Advanced 3D Facial Scanning in Orthodontics: A Correlative Analysis of Craniofacial Anthropometric Parameters

Abstract

Background/Objectives: This study aims to investigate the correlation between vertical facial dimensions, lip morphology, angular facial parameters, and demographic variables (age and sex) in Romanian orthodontic patients, using structured-light 3D facial scanning technology. Methods: This cross-sectional study assessed craniofacial soft tissue parameters in 90 Romanian orthodontic patients (57 females, 33 males; median age 14 years) using the MetiSmile 3D facial scanner. Measurements included vertical facial heights (upper, middle, and lower), total facial height, facial proportion indices, lip lengths, interpupillary distance, bizygomatic width, mouth width, angular parameters (nasofrontal, nasolabial, mentolabial, and facial angles), and distances from the lips to the esthetic (E) line. Spearman’s rank correlation and Mann–Whitney U-tests were applied for statistical analysis. Results: A total of 90 subjects (63.3% female; median age, 14 years [range: 9–27 years]) were evaluated. Age showed strong positive correlations with total facial height (ρ = 0.690), middle (ρ = 0.631), and lower facial height (ρ = 0.615), while upper facial height had a weaker correlation (ρ = 0.334). Upper and lower lip lengths were moderately correlated with each other and with bizygomatic and interpupillary widths. Vertical facial proportion indices reflected distinct associations with their respective facial segments. Sex-based analysis revealed that females had significantly greater middle and total facial heights and nasofrontal angles, while males exhibited larger nasolabial angles. Upper lip protrusion relative to the E-line was significantly more pronounced in females (p = 0.041). No significant sex differences were noted in lip dimensions or transverse widths. Conclusions: The research demonstrates the value of 3D structured-light facial scanning in orthodontics as a non-invasive, radiation-free method for evaluating age- and sex-related morphological patterns. These findings support the integration of facial morphometric analysis into individualized diagnostic and treatment workflows in clinical orthodontics.