels that could be considered as significant impact of the simulation development. In addition, while Bloom’s affective domain should be required for orthodontic practice, none of the included articles discussed the enhancement of this competence, although simulation-based pedagogical approaches can be considered as effective in improving these skills.4949 Lee J, Kim H, Kim KH, Jung D, Jowsey T, Webster CS. Effective virtual patient simula-tors for medical communication training: a systematic review. Med Educ. 2020 Sep;54(9):786-95. Consequently, further design and development of high-fidelity computer-based simulations are necessary to simulate actual patient tasks, as well as research with robust design (e.g. well-blinded randomized controlled trials) to confirm these outcome evaluation in orthodontic education.

A few limitations were identified when conducting this scoping review. While the simulations designed for orthodontic treatment as a primary purpose could be adapted for training residents,2424 Rodrigues MAF, Silva WB, Barbosa Neto ME, Gillies DF, Ribeiro IMMP. An interactive simulation system for training and treatment planning in orthodontics. Comput Graph. 2007 Oct;31(5): 688-97.,3333 Rodrigues MAF, Silva WB, Barbosa RG, Ribeiro IMMP, Neto MEB. J-Ortho: an open-source orthodontic treatment simulator. Proceedings of the 2006 ACM Symposium on Applied Computing; Dijon, France. Dijon: Association for Computing Machinery; 2006. p. 245-9.

34 Rodrigues MAF, Rocha RS, Silva WB. Interactive and accurate collision detection in virtual orthodontics. Proceedings of the 14th Eurographics conference on Virtual Environments; Eindhoven, The Netherlands. Eindhoven: Eurographics Association; 2008. p. 65-72.

35 Sinthanayothin C, Tharanont W. Orthodontics treatment simulation by teeth segmentation and setup. Proceedings of 2008 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology; 14-17 May 2008; Krabi, Thailand. Krabi: IEEE; 2008. p. 81-4.
-3636 Yaqi M, Zhongke L. Computer aided orthodontics treatment by virtual segmentation and adjustment. Proceedings of 2010 International Conference on Image Analysis and Signal Processing; 9-11 April 2010; Zhejiang, China. Zhejiang: IEEE; 2010. p. 336-9.,3939 Kumar Y. Automated virtual treatment planning in orthodontics: modeling and algo-rithms. Minnesota: University of Minnesota; 2012. the evidence of their outcome evaluation was not reported in these articles. Therefore, this scoping review cannot summarize the effectiveness of the implementation of orthodontic treatment simulations for teaching and training purposes. As this review had an emphasis on the outcome evaluation in orthodontic education, it was necessary to understand how to design training activities with simulations for training orthodontists or residents. For instance, they should be evaluated whether to be used as a bridge between classroom settings and clinical practice or a supplementary to both of them. Therefore, further original articles should be conducted to compare the effectiveness of the orthodontic treatment simulations adapted for training and the ones designed specifically for educational purposes, as well as to summarize how they should be implemented for the higher effectiveness in orthodontic practice. Furthermore, non-English search terms and other databases should be considered to further identify publications in other languages.

CONCLUSION

Limited evidence identified in this scoping review has been generated to demonstrate the effectiveness of technology-enhanced simulations in orthodontic practice, although some studies reported no significant difference of the outcome evaluation, when comparing with traditional approaches. In addition, the outcome evaluations of technology-enhanced simulations in orthodontic practice had not yet been reported in a number of included articles. Consequently, further research should be required to confirm positive educational impact of technology-enhanced simulations on orthodontic education.

REFERENCES

  • 1
    Afify AR, Zawawi KH, Othman HI, Al-Dharrab AA. Correlation of psychomotor skills and didactic performance among dental students in Saudi Arabia. Adv Med Educ Pract. 2013 Oct;4:223-6.
  • 2
    Perry S, Bridges SM, Burrow MF. A review of the use of simulation in dental education. Simul Healthc. 2015 Feb;10(1):31-7.
  • 3
    Joda T, Gallucci GO, Wismeijer D, Zitzmann NU. Augmented and virtual reality in dental medicine: a systematic review. Comput Biol Med. 2019 May;108:93-100.
  • 4
    Sipiyaruk K, Hatzipanagos S, Reynolds PA, Gallagher JE. Serious games and the COVID-19 pandemic in dental education: an integrative review of the literature. Computers. 2021;10(4):42.
  • 5
    Graber LW, Vanarsdall RL, Vig KWL, Huang GJ. Orthodontics: current principles and techniques. 6th ed. St. Louis: Elsevier; 2016.
  • 6
    Proffit WR, Fields HW, Larson B, Sarver DM. Contemporary orthodontics. 6th ed. St. Louis: Elsevier; 2018.
  • 7
    Athanasiou AE, Darendeliler MA, Eliades T, Hägg U, Larson BE, Pirttiniemi P, et al. World Federation of Orthodontists (WFO) guidelines for postgraduate orthodontic education. World J Orthod. 2009;10(2):153-66.
  • 8
    Morosan H. Orthodontic treatment in times of Covid-19. J Med Life. 2021;14(2):205-9.
  • 9
    Suri S, Vandersluis YR, Kochhar AS, Bhasin R, Abdallah MN. Clinical orthodontic management during the COVID-19 pandemic. Angle Orthod. 2020 Jul;90(4):473-84.
  • 10
    Yavan MA. Effects of the COVID-19 pandemic on new patient visits for orthodontic treatment: a comparison of 2020 and the previous 3 years. J World Fed Orthod. 2021 Sep;10(3):127-31.
  • 11
    Cotrin P, Peloso RM, Oliveira RC, Oliveira RCG, Pini NIP, Valarelli FP, et al. Impact of coronavirus pandemic in appointments and anxiety/concerns of patients regarding orthodontic treatment. Orthod Craniofac Res. 2020 Nov;23(4):455-61.
  • 12
    Umeh OD, Utomi IL, Isiekwe IG, Aladenika ET. Impact of the coronavirus disease 2019 pandemic on orthodontic patients and their attitude to orthodontic treatment. Am J Orthod Dentofacial Orthop. 2021 May;159(5):e399-409.
  • 13
    Quinn B, Field J, Gorter R, Akota I, Manzanares MC, Paganelli C, et al. COVID-19: the immediate response of European academic dental institutions and future implications for dental education. Eur J Dent Educ. 2020 Nov;24(4):811-4.
  • 14
    Artese F. Covid 19 pandemic unveiling the opportunities and challenges in orthodontic training. Dental Press J Orthod. 2020;25(3):7-8.
  • 15
    Higgins D, Hayes M, Taylor J, Wallace J. A scoping review of simulation-based dental education. MedEdPublish. 2020; 9:36.
  • 16
    Perry S, Bridges SM, Burrow MF. A conceptual model for clinical psychomotor skill development in an era of simulated and virtual reality. Eur J Dent Educ. 2022 May;26(2):263-76.
  • 17
    Moussa R, Alghazaly A, Althagafi N, Eshky R, Borzangy S. Effectiveness of virtual reality and interactive simulators on dental education outcomes: systematic review. Eur J Dent. 2022 Feb;16(1):14-31.
  • 18
    Munn Z, Peters MDJ, Stern C, Tufanaru C, McArthur A, Aromataris E. Systematic re-view or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Med Res Methodol. 2018 Nov;18(1):143.
  • 19
    Sucharew H, Macaluso M. Progress notes: methods for research evidence synthesis: the scoping review approach. J Hosp Med. 2019 Jul;14(7):416-8.
  • 20
    Levac D, Colquhoun H, O'Brien KK. Scoping studies: advancing the methodology. Implement Sci. 2010 Sep;5:69.
  • 21
    Tricco AC, Lillie E, Zarin W, O'Brien KK, Colquhoun H, Levac D, et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. 2018 Oct;169(7):467-73.
  • 22
    Methley AM, Campbell S, Chew-Graham C, McNally R, Cheraghi-Sohi S. PICO, PICOS and SPIDER: a comparison study of specificity and sensitivity in three search tools for qualitative systematic reviews. BMC Health Serv Res. 2014 Nov;14:579.
  • 23
    Snyder H. Literature review as a research methodology: An overview and guidelines. J Bus Res. 2019 Nov;104: 333-9.
  • 24
    Rodrigues MAF, Silva WB, Barbosa Neto ME, Gillies DF, Ribeiro IMMP. An interactive simulation system for training and treatment planning in orthodontics. Comput Graph. 2007 Oct;31(5): 688-97.
  • 25
    Naser-ud-Din S. Introducing Scenario Based Learning interactive to postgraduates in UQ Orthodontic Program. Eur J Dent Educ. 2015 Aug;19(3):169-76.
  • 26
    Rao GKL, Srinivasa AC, Iskandar YHP, Mokhtar N. Identification and analysis of photometric points on 2D facial images: a machine learning approach in orthodontics. Health Technol. 2019 Mar;9(5):715-24.
  • 27
    Sakowitz SM, Inglehart MR, Ramaswamy V, Edwards S, Shoukri B, Sachs S, et al. A comparison of two-dimensional prediction tracing and a virtual reality patient methods for diagnosis and treatment planning of orthognathic cases in dental students: a randomized preliminary study. Virtual Real. 2020;24(3):399-409.
  • 28
    Gredes T, Pricop-Jeckstadt M, Mereti E, Botzenhart U. Survey of student attitudes toward digital technology in practical technical dental education using the AR-Demonstrator-App. J Dent Educ. 2022 Jan;86(1):12-20.
  • 29
    Ho ACH, Liao C, Lu J, Shan Z, Gu M, Bridges SM, et al. 3-Dimensional simulations and student learning in orthodontic education. Eur J Dent Educ. 2022 Aug;26(3):435-45.
  • 30
    Lo YC, Chen GA, Liu YC, Chen YH, Hsu JT, Yu JH. Prototype of augmented reality technology for orthodontic bracket positioning: an in vivo study. Appl Sci. 2021;11(5):2315.
  • 31
    Sytek L, Inglehart MR, Ramaswamy V, Aronovich S, Edwards S, Kim-Berman H. Comparisons of orthodontic residents' performance and attitudes using 2D, 3D, and virtual reality surgical simulation methods. J Dent Educ. 2021 Aug;85(8):1415-26.
  • 32
    Ye F, Liu L, Yan B, Zhao X, Hao A. Orthodontic simulation system with force feedback for training complete bracket placement procedures. Virtual Real Intell Hardw. 2021 Aug;3(4):261-73.
  • 33
    Rodrigues MAF, Silva WB, Barbosa RG, Ribeiro IMMP, Neto MEB. J-Ortho: an open-source orthodontic treatment simulator. Proceedings of the 2006 ACM Symposium on Applied Computing; Dijon, France. Dijon: Association for Computing Machinery; 2006. p. 245-9.
  • 34
    Rodrigues MAF, Rocha RS, Silva WB. Interactive and accurate collision detection in virtual orthodontics. Proceedings of the 14th Eurographics conference on Virtual Environments; Eindhoven, The Netherlands. Eindhoven: Eurographics Association; 2008. p. 65-72.
  • 35
    Sinthanayothin C, Tharanont W. Orthodontics treatment simulation by teeth segmentation and setup. Proceedings of 2008 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology; 14-17 May 2008; Krabi, Thailand. Krabi: IEEE; 2008. p. 81-4.
  • 36
    Yaqi M, Zhongke L. Computer aided orthodontics treatment by virtual segmentation and adjustment. Proceedings of 2010 International Conference on Image Analysis and Signal Processing; 9-11 April 2010; Zhejiang, China. Zhejiang: IEEE; 2010. p. 336-9.
  • 37
    Rao GKL, Mokhtar NB, Iskandar YHP, editors. An integration of augmented reality technology for orthodontic education: case of bracket positioning. Proceedings of 2017 IEEE Conference on e-Learning, e-Management and e-Services (IC3e); 16-17 Nov. 2017; Miri, Ma-laysia. Miri: IEEE; 2017. p. 7-11.
  • 38
    Rao GKL, Mokhtar N, Iskandar YHP, Channarayapatna Srinivasa A. Learning orthodontic cephalometry through augmented reality: a conceptual machine learning validation approach. Proceedings of 2018 International Conference on Electrical Engineering and Informat-ics (ICELTICs); Banda Aceh, Indonesia. Banda Aceh: IEEE; 2018. p. 133-8.
  • 39
    Kumar Y. Automated virtual treatment planning in orthodontics: modeling and algo-rithms. Minnesota: University of Minnesota; 2012.
  • 40
    Kirkpatrick D, Kirkpatrick J. Transferring learning to behavior: using the four levels to improve performance. San Francisco: Berrett-Koehler Publishers; 2005.
  • 41
    Huang TK, Yang CH, Hsieh YH, Wang JC, Hung CC. Augmented reality (AR) and virtual reality (VR) applied in dentistry. Kaohsiung J Med Sci. 2018 Apr;34(4):243-8.
  • 42
    Vincent M, Joseph D, Amory C, Paoli N, Ambrosini P, Mortier É, et al. Contribution of haptic simulation to analogic training environment in restorative dentistry. J Dent Educ. 2020 Mar;84(3):367-76.
  • 43
    Bukhary DM, Alshali RZ. A simulation model used in teaching denture border adjustment: Randomized controlled trial. J Dent Educ. 2022 Jan;86(1):98-106.
  • 44
    Zafar S, Lai Y, Sexton C, Siddiqi A. Virtual reality as a novel educational tool in pre-clinical paediatric dentistry training: students' perceptions. Int J Paediatr Dent. 2020 Nov;30(6):791-7.
  • 45
    Gee JP. Learning and games. In: Salen K, editor. The ecology of games: connecting youth, games, and learning. Cambridge: MIT Press; 2008. p. 21-40.
  • 46
    Towers A, Field J, Stokes C, Maddock S, Martin N. A scoping review of the use and application of virtual reality in pre-clinical dental education. Br Dent J. 2019 Mar;226(5):358-66.
  • 47
    Li Y, Ye H, Ye F, Liu Y, Lv L, Zhang P, et al. The current situation and future prospects of simulators in dental education. J Med Internet Res. 2021 Apr;23(4):e23635.
  • 48
    Sipiyaruk K, Gallagher JE, Hatzipanagos S, Reynolds PA. A rapid review of serious games: From healthcare education to dental education. Eur J Dent Educ. 2018 Nov;22(4):243-57.
  • 49
    Lee J, Kim H, Kim KH, Jung D, Jowsey T, Webster CS. Effective virtual patient simula-tors for medical communication training: a systematic review. Med Educ. 2020 Sep;54(9):786-95.

Publication Dates

  • Publication in this collection
    17 July 2023
  • Date of issue
    2023

History

  • Received
    31 Oct 2021
  • Accepted
    05 Apr 2022
Dental Press International Av. Luís Teixeira Mendes, 2712 , 87015-001 - Maringá - PR, Tel: (55 44) 3033-9818 - Maringá - PR - Brazil
E-mail: artigos@dentalpress.com.br