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Mathematical algorithm for the automatic recognition of intestinal parasites.

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.
PMID
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Authors

Mayor MeshTerms

Algorithms

Keywords
Journal Title plos one
Publication Year Start




PMID- 28410387
OWN - NLM
STAT- MEDLINE
DA  - 20170414
DCOM- 20170426
LR  - 20170504
IS  - 1932-6203 (Electronic)
IS  - 1932-6203 (Linking)
VI  - 12
IP  - 4
DP  - 2017
TI  - Mathematical algorithm for the automatic recognition of intestinal parasites.
PG  - e0175646
LID - 10.1371/journal.pone.0175646 [doi]
AB  - 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.
FAU - Alva, Alicia
AU  - Alva A
AD  - Unidad de Bioinformatica, Laboratorios de Investigacion y Desarrollo, Facultad de
      Ciencias y Filosofia, Universidad Peruana Cayetano Heredia, Lima, Peru.
FAU - Cangalaya, Carla
AU  - Cangalaya C
AD  - Unidad de Bioinformatica, Laboratorios de Investigacion y Desarrollo, Facultad de
      Ciencias y Filosofia, Universidad Peruana Cayetano Heredia, Lima, Peru.
AD  - Laboratorio de Inmunopatologia en Neurocisticercosis, Laboratorio de
      Investigacion y Desarrollo, Facultad de Ciencias y Filosofia, Universidad Peruana
      Cayetano Heredia, Lima, Peru.
AD  - Facultad de Medicina Humana, Universidad Nacional Mayor de San Marcos, Lima,
      Peru.
FAU - Quiliano, Miguel
AU  - Quiliano M
AD  - Unidad de Bioinformatica, Laboratorios de Investigacion y Desarrollo, Facultad de
      Ciencias y Filosofia, Universidad Peruana Cayetano Heredia, Lima, Peru.
FAU - Krebs, Casey
AU  - Krebs C
AD  - Weill Cornell Medical College in New York City, New York, United States of
      America.
FAU - Gilman, Robert H
AU  - Gilman RH
AD  - Department of International Health, School of Public Health, Johns Hopkins
      University, Baltimore, Maryland, United States of America.
FAU - Sheen, Patricia
AU  - Sheen P
AD  - Unidad de Bioinformatica, Laboratorios de Investigacion y Desarrollo, Facultad de
      Ciencias y Filosofia, Universidad Peruana Cayetano Heredia, Lima, Peru.
FAU - Zimic, Mirko
AU  - Zimic M
AD  - Unidad de Bioinformatica, Laboratorios de Investigacion y Desarrollo, Facultad de
      Ciencias y Filosofia, Universidad Peruana Cayetano Heredia, Lima, Peru.
LA  - eng
PT  - Journal Article
DEP - 20170414
PL  - United States
TA  - PLoS One
JT  - PloS one
JID - 101285081
SB  - IM
MH  - *Algorithms
MH  - Animals
MH  - Diphyllobothriasis/diagnosis
MH  - Diphyllobothrium/growth & development
MH  - Fasciola hepatica/growth & development
MH  - Fascioliasis/diagnosis
MH  - Helminthiasis/*diagnosis
MH  - Humans
MH  - Image Processing, Computer-Assisted
MH  - Microscopy
MH  - Ovum/pathology
MH  - Pattern Recognition, Automated
MH  - Sensitivity and Specificity
MH  - Taenia/growth & development
MH  - Taeniasis/diagnosis
MH  - Trichuriasis/diagnosis
MH  - Trichuris/growth & development
PMC - PMC5391948
EDAT- 2017/04/15 06:00
MHDA- 2017/04/27 06:00
CRDT- 2017/04/15 06:00
PHST- 2016/08/19 [received]
PHST- 2017/03/29 [accepted]
AID - 10.1371/journal.pone.0175646 [doi]
AID - PONE-D-16-28433 [pii]
PST - epublish
SO  - PLoS One. 2017 Apr 14;12(4):e0175646. doi: 10.1371/journal.pone.0175646.
      eCollection 2017.

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