Rahman Eqram, Sayed Karim, Rao Parinitha, Flanagan Aofie, Garcia Patricia E, Chowdhury Nusrat Jahan, Ioannidis Sotirios, Nassif Alexander D, Webb William Richard, Peng Hsien-Li Peter, Goodman Greg J, Carruthers Jean D A, Wu Woffles T L
Research and Innovation Hub, Innovation Aesthetics, London, UK.
Nomi Oslo, Oslo, Norway.
Aesthetic Plast Surg. 2025 Aug 25. doi: 10.1007/s00266-025-05131-0.
The study investigates the evolution of glabellar wrinkle patterns over the past century, examining their role as indicators of biological ageing, environmental exposure, and societal shifts. Beyond aesthetic concerns, these wrinkles reflect changes in human behaviour, technological advancements, and environmental conditions.
A dataset of 12,400 facial images from 1900 to 2024, featuring individuals aged 25-65, was analysed. Images were enhanced using Enhanced Super-Resolution Generative Adversarial Network to preserve wrinkle details. Convolutional neural networks and long short-term memory models identified temporal trends, while finite element analysis simulated the biomechanical factors influencing wrinkle formation, including skin elasticity and muscle dynamics.
The "11" wrinkle pattern increased from 33% in 1900 to 55% in 2024, linked to stress and prolonged screen exposure. Conversely, the "Omega" pattern declined from 56 to 28%, reflecting improved skincare and cosmetic interventions. Wrinkle depth decreased globally, influenced by reduced UV exposure and widespread use of aesthetic treatments. Biomechanical simulations highlighted the role of muscle interdigitation and skin elasticity in shaping wrinkle morphology.
Glabellar wrinkles are dynamic markers of human experience, reflecting the interplay of biology, environment, and culture. The interdisciplinary study, combining AI and dermatological insights, provides a comprehensive understanding of wrinkle evolution. The findings have significant implications for personalised aesthetic treatments, preventative skincare strategies, and public health policies addressing the impacts of modern lifestyles on skin ageing.
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本研究调查了过去一个世纪眉间皱纹模式的演变，考察了它们作为生物衰老、环境暴露和社会变迁指标的作用。除了美学方面的考量，这些皱纹还反映了人类行为、技术进步和环境条件的变化。
分析了一个包含1900年至2024年期间12400张面部图像的数据集，这些图像的主人公年龄在25至65岁之间。使用增强超分辨率生成对抗网络对图像进行增强，以保留皱纹细节。卷积神经网络和长短期记忆模型识别时间趋势，而有限元分析模拟了影响皱纹形成的生物力学因素，包括皮肤弹性和肌肉动态。
“11”皱纹模式从1900年的33%增加到2024年的55%，这与压力和长时间屏幕暴露有关。相反，“Ω”模式从56%下降到28%，反映了皮肤护理和美容干预的改善。全球皱纹深度有所下降，这受到紫外线暴露减少和美容治疗广泛使用的影响。生物力学模拟突出了肌肉交叉和皮肤弹性在塑造皱纹形态方面的作用。
眉间皱纹是人类经历的动态标志，反映了生物学、环境和文化之间的相互作用。这项结合了人工智能和皮肤病学见解的跨学科研究，提供了对皱纹演变的全面理解。这些发现对个性化美容治疗、预防性皮肤护理策略以及应对现代生活方式对皮肤衰老影响的公共卫生政策具有重要意义。
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