Scholarlink

Introduction <p>Glabellar wrinkle patterns, a visible manifestation of facial dynamics and ageing, result from complex interactions between anatomical and functional variables such as muscle interdigitation density, skin elasticity, and subcutaneous tissue characteristics. The standard injection pattern for botulinum toxin type A (BoNTA) is widely used but assumes uniformity in these factors, potentially overlooking individual anatomical and demographic variability. This study evaluates whether the standard injection pattern adequately addresses these differences.</p> Methods <p>Advanced biomechanical modelling and machine learning were employed to analyse the interactions between glabellar muscles, skin, and subcutaneous tissue. AI frameworks, including convolutional neural networks and long short-term memory networks, were trained on 3D facial scan data capturing dynamic expressions. Bayesian modelling quantified demography-specific predictors, while finite element analysis simulated wrinkle patterns under varying biomechanical conditions.</p> Results <p>The hybrid&#xa0;cohort included 600 participants (300 males, 300 females), evenly distributed across Caucasian (<i>n</i> = 150), African American (<i>n</i> = 150), Asian (<i>n</i> = 100), Hispanic (<i>n</i> = 100), and Middle Eastern (<i>n</i> = 100) ethnicities, aged 18–65 years. Significant predictors of wrinkle patterns were age (<i>β</i> = −0.64, <i>p</i> &lt; 0.001), elasticity (<i>β</i> = 0.48, <i>p</i> &lt; 0.01), and BMI (<i>β</i> = −0.52, p &lt; 0.01). Within our cohort, higher interdigitation density was observed in Asian and African American participants, producing narrower wrinkle configurations (“11”, “V”), while lower densities in Caucasian and Hispanic groups allowed broader patterns (“omega”, “converging arrows”). AI models achieved &gt;90% accuracy, demonstrating significant variability in wrinkle morphology and the need to reassess the universal applicability of the standard injection pattern.</p> Conclusion <p>This study underscores the limitations of the standard injection pattern, advocating for anatomically precise, personalised treatment protocols to better accommodate demographic and anatomical diversity in clinical practice.</p> Level of Evidence III <p>This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors &#xa0;<a href="http://www.springer.com/00266">www.springer.com/00266</a>.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

AI-Driven Insights into Glabellar Wrinkle Patterns: Reassessing the Standardised Botulinum Toxin: An Injection Protocol to Address Anatomical Variability

  • 期刊论文

  • 发表日期：

    2025年11月19日

查看全文
  • Eqram Rahman,
  • Patricia E. Garcia,
  • Karim Sayed,
  • Parinitha Rao,
  • Sotirios Ioannidis,
  • Nanze Yu,
  • Leila Sayed,
  • Aoife Flanagan,
  • Wolfgang G. Philipp-Dormston,
  • Alexander D. Nassif,
  • William Richard Webb,
  • Jean D. A. Carruthers,
  • Greg J. Goodman,
  • Woffles T. L. Wu

摘要

Introduction

Glabellar wrinkle patterns, a visible manifestation of facial dynamics and ageing, result from complex interactions between anatomical and functional variables such as muscle interdigitation density, skin elasticity, and subcutaneous tissue characteristics. The standard injection pattern for botulinum toxin type A (BoNTA) is widely used but assumes uniformity in these factors, potentially overlooking individual anatomical and demographic variability. This study evaluates whether the standard injection pattern adequately addresses these differences.

Methods

Advanced biomechanical modelling and machine learning were employed to analyse the interactions between glabellar muscles, skin, and subcutaneous tissue. AI frameworks, including convolutional neural networks and long short-term memory networks, were trained on 3D facial scan data capturing dynamic expressions. Bayesian modelling quantified demography-specific predictors, while finite element analysis simulated wrinkle patterns under varying biomechanical conditions.

Results

The hybrid cohort included 600 participants (300 males, 300 females), evenly distributed across Caucasian (n = 150), African American (n = 150), Asian (n = 100), Hispanic (n = 100), and Middle Eastern (n = 100) ethnicities, aged 18–65 years. Significant predictors of wrinkle patterns were age (β = −0.64, p < 0.001), elasticity (β = 0.48, p < 0.01), and BMI (β = −0.52, p < 0.01). Within our cohort, higher interdigitation density was observed in Asian and African American participants, producing narrower wrinkle configurations (“11”, “V”), while lower densities in Caucasian and Hispanic groups allowed broader patterns (“omega”, “converging arrows”). AI models achieved >90% accuracy, demonstrating significant variability in wrinkle morphology and the need to reassess the universal applicability of the standard injection pattern.

Conclusion

This study underscores the limitations of the standard injection pattern, advocating for anatomically precise, personalised treatment protocols to better accommodate demographic and anatomical diversity in clinical practice.

Level of Evidence III

This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors  www.springer.com/00266.