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Current Status of Computational Intelligence Applications in Dermatological Clinical Practice

机译：皮肤科临床实践中计算智能应用的现状

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Background: The yeast infections are increasingly frequent and the correct diagnosis consists of the identification of the yeast fungus, which in our case we are going to refer to the different species of Candida. The prescription of a broad-spectrum antifungal without taking into account the etiological agent, leads to an increase in the resistance to these treatments. Objective: The objective of this work is to differentiate Candida albicans from other Candida species (Candida spp.) By means of digital images obtained from the optical microscope. Material and Methods: It has reviewed about 100 photographs from patients in our consultations. In this study we will use the microscopic images of the Candida variety to be processed later with the Octave programming language and its image processing package (image-2.8.0). Results and Discussion: This system is able to differentiate Candida albicans from the other varieties of Candida such as C. parapsilosis, C. krusei, and C. kefyr with accuracy. The candida identifier application, which was designed and programmed in Octave, allows identification of candida species by locating certain geometric descriptors, such as the centroid and the surfaces of circular objects within the images. The program was highly effective for the diagnosis of Candida spp. So, we got a sensitivity and specificity above 90% with the images used. Conclusion: The results that we obtain from the Candida spp. identifier system that opens the way to be able to work with images obtained from the optical microscope.
机译：背景：酵母感染越来越频繁，正确的诊断包括鉴定酵母真菌，在我们的案例中我们将参考不同的念珠菌种类。广谱抗真菌的处方而不考虑到病因的病因，导致对这些处理的抗性增加。目的：这项工作的目的是通过从光学显微镜获得的数字图像来区分来自其他念珠菌种类（Candida SPP）的念珠菌醛糖。材料和方法：它已在咨询中审查了患者的约100张照片。在这项研究中，我们将使用糖丹的微观图像以后用八度编程语言及其图像处理包（Image-2.8.0）处理。结果与讨论：该系统能够将Candida醛糖蛋白与念珠菌等各种品种区分，例如C.Parapsiliss，C.Krusei和C.Kefyr以准确性。在Octave中设计和编程的念珠菌标识符应用程序允许通过定位某些几何描述符，例如质心和图像内的圆形物体的表面来识别念珠菌物种。该计划对念珠菌SPP的诊断非常有效。因此，我们使用的图像获得了90％以上的敏感性和特异性。结论：我们从Candida SPP获得的结果。打开能够使用从光学显微镜获得的图像的方式打开方法。

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