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Mathematical algorithm for the automatic recognition of intestinal parasites

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dc.contributor.author Alva, Alicia
dc.contributor.author Cangalaya, Carla
dc.contributor.author Quiliano, Miguel
dc.contributor.author Krebs, Casey
dc.contributor.author Gilman, Robert H.
dc.contributor.author Sheen, Patricia
dc.contributor.author Zimic, Mirko
dc.date.accessioned 2019-01-25T15:28:05Z
dc.date.available 2019-01-25T15:28:05Z
dc.date.issued 2017
dc.identifier.uri https://hdl.handle.net/20.500.12866/4699
dc.description.abstract Parasitic infections are generally diagnosed by professionals trained to recognize the morphological characteristics of the eggs in microscopic images of fecal smears. However, this laboratory diagnosis requires medical specialists which are lacking in many of the areas where these infections are most prevalent. In response to this public health issue, we developed a software based on pattern recognition analysis from microscopi digital images of fecal smears, capable of automatically recognizing and diagnosing common human intestinal parasites. To this end, we selected 229, 124, 217, and 229 objects from microscopic images of fecal smears positive for Taenia sp., Trichuris trichiura, Diphyllobothrium latum, and Fasciola hepatica, respectively. Representative photographs were selected by a parasitologist. We then implemented our algorithm in the open source program SCILAB. The algorithm processes the image by first converting to gray-scale, then applies a fourteen step filtering process, and produces a skeletonized and tri-colored image. The features extracted fall into two general categories: geometric characteristics and brightness descriptions. Individual characteristics were quantified and evaluated with a logistic regression to model their ability to correctly identify each parasite separately. Subsequently, all algorithms were evaluated for false positive cross reactivity with the other parasites studied, excepting Taenia sp. which shares very few morphological characteristics with the others. The principal result showed that our algorithm reached sensitivities between 99.10%-100% and specificities between 98.13%- 98.38% to detect each parasite separately. We did not find any cross-positivity in the algorithms for the three parasites evaluated. In conclusion, the results demonstrated the capacity of our computer algorithm to automatically recognize and diagnose Taenia sp., Trichuris trichiura, Diphyllobothrium latum, and Fasciola hepatica with a high sensitivity and specificity. en_US
dc.language.iso eng
dc.publisher Public Library of Science
dc.relation.ispartof urn:issn:1932-6203
dc.rights info:eu-repo/semantics/restrictedAccess
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject Humans en_US
dc.subject Microscopy en_US
dc.subject Animals en_US
dc.subject Sensitivity and Specificity en_US
dc.subject Algorithms en_US
dc.subject Diphyllobothriasis/diagnosis en_US
dc.subject Diphyllobothrium/growth & development en_US
dc.subject Fasciola hepatica/growth & development en_US
dc.subject Fascioliasis/diagnosis en_US
dc.subject Helminthiasis/diagnosis en_US
dc.subject Image Processing, Computer-Assisted en_US
dc.subject Ovum/pathology en_US
dc.subject Pattern Recognition, Automated en_US
dc.subject Taenia/growth & development en_US
dc.subject Taeniasis/diagnosis en_US
dc.subject Trichuriasis/diagnosis en_US
dc.subject Trichuris/growth & development en_US
dc.title Mathematical algorithm for the automatic recognition of intestinal parasites en_US
dc.type info:eu-repo/semantics/article
dc.identifier.doi https://doi.org/10.1371/journal.pone.0175646
dc.subject.ocde https://purl.org/pe-repo/ocde/ford#3.02.00 es_PE


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