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A Prospective Longitudinal Observational Study on the Emotional Impact of AI-Simulated Smiles on Orthodontic Patient
Thomas Mourgues1, María José González-Olmo1, Luis Huanca Ghislanzoni2
1Department of Orthodontics, Rey Juan Carlos University, 28922, Alcorcón, Madrid, Spain, urjc.es.
Background:
To evaluate the impact of the SmileView (SV) simulation on smile esthetic perception and its influence on motivation to undertake orthodontic treatment.
Methods:
A longitudinal study was conducted with 51 subjects in Madrid, Spain. Participants had an initial smile photo taken (T0) and a simulated image generated by SV (T1). They rated, on a 1-10 scale, the general perception of their smile and specific variables (width, alignment, color, gingival exposure, and shape) at both T0 and T1. Intention to pursue orthodontic treatment was recorded. Data were analyzed with SPSS 28.0.
Results:
Smile perception improved significantly postsimulation (T0: 5.84 ± 2.12; T1: 7.00 ± 2.61; p = 0.038), with notable gains in alignment (p < 0.001), color (p = 0.004), and shape (p = 0.008). No significant changes were found for width (p = 0.143) or gingival exposure (p = 0.721). Subjects with negative perception at T1 were influenced mainly by tooth shape (p = 0.033). Those with improved perception were driven by alignment, color, and shape. Intent to undergo orthodontic treatment decreased slightly (49.0%-45.0%). Influenced subjects were mostly women, aged 41, with higher education and socioeconomic status. While alignment, color, and shape influenced decision changes, perceived increase in smile width emerged as the strongest independent predictor of treatment intention.
Conclusion:
SV improves smile perception but has limited influence on treatment motivation without clinical guidance.
