Clinical audit of an artificial intelligence (AI) empowered smile simulation system: a prospective clinical trial.
Artificial intelligence
Invisalign
Invisalign SmileView
Smile aesthetics
Smile prediction
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
21 08 2024
21 08 2024
Historique:
received:
25
10
2023
accepted:
02
08
2024
medline:
22
8
2024
pubmed:
22
8
2024
entrez:
21
8
2024
Statut:
epublish
Résumé
Smile aesthetics is an important factor to consider during orthodontic treatment planning. The aim of the present study is to assess the predictability of Invisalign SmileView for digital AI smile simulation in comparison to actual smile treatment outcomes, using various smile assessment parameters. A total of 24 adult subjects (12 females and 12 males; mean age 22 ± 5.2 years) who chose to be treated using Invisalign were prospectively recruited to have their pretreatment smiles captured using the Invisalign SmileView to simulate their new smiles before treatment was started. Patients were then treated using upper and lower Invisalign aligners with average treatment time of 18 ± 6 months. Full post-treatment records were obtained and full smile frame images of simulated smile and actual final smile of each subject were evaluated by an independent examiner using an objective assessment sheet. Ten smile variants were used to assess the characteristics of the full smile images. Significance level was set at P < 0.05. The ICC for the quantitative parameters showed that there was an overall excellent & good internal consistency (alpha value > 0.7 & > 0.9). The Independent t test was performed amongst the quantitative variables. The P value was not significant for all except maxillary inter canine width (P = 0.05), stating that for the five variables namely; philtrum height, commissure height, smile width, buccal corridor and smile index, actual mean values were similar to the simulation mean values. For the qualitative variables, the Kappa value ranged between 0.66 and - 0.75 which showed a substantial level of agreement between the examiners. Additionally, the Chi square test for the qualitative variables, revealed that the P value was found to be significant in all except lip line. This implies that only the lip line values are comparable. More optimal lip lines, straighter smile arcs and more ideal tooth display were achieved in actual post treatment results in comparison to the initially predicted smiles. Five quantitative smile assessment parameters i.e., philtrum height, commissure height, smile width, buccal corridor, and smile index, could be used as reliable predictors of smile simulation. Maxillary inter canine width cannot be considered to be a reliable parameter for smile simulation prediction. A single qualitative parameter, namely the lip line, can be used as a reliable predictor for smile simulation. Three qualitative parameters i.e., most posterior tooth display, smile arc, and amount of lower incisor exposure cannot be considered as reliable parameters for smile prediction.Trial Registration number and date: NCT06123585, (09/11/2023).
Identifiants
pubmed: 39169095
doi: 10.1038/s41598-024-69314-6
pii: 10.1038/s41598-024-69314-6
doi:
Banques de données
ClinicalTrials.gov
['NCT06123585']
Types de publication
Journal Article
Clinical Trial
Langues
eng
Sous-ensembles de citation
IM
Pagination
19385Informations de copyright
© 2024. The Author(s).
Références
Patankar, D. & Khatri, D. Smile esthetics in orthodontic: A review article. Int. J. Appl. Dent. Sci. 7, 223–227 (2021).
Ackerman, M. B. & Ackerman, J. L. Smile analysis and design in the digital era. J. Clin. Orthod. 36, 221–236 (2002).
pubmed: 12025359
Morley, J. & Eubank, J. Macroesthetic elements of smile design. J. Am. Dent. Assoc. 132, 39–45 (2001).
pubmed: 11194397
Sabri, R. The eight components of a balanced smile. J. Clin. Orthod. 39, 155–167 (2005).
pubmed: 15888949
Machado, A. W. 10 commandments of smile esthetics. Dental. Press. J. Orthod. 19, 136–157 (2014).
pubmed: 25279532
pmcid: 4296640
Kravitz, N. D., Kusnoto, B., BeGole, E., Obrez, A. & Agran, B. How well does Invisalign work? A prospective clinical study evaluating the efficacy of tooth movement with Invisalign. Am. J. Orthod. Dentofacial. Orthop. 135, 27–35 (2009).
pubmed: 19121497
Haouili, N., Kravitz, N. D., Vaid, N. R., Ferguson, D. J. & Makki, L. Has Invisalign improved? A prospective follow-up study on the efficacy of tooth movement with Invisalign. Am. J. Orthod. Dentofacial. Orthop. 158, 420–425 (2020).
pubmed: 32620479
Fiori, A. et al. Predictability of crowding resolution in clear aligner treatment. Prog. Orthod. 23, 43 (2022).
pubmed: 36437397
pmcid: 9702322
Prabakaran, R., Seymour, S., Moles, D. R. & Cunningham, S. J. Motivation for orthodontic treatment investigated with Q-methodology: patients’ and parents’ perspectives. Am. J. Orthod. Dentofacial. Orthop. 142, 213–220 (2012).
pubmed: 22858331
Meghna, V., Nikhilesh, V., Dhaval, F. & Meetali, S. Integrating, “Experience Economy” into orthodontic practice management: a current perspective on internal marketing (Elsevier, 2016).
With the fastest treatment, you'll be smiling in no time. Available at: https://www.invisalign.com/get-started/invisalign-smileview . 2023.
Paim, J. et al. Assessment of patients’ knowledge and preferences for the use of orthodontic aligners. J. Orthod. https://doi.org/10.1177/14653125241229456 (2024).
pubmed: 38323415
Kravitz, N. D., Dalloul, B., Zaid, Y. A., Shah, C. & Vaid, N. R. What percentage of patients switch from Invisalign to braces? A retrospective study evaluating the conversion rate, number of refinement scans, and length of treatment. Am. J. Orthod. Dentofacial. Orthop. 163, 526–530 (2023).
pubmed: 36539316
Alansari, R. & Vaiid, N. Why do patients transition between orthodontic appliances? A qualitative analysis of patient decision-making. Orthod. Craniofac. Res. https://doi.org/10.1111/ocr.12750 (2023).
pubmed: 38149336
Ibrahim, H. et al. Reporting guidelines for clinical trials of artificial intelligence interventions: the SPIRIT-AI and CONSORT-AI guidelines. Trials. 22, 11 (2021).
pubmed: 33407780
pmcid: 7788716
Invisalign® Lite Package. Available at: https://cloud.news.aligntech.com/lite .
Paken, G. & Ünal, M. Evaluation of perceptions of smile esthetics by dental students. Balk. J. Dent. Med. 25, 100–107 (2021).
Zou, G. Y. Sample size formulas for estimating intraclass correlation coefficients with precision and assurance. Stat. Med. 31, 3972–3981 (2012).
pubmed: 22764084
Arrubla-Escobar, D. et al. Smile aesthetics assessment in patients undergoing the finishing phase of orthodontic treatment: An observational cross-sectional study. Cureus. 15, e45644 (2023).
pubmed: 37868569
pmcid: 10590081
Tjan, A. H., Miller, G. D. & J. G.,. The Some esthetic factors in a smile. J. Prosthet. Dent. 51, 24–28 (1984).
pubmed: 6583388
Wang, C., Hu, W. J., Liang, L. Z., Zhang, Y. L. & Chung, K. H. Esthetics and smile-related characteristics assessed by laypersons. J. Esthet. Restor. Dent. 30, 136–145 (2018).
pubmed: 29285855
Steiner, C. C. Cephalometrics for you and me. Am. J. Orthod. 39, 729–755 (1953).
Sarver, D. M. The importance of incisor positioning in the esthetic smile: the smile arc. Am. J. Orthod. Dentofacial. Orthop. 120, 98–111 (2001).
pubmed: 11500650
Khan, M., Kazmi, S. M. R., Khan, F. R. & Samejo, I. Analysis of different characteristics of smile. BDJ Open. 6, 6 (2020).
pubmed: 32411387
pmcid: 7200793
Dickens, S., Sarver, D. & Proffit, W. The dynamics of the maxillary incisor and the upper lip: a cross-sectional study of resting and smile hard tissue characteristics. World. J. Orthod. 3, 313–320 (2002).
Sarver, D. M. & Ackerman, M. B. Dynamic smile visualization and quantification: part 1. Evolution of the concept and dynamic records for smile capture. Am. J. Orthod. Dentofacial. Orthop. 124, 4–12 (2003).
pubmed: 12867893
Sachdeva, K., Singla, A., Mahajan, V., Jaj, H. & Negi, A. Esthetic and smile characteristics at rest and during smiling. J. Indian. Orthod. Soc. 46, 17–25 (2012).
McNamara, L., McNamara, J. A. Jr., Ackerman, M. B. & Baccetti, T. Hard- and soft-tissue contributions to the esthetics of the posed smile in growing patients seeking orthodontic treatment. Am J Orthod Dentofacial Orthop 133, 491–499 (2008).
pubmed: 18405812
Janson, G., Branco, N. C., Morais, J. F. & Freitas, M. R. Smile attractiveness in patients with Class II division 1 subdivision malocclusions treated with different tooth extraction protocols. Eur. J. Orthod. 36, 1–8 (2014).
pubmed: 21771805
Moore, T., Southard, K. A., Casko, J. S., Qian, F. & Southard, T. E. Buccal corridors and smile esthetics. Am. J. Orthod. Dentofacial. Orthop. 127, 208–213 (2005).
pubmed: 15750540
Frush, J. P. & Fisher, R. D. The dynesthetic interpretation of the dentogenic concept. J. Prosthet. Dent. 8, 558–581 (1958).
Drummond, S. & Capelli, J. Jr. Incisor display during speech and smile: Age and gender correlations. Angle. Orthod. 86, 631–637 (2016).
pubmed: 26535953
Bichu, Y. M. et al. Applications of artificial intelligence and machine learning in orthodontics: a scoping review. Prog. Orthod. 22, 18 (2021).
pubmed: 34219198
pmcid: 8255249
Gandedkar, N. H., Wong, M. T. & Darendeliler, M. A. Role of virtual reality (VR), augmented reality (AR) and artificial intelligence (AI) in tertiary education and research of orthodontics: An insight. Semin. Orthod. https://doi.org/10.1053/j.sodo.2021.05.003 (2021).
Mohamed, M. et al. An artificial intelligence-based referral application to optimize orthodontic referrals in a public oral healthcare system. Semin. Orthod. https://doi.org/10.1053/j.sodo.2021.05.011 (2021).
Vaid, N. R. & Adel, S. M. Contemporary orthodontic workflows: A panacea for efficiency?. Semin. Orthod. https://doi.org/10.1053/j.sodo.2023.02.002 (2023).
Adel, S. et al. Robotic applications in orthodontics: changing the face of contemporary clinical care. Biomed. Res. Int. 2021, 9954615 (2021).
pubmed: 34222490
pmcid: 8225419
Ryu, J. et al. Application of deep learning artificial intelligence technique to the classification of clinical orthodontic photos. BMC. Oral. Health. 22, 454 (2022).
pubmed: 36284294
pmcid: 9597951
Li, S., Guo, Z., Lin, J. & Ying, S. Artificial Intelligence for Classifying and Archiving Orthodontic Images. Biomed. Res. Int. 2022, 1473977 (2022).
pubmed: 35127938
pmcid: 8813223
Adel, S. M., Vaid, N. R., El-Harouni, N., Kassem, H. & Zaher, A. R. TIP, TORQUE & ROTATIONS: How accurately do digital superimposition software packages quantify tooth movement?. Prog. Orthod. 23, 8 (2022).
pubmed: 35284950
pmcid: 8918442
Adel, S. M., Vaid, N. R., El-Harouni, N., Kassem, H. & Zaher, A. R. Digital model superimpositions: are different software algorithms equally accurate in quantifying linear tooth movements?. BMC. Oral. Health. 22, 103 (2022).
pubmed: 35361187
pmcid: 8973572
Adel, S. M. et al. Quantifying maxillary anterior tooth movement in digital orthodontics: Does the choice of the superimposition software matter?. J. World Fed. Orthod. 12, 187–196 (2023).
pubmed: 37625927
Machado, A. W., McComb, R. W., Moon, W. & Gandini, L. G. Jr. Influence of the vertical position of maxillary central incisors on the perception of smile esthetics among orthodontists and laypersons. J. Esthet. Restor. Dent. 25, 392–401 (2013).
pubmed: 24180675
Zachrisson, B. Esthetics in Tooth Display and Smile Design. In: Nanda R, (ed). Biomechanics and Esthetic Strategies in Clinical Orthodontics. 1st ed: Saunders; 110–130 (2005).
Krishnan, V., Daniel, S. T., Lazar, D. & Asok, A. Characterization of posed smile by using visual analog scale, smile arc, buccal corridor measures, and modified smile index. Am. J. Orthod. Dentofacial. Orthop. 133, 515–523 (2008).
pubmed: 18405815
Nascimento, D. C., Santos, Ê. R. D., Machado, A. W. L. & Bittencourt, M. A. V. Influence of buccal corridor dimension on smile esthetics. Dental. Press. J. Orthod. 17, 145–150 (2012).
Katyal, V. & Vaid, K. Virtual-First: A virtual workflow for new patient consultation, engagement and education in orthodontics. Semin. Orthod. 29, 109–115 (2023).