Chica Juan Fernando, Zaputt Sayonara, Encalada Javier, Salamea Christian, Montalvo Melissa
Interaction, Robotics and Automation Research Group (GIIRA), Universidad Politécnica Salesiana, Cuenca, Ecuador.
Departament of Dermatology, José Carrasco Arteaga Hospital, Cuenca, Ecuador.
J Med Signals Sens. 2019 Apr-Jun;9(2):88-99. doi: 10.4103/jmss.JMSS_52_18.
Vitiligo is a pathology that causes the appearance of achromic macules on the skin that can spread on to other areas of the body. It is estimated that it affects 1.2% of the world population and can disrupt the mental state of people in whom this disease has developed, generating negative feelings that can become suicidal in the worst of cases. The present work focuses on the development of a support tool that allows to objectively quantifying the repigmentation of the skin.
We propose a novel method based on artificial neural networks that use characteristics of the interaction of light with the skin to determine areas of healthy skin and skin with vitiligo. We used photographs of specific areas of skin containing vitiligo. We select as independent variables: the type of skin, the amount of skin with vitiligo and the amount of repigmented skin. Considering these variables, the experiments were organized in an orthogonal table. We analyzed the result of the method based on three parameters (sensitivity, specificity, and F1-Score) and finally, its results were compared with other methods proposed in similar research.
The proposed method demonstrated the best performance of the three methods, and it also showed its capability to detect healthy skin and skin with vitiligo in areas up to 1 × 1 pixels.
The results show that the proposed method has the potential to be used in clinical applications. It should be noted that the performance could be significantly improved by increasing the training patterns.
白癜风是一种导致皮肤上出现无色斑的病症,这些色斑可扩散至身体其他部位。据估计,全球有1.2%的人口受其影响,它会扰乱患病者的心理状态,在最糟糕的情况下产生可能导致自杀的负面情绪。目前的工作聚焦于开发一种支持工具,用于客观量化皮肤的色素再生情况。
我们提出了一种基于人工神经网络的新方法,该方法利用光与皮肤相互作用的特征来确定健康皮肤区域和白癜风皮肤区域。我们使用了包含白癜风的特定皮肤区域的照片。我们选择皮肤类型、白癜风皮肤量和色素再生皮肤量作为自变量。考虑这些变量,实验按照正交表进行组织。我们基于三个参数(敏感性、特异性和F1分数)分析了该方法的结果,最后将其结果与类似研究中提出的其他方法进行了比较。
所提出的方法在三种方法中表现最佳,并且还展示了其在高达1×1像素的区域中检测健康皮肤和白癜风皮肤的能力。
结果表明所提出的方法具有用于临床应用的潜力。需要注意的是,通过增加训练模式,性能可能会显著提高。