RESUMEN
This study was aimed to assess whether facial asymmetry increases with age and to examine potential gender differences using 3D stereophotogrammetry. A prospective cross-sectional study was performed. 3D photographs were acquired from 600 control subjects, 300 male, 300 female, and were stratified into 15 different age groups ranging from 0 to 70+. The 3D photographs were postprocessed and mirrored. The original and mirrored faces were surface-based matched using an iterative closest point algorithm. The primary outcome variable, facial asymmetry, was evaluated by calculating the absolute mean distance between the original and mirrored images. The primary predictor was age. Pearson's correlation was used to assess the correlation between facial asymmetry and age. The average overall facial asymmetry was 0.72 mm (SD 0.72 mm; range 0.25 - 3.04 mm). Mean facial asymmetry increased significantly with age, from 0.45 mm in the age group of 0-4 years to 0.98 mm in the age group of 70+ (p<0.001). Facial asymmetry was positively correlated with age (Pearson's r = 0.55; p<0.001). Male subjects were significantly more asymmetric compared to females, 0.77 mm and 0.67 mm, respectively (p<0.001). This study indicates that facial asymmetry significantly increases with age and is significantly larger in males than in females.
RESUMEN
The integration of dentistry into primary health care is crucial for promoting patient well-being. However, clinical studies in dentistry face challenges, including issues with study design, transparency, and relevance to primary care. Clinical trials in dentistry often focus on specific issues with strict eligibility criteria, limiting the generalizability of findings. Randomized clinical trials (RCTs) face challenges in reflecting real-world conditions and using clinically relevant outcomes. The need for more pragmatic approaches and the inclusion of clinically relevant outcomes (CROs) is discussed, such as tooth loss or implant success. Solutions proposed include well-controlled observational studies, optimized data collection tools, and the integration of artificial intelligence (AI) for predictive modelling, computer-aided diagnostics and automated diagnosis. In this position paper advocates for more efficient trials with a focus on patient-centred outcomes, as well as the adoption of pragmatic study designs reflecting real-world conditions. Collaborative research networks, increased funding, enhanced data retrieval, and open science practices are also recommended. Technology, including intraoral scanners and AI, is highlighted for improving efficiency in dental research. AI is seen as a key tool for participant recruitment, predictive modelling, and outcome evaluation. However, ethical considerations and ongoing validation are emphasized to ensure the reliability and trustworthiness of AI-driven solutions in dental research. In conclusion, the efficient conduct of clinical research in primary care dentistry requires a comprehensive approach, including changes in study design, data collection, and analytical methods. The integration of AI is seen as pivotal in achieving these objectives in a meaningful and efficient way.
