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Computer-vision-based technology for fast, accurate and cost effective diagnosis of malaria

BACKGROUND: Microscopy has long been considered to be the gold standard for diagnosis of malaria despite the introduction of newer assays. However, it has many challenges like requirement of trained microscopists and logistic issues. A vision based device that can diagnose malaria, provide speciatio...

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Autores principales: Srivastava, Bina, Anvikar, Anupkumar R., Ghosh, Susanta K., Mishra, Neelima, Kumar, Navin, Houri-Yafin, Arnon, Pollak, Joseph Joel, Salpeter, Seth J., Valecha, Neena
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4696165/
https://www.ncbi.nlm.nih.gov/pubmed/26714633
http://dx.doi.org/10.1186/s12936-015-1060-1
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author Srivastava, Bina
Anvikar, Anupkumar R.
Ghosh, Susanta K.
Mishra, Neelima
Kumar, Navin
Houri-Yafin, Arnon
Pollak, Joseph Joel
Salpeter, Seth J.
Valecha, Neena
author_facet Srivastava, Bina
Anvikar, Anupkumar R.
Ghosh, Susanta K.
Mishra, Neelima
Kumar, Navin
Houri-Yafin, Arnon
Pollak, Joseph Joel
Salpeter, Seth J.
Valecha, Neena
author_sort Srivastava, Bina
collection PubMed
description BACKGROUND: Microscopy has long been considered to be the gold standard for diagnosis of malaria despite the introduction of newer assays. However, it has many challenges like requirement of trained microscopists and logistic issues. A vision based device that can diagnose malaria, provide speciation and estimate parasitaemia was evaluated. METHODS: The device was evaluated using samples from 431 consented patients, 361 of which were initially screened by RDT and microscopy and later analysed by PCR. It was a prospective, non-randomized, blinded trial. Quantification of parasitaemia was performed by two experienced technicians. Samples were subjected to diagnosis by Sight Dx digital imaging scanning. RESULTS: The sensitivity and specificity of the SightDx P1 device for analysed samples were found to be 97.05 and 96.33 %, respectively, when compared to PCR. When compared to microscopy, sensitivity and specificity were found to be 94.4 and 95.6 %, respectively. The device was able to speciate 73.3 % of the PCR Plasmodium falciparum positive samples and 91.4 % of PCR Plasmodium vivax positive samples. CONCLUSION: The ability of the device to detect parasitaemia as compared with microscopy, was within 50 % in 71.3 % of cases and demonstrated a correlation coefficient of 0.89.
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spelling pubmed-46961652015-12-31 Computer-vision-based technology for fast, accurate and cost effective diagnosis of malaria Srivastava, Bina Anvikar, Anupkumar R. Ghosh, Susanta K. Mishra, Neelima Kumar, Navin Houri-Yafin, Arnon Pollak, Joseph Joel Salpeter, Seth J. Valecha, Neena Malar J Research BACKGROUND: Microscopy has long been considered to be the gold standard for diagnosis of malaria despite the introduction of newer assays. However, it has many challenges like requirement of trained microscopists and logistic issues. A vision based device that can diagnose malaria, provide speciation and estimate parasitaemia was evaluated. METHODS: The device was evaluated using samples from 431 consented patients, 361 of which were initially screened by RDT and microscopy and later analysed by PCR. It was a prospective, non-randomized, blinded trial. Quantification of parasitaemia was performed by two experienced technicians. Samples were subjected to diagnosis by Sight Dx digital imaging scanning. RESULTS: The sensitivity and specificity of the SightDx P1 device for analysed samples were found to be 97.05 and 96.33 %, respectively, when compared to PCR. When compared to microscopy, sensitivity and specificity were found to be 94.4 and 95.6 %, respectively. The device was able to speciate 73.3 % of the PCR Plasmodium falciparum positive samples and 91.4 % of PCR Plasmodium vivax positive samples. CONCLUSION: The ability of the device to detect parasitaemia as compared with microscopy, was within 50 % in 71.3 % of cases and demonstrated a correlation coefficient of 0.89. BioMed Central 2015-12-30 /pmc/articles/PMC4696165/ /pubmed/26714633 http://dx.doi.org/10.1186/s12936-015-1060-1 Text en © Srivastava et al. 2015 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Srivastava, Bina
Anvikar, Anupkumar R.
Ghosh, Susanta K.
Mishra, Neelima
Kumar, Navin
Houri-Yafin, Arnon
Pollak, Joseph Joel
Salpeter, Seth J.
Valecha, Neena
Computer-vision-based technology for fast, accurate and cost effective diagnosis of malaria
title Computer-vision-based technology for fast, accurate and cost effective diagnosis of malaria
title_full Computer-vision-based technology for fast, accurate and cost effective diagnosis of malaria
title_fullStr Computer-vision-based technology for fast, accurate and cost effective diagnosis of malaria
title_full_unstemmed Computer-vision-based technology for fast, accurate and cost effective diagnosis of malaria
title_short Computer-vision-based technology for fast, accurate and cost effective diagnosis of malaria
title_sort computer-vision-based technology for fast, accurate and cost effective diagnosis of malaria
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4696165/
https://www.ncbi.nlm.nih.gov/pubmed/26714633
http://dx.doi.org/10.1186/s12936-015-1060-1
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