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Automated Recognition of Plasmodium falciparum Parasites from Portable Blood Levitation Imaging

In many malaria‐endemic regions, current detection tools are inadequate in diagnostic accuracy and accessibility. To meet the need for direct, phenotypic, and automated malaria parasite detection in field settings, a portable platform to process, image, and analyze whole blood to detect Plasmodium f...

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Autores principales: Deshmukh, Shreya S., Byaruhanga, Oswald, Tumwebaze, Patrick, Akin, Demir, Greenhouse, Bryan, Egan, Elizabeth S., Demirci, Utkan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9534981/
https://www.ncbi.nlm.nih.gov/pubmed/35957519
http://dx.doi.org/10.1002/advs.202105396
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author Deshmukh, Shreya S.
Byaruhanga, Oswald
Tumwebaze, Patrick
Akin, Demir
Greenhouse, Bryan
Egan, Elizabeth S.
Demirci, Utkan
author_facet Deshmukh, Shreya S.
Byaruhanga, Oswald
Tumwebaze, Patrick
Akin, Demir
Greenhouse, Bryan
Egan, Elizabeth S.
Demirci, Utkan
author_sort Deshmukh, Shreya S.
collection PubMed
description In many malaria‐endemic regions, current detection tools are inadequate in diagnostic accuracy and accessibility. To meet the need for direct, phenotypic, and automated malaria parasite detection in field settings, a portable platform to process, image, and analyze whole blood to detect Plasmodium falciparum parasites, is developed. The liberated parasites from lysed red blood cells suspended in a magnetic field are accurately detected using this cellphone‐interfaced, battery‐operated imaging platform. A validation study is conducted at Ugandan clinics, processing 45 malaria‐negative and 36 malaria‐positive clinical samples without external infrastructure. Texture and morphology features are extracted from the sample images, and a random forest classifier is trained to assess infection status, achieving 100% sensitivity and 91% specificity against gold‐standard measurements (microscopy and polymerase chain reaction), and limit of detection of 31 parasites per µL. This rapid and user‐friendly platform enables portable parasite detection and can support malaria diagnostics, surveillance, and research in resource‐constrained environments.
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spelling pubmed-95349812022-10-11 Automated Recognition of Plasmodium falciparum Parasites from Portable Blood Levitation Imaging Deshmukh, Shreya S. Byaruhanga, Oswald Tumwebaze, Patrick Akin, Demir Greenhouse, Bryan Egan, Elizabeth S. Demirci, Utkan Adv Sci (Weinh) Research Articles In many malaria‐endemic regions, current detection tools are inadequate in diagnostic accuracy and accessibility. To meet the need for direct, phenotypic, and automated malaria parasite detection in field settings, a portable platform to process, image, and analyze whole blood to detect Plasmodium falciparum parasites, is developed. The liberated parasites from lysed red blood cells suspended in a magnetic field are accurately detected using this cellphone‐interfaced, battery‐operated imaging platform. A validation study is conducted at Ugandan clinics, processing 45 malaria‐negative and 36 malaria‐positive clinical samples without external infrastructure. Texture and morphology features are extracted from the sample images, and a random forest classifier is trained to assess infection status, achieving 100% sensitivity and 91% specificity against gold‐standard measurements (microscopy and polymerase chain reaction), and limit of detection of 31 parasites per µL. This rapid and user‐friendly platform enables portable parasite detection and can support malaria diagnostics, surveillance, and research in resource‐constrained environments. John Wiley and Sons Inc. 2022-08-11 /pmc/articles/PMC9534981/ /pubmed/35957519 http://dx.doi.org/10.1002/advs.202105396 Text en © 2022 The Authors. Advanced Science published by Wiley‐VCH GmbH https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Articles
Deshmukh, Shreya S.
Byaruhanga, Oswald
Tumwebaze, Patrick
Akin, Demir
Greenhouse, Bryan
Egan, Elizabeth S.
Demirci, Utkan
Automated Recognition of Plasmodium falciparum Parasites from Portable Blood Levitation Imaging
title Automated Recognition of Plasmodium falciparum Parasites from Portable Blood Levitation Imaging
title_full Automated Recognition of Plasmodium falciparum Parasites from Portable Blood Levitation Imaging
title_fullStr Automated Recognition of Plasmodium falciparum Parasites from Portable Blood Levitation Imaging
title_full_unstemmed Automated Recognition of Plasmodium falciparum Parasites from Portable Blood Levitation Imaging
title_short Automated Recognition of Plasmodium falciparum Parasites from Portable Blood Levitation Imaging
title_sort automated recognition of plasmodium falciparum parasites from portable blood levitation imaging
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9534981/
https://www.ncbi.nlm.nih.gov/pubmed/35957519
http://dx.doi.org/10.1002/advs.202105396
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