Anaxomics Biotech, Barcelona, Spain.
Genocosmetics Laboratory, Barcelona, Spain.
Clin Cosmet Investig Dermatol. 2014 Apr 17;7:133-7. doi: 10.2147/CCID.S52257. eCollection 2014.
Perceived age has been defined as the age that a person is visually estimated to be on the basis of physical appearance. In a society where a youthful appearance are an object of desire for consumers, and a source of commercial profit for cosmetic companies, this concept has a prominent role. In addition, perceived age is also an indicator of overall health status in elderly people, since old-looking people tend to show higher rates of morbidity and mortality. However, there is a lack of objective methods for quantifying perceived age.
In order to satisfy the need of objective approaches for estimating perceived age, a novel algorithm was created. The novel algorithm uses supervised mathematical learning techniques and error retropropagation for the creation of an artificial neural network able to learn biophysical and clinically assessed parameters of subjects. The algorithm provides a consistent estimation of an individual's perceived age, taking into account a defined set of facial skin phenotypic traits, such as wrinkles and roughness, number of wrinkles, depth of wrinkles, and pigmentation. A nonintervention, epidemiological cross-sectional study of cases and controls was conducted in 120 female volunteers for the diagnosis of perceived age using this novel algorithm. Data collection was performed by clinical assessment of an expert panel and biophysical assessment using the ANTERA 3D(®) device.
Employing phenotype data as variables and expert assignments as objective data, the algorithm was found to correctly classify the samples with an accuracy of 92.04%. Therefore, we have developed a method for determining the perceived age of a subject in a standardized, consistent manner. Further application of this algorithm is thus a promising approach for the testing and validation of cosmetic treatments and aesthetic surgery, and it also could be used as a screening method for general health status in the population.
感知年龄是指根据外貌目测得出的一个人的年龄。在一个年轻的外表是消费者所渴望的，也是化妆品公司商业利润的来源的社会中，这个概念具有突出的作用。此外，感知年龄也是老年人整体健康状况的一个指标，因为看起来老的人往往表现出更高的发病率和死亡率。然而，目前缺乏量化感知年龄的客观方法。
为了满足客观评估感知年龄的需求，我们开发了一种新的算法。该算法使用有监督的数学学习技术和误差反向传播，创建一个能够学习受试者生物物理和临床评估参数的人工神经网络。该算法提供了对个体感知年龄的一致估计，考虑了一组定义明确的面部皮肤表型特征，如皱纹和粗糙度、皱纹数量、皱纹深度和色素沉着。我们对 120 名女性志愿者进行了一项非干预、病例对照的横断面研究，使用这种新算法来诊断感知年龄。数据收集是通过专家小组的临床评估和使用 ANTERA 3D（®）设备进行的生物物理评估来完成的。
我们发现，将表型数据作为变量，将专家评估作为客观数据，该算法能够以 92.04%的准确率正确分类样本。因此，我们已经开发出一种以标准化、一致的方式确定受试者感知年龄的方法。因此，进一步应用该算法是测试和验证化妆品治疗和美容手术的有前途的方法，也可以用作人群一般健康状况的筛选方法。
