Abstract：
Machine Learning (ML) techniques provide the ability to effectively evaluate and analyze human skin and hair assessments. The aim of this study is to systematically review the effectiveness of applying Machine Learning (ML) methods and Artificial Intelligence (AI) techniques in order to evaluate hair and skin assessments. PubMed, Web of Science, IEEE Xplore, and Science Direct were searched in order to retrieve research publications between 1 January 2010 and 31 March 2020 using appropriate keywords such as \"hair and skin analysis.\" Following accurate screening, 20 peer-reviewed publications were selected for inclusion in this systematic review. The analysis demonstrated that prevalent Machine Learning (ML) methods comprised of Support Vector Machine (SVM), k-nearest Neighbor, and Artificial Neural Networks (ANN). ANN\'s were observed to yield the highest accuracy of 95% followed by SVM generating 90%. These techniques were most commonly applied for drafting framework assessments such as that of Melanoma. Values of parameters such as Sensitivity, Specificity, and Area under the Curve (AUC) were extracted from the studies and with the help of comparisons, relevant inferences were also made. ANN\'s were observed to yield the highest sensitivity of 82.30% as well as a 96.90% specificity. Hence, with this systematic review, a summarization of the studies was drafted that encapsulated how Machine Learning (ML) techniques have been employed for the analysis and evaluation of hair and skin assessments.
摘要：
机器学习（ML）技术提供了有效评估和分析人类皮肤和头发评估的能力。这项研究的目的是系统地回顾应用机器学习（ML）方法和人工智能（AI）技术来评估头发和皮肤评估的有效性。PubMed,WebofScience,IEEEXplore,为了检索2010年1月1日至2020年3月31日之间的研究出版物，使用适当的关键词，如“头发和皮肤分析”。“经过准确的筛查，选择了20篇同行评审的出版物纳入本系统综述。分析表明，流行的机器学习(ML)方法由支持向量机(SVM)组成，k-近邻，和人工神经网络(ANN)。观察到人工神经网络产生95%的最高准确度，其次是SVM产生90%。这些技术最常用于起草诸如黑色素瘤之类的框架评估。参数的值，如灵敏度，特异性,和曲线下面积（AUC）从研究中提取，并在比较的帮助下，也做出了相关推论。观察到ANN产生82.30%的最高灵敏度和96.90%的特异性。因此，有了这个系统的审查，起草了研究的摘要，其中概述了如何将机器学习（ML）技术用于头发和皮肤评估的分析和评估。
