Current Status of Computational Intelligence Applications in Dermatological Clinical Practice
Carmen Rodriguez-Cerdeira1, 2, 3,4*, Jose Luis Gonzalez-Cespon1 and Roberto Arenas1, 3, 4,5
1Efficiency, Quality, and Costs in Health Services Research Group (EFISALUD), Health Research Institute. SERGAS-UVIGO.
2Dermatology Department, Hospital do Meixoeiro and University of Vigo, Vigo, Spain
3European Women’s Dermatologic and Venereologic Society (EWDVS), Tui, Spain
4Psychodermatology task force of the Ibero-Latin American College of Dermatology (CILAD), Buenos Aires, Argentina
5 Micology Department Manuel Gea González hospital, Mexico City, Mexico
Citation: Rodriguez-Cerdeira C, Gonzalez-Cespon JL, Arenas R (2021) Current Status of Computational Intelligence Applications in Dermatological Clinical Practice. SciTech Central Derma 2021.
Received: January 18, 2021 Accepted: January 20, 2021 Published: January 20, 2021
Abstract
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.
Keywords: Candida spp, Octave, Image processing, Candydos program, Computational intelligence, Clinical practice, Candida infections.
