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Field evaluation of the diagnostic performance of EasyScan GO: a digital malaria microscopy device based on machine-learning

BACKGROUND: Microscopic examination of Giemsa-stained blood films remains the reference standard for malaria parasite detection and quantification, but is undermined by difficulties in ensuring high-quality manual reading and inter-reader reliability. Automated parasite detection and quantification...

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Autores principales: Das, Debashish, Vongpromek, Ranitha, Assawariyathipat, Thanawat, Srinamon, Ketsanee, Kennon, Kalynn, Stepniewska, Kasia, Ghose, Aniruddha, Sayeed, Abdullah Abu, Faiz, M. Abul, Netto, Rebeca Linhares Abreu, Siqueira, Andre, Yerbanga, Serge R., Ouédraogo, Jean Bosco, Callery, James J., Peto, Thomas J., Tripura, Rupam, Koukouikila-Koussounda, Felix, Ntoumi, Francine, Ong’echa, John Michael, Ogutu, Bernhards, Ghimire, Prakash, Marfurt, Jutta, Ley, Benedikt, Seck, Amadou, Ndiaye, Magatte, Moodley, Bhavani, Sun, Lisa Ming, Archasuksan, Laypaw, Proux, Stephane, Nsobya, Sam L., Rosenthal, Philip J., Horning, Matthew P., McGuire, Shawn K., Mehanian, Courosh, Burkot, Stephen, Delahunt, Charles B., Bachman, Christine, Price, Ric N., Dondorp, Arjen M., Chappuis, François, Guérin, Philippe J., Dhorda, Mehul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9004086/
https://www.ncbi.nlm.nih.gov/pubmed/35413904
http://dx.doi.org/10.1186/s12936-022-04146-1
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author Das, Debashish
Vongpromek, Ranitha
Assawariyathipat, Thanawat
Srinamon, Ketsanee
Kennon, Kalynn
Stepniewska, Kasia
Ghose, Aniruddha
Sayeed, Abdullah Abu
Faiz, M. Abul
Netto, Rebeca Linhares Abreu
Siqueira, Andre
Yerbanga, Serge R.
Ouédraogo, Jean Bosco
Callery, James J.
Peto, Thomas J.
Tripura, Rupam
Koukouikila-Koussounda, Felix
Ntoumi, Francine
Ong’echa, John Michael
Ogutu, Bernhards
Ghimire, Prakash
Marfurt, Jutta
Ley, Benedikt
Seck, Amadou
Ndiaye, Magatte
Moodley, Bhavani
Sun, Lisa Ming
Archasuksan, Laypaw
Proux, Stephane
Nsobya, Sam L.
Rosenthal, Philip J.
Horning, Matthew P.
McGuire, Shawn K.
Mehanian, Courosh
Burkot, Stephen
Delahunt, Charles B.
Bachman, Christine
Price, Ric N.
Dondorp, Arjen M.
Chappuis, François
Guérin, Philippe J.
Dhorda, Mehul
author_facet Das, Debashish
Vongpromek, Ranitha
Assawariyathipat, Thanawat
Srinamon, Ketsanee
Kennon, Kalynn
Stepniewska, Kasia
Ghose, Aniruddha
Sayeed, Abdullah Abu
Faiz, M. Abul
Netto, Rebeca Linhares Abreu
Siqueira, Andre
Yerbanga, Serge R.
Ouédraogo, Jean Bosco
Callery, James J.
Peto, Thomas J.
Tripura, Rupam
Koukouikila-Koussounda, Felix
Ntoumi, Francine
Ong’echa, John Michael
Ogutu, Bernhards
Ghimire, Prakash
Marfurt, Jutta
Ley, Benedikt
Seck, Amadou
Ndiaye, Magatte
Moodley, Bhavani
Sun, Lisa Ming
Archasuksan, Laypaw
Proux, Stephane
Nsobya, Sam L.
Rosenthal, Philip J.
Horning, Matthew P.
McGuire, Shawn K.
Mehanian, Courosh
Burkot, Stephen
Delahunt, Charles B.
Bachman, Christine
Price, Ric N.
Dondorp, Arjen M.
Chappuis, François
Guérin, Philippe J.
Dhorda, Mehul
author_sort Das, Debashish
collection PubMed
description BACKGROUND: Microscopic examination of Giemsa-stained blood films remains the reference standard for malaria parasite detection and quantification, but is undermined by difficulties in ensuring high-quality manual reading and inter-reader reliability. Automated parasite detection and quantification may address this issue. METHODS: A multi-centre, observational study was conducted during 2018 and 2019 at 11 sites to assess the performance of the EasyScan Go, a microscopy device employing machine-learning-based image analysis. Sensitivity, specificity, accuracy of species detection and parasite density estimation were assessed with expert microscopy as the reference. Intra- and inter-device reliability of the device was also evaluated by comparing results from repeat reads on the same and two different devices. This study has been reported in accordance with the Standards for Reporting Diagnostic accuracy studies (STARD) checklist. RESULTS: In total, 2250 Giemsa-stained blood films were prepared and read independently by expert microscopists and the EasyScan Go device. The diagnostic sensitivity of EasyScan Go was 91.1% (95% CI 88.9–92.7), and specificity 75.6% (95% CI 73.1–78.0). With good quality slides sensitivity was similar (89.1%, 95%CI 86.2–91.5), but specificity increased to 85.1% (95%CI 82.6–87.4). Sensitivity increased with parasitaemia rising from 57% at < 200 parasite/µL, to ≥ 90% at > 200–200,000 parasite/µL. Species were identified accurately in 93% of Plasmodium falciparum samples (kappa = 0.76, 95% CI 0.69–0.83), and in 92% of Plasmodium vivax samples (kappa = 0.73, 95% CI 0.66–0.80). Parasite density estimates by the EasyScan Go were within ± 25% of the microscopic reference counts in 23% of slides. CONCLUSIONS: The performance of the EasyScan Go in parasite detection and species identification accuracy fulfil WHO-TDR Research Malaria Microscopy competence level 2 criteria. In terms of parasite quantification and false positive rate, it meets the level 4 WHO-TDR Research Malaria Microscopy criteria. All performance parameters were significantly affected by slide quality. Further software improvement is required to improve sensitivity at low parasitaemia and parasite density estimations. Trial registration ClinicalTrials.gov number NCT03512678. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12936-022-04146-1.
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spelling pubmed-90040862022-04-13 Field evaluation of the diagnostic performance of EasyScan GO: a digital malaria microscopy device based on machine-learning Das, Debashish Vongpromek, Ranitha Assawariyathipat, Thanawat Srinamon, Ketsanee Kennon, Kalynn Stepniewska, Kasia Ghose, Aniruddha Sayeed, Abdullah Abu Faiz, M. Abul Netto, Rebeca Linhares Abreu Siqueira, Andre Yerbanga, Serge R. Ouédraogo, Jean Bosco Callery, James J. Peto, Thomas J. Tripura, Rupam Koukouikila-Koussounda, Felix Ntoumi, Francine Ong’echa, John Michael Ogutu, Bernhards Ghimire, Prakash Marfurt, Jutta Ley, Benedikt Seck, Amadou Ndiaye, Magatte Moodley, Bhavani Sun, Lisa Ming Archasuksan, Laypaw Proux, Stephane Nsobya, Sam L. Rosenthal, Philip J. Horning, Matthew P. McGuire, Shawn K. Mehanian, Courosh Burkot, Stephen Delahunt, Charles B. Bachman, Christine Price, Ric N. Dondorp, Arjen M. Chappuis, François Guérin, Philippe J. Dhorda, Mehul Malar J Research BACKGROUND: Microscopic examination of Giemsa-stained blood films remains the reference standard for malaria parasite detection and quantification, but is undermined by difficulties in ensuring high-quality manual reading and inter-reader reliability. Automated parasite detection and quantification may address this issue. METHODS: A multi-centre, observational study was conducted during 2018 and 2019 at 11 sites to assess the performance of the EasyScan Go, a microscopy device employing machine-learning-based image analysis. Sensitivity, specificity, accuracy of species detection and parasite density estimation were assessed with expert microscopy as the reference. Intra- and inter-device reliability of the device was also evaluated by comparing results from repeat reads on the same and two different devices. This study has been reported in accordance with the Standards for Reporting Diagnostic accuracy studies (STARD) checklist. RESULTS: In total, 2250 Giemsa-stained blood films were prepared and read independently by expert microscopists and the EasyScan Go device. The diagnostic sensitivity of EasyScan Go was 91.1% (95% CI 88.9–92.7), and specificity 75.6% (95% CI 73.1–78.0). With good quality slides sensitivity was similar (89.1%, 95%CI 86.2–91.5), but specificity increased to 85.1% (95%CI 82.6–87.4). Sensitivity increased with parasitaemia rising from 57% at < 200 parasite/µL, to ≥ 90% at > 200–200,000 parasite/µL. Species were identified accurately in 93% of Plasmodium falciparum samples (kappa = 0.76, 95% CI 0.69–0.83), and in 92% of Plasmodium vivax samples (kappa = 0.73, 95% CI 0.66–0.80). Parasite density estimates by the EasyScan Go were within ± 25% of the microscopic reference counts in 23% of slides. CONCLUSIONS: The performance of the EasyScan Go in parasite detection and species identification accuracy fulfil WHO-TDR Research Malaria Microscopy competence level 2 criteria. In terms of parasite quantification and false positive rate, it meets the level 4 WHO-TDR Research Malaria Microscopy criteria. All performance parameters were significantly affected by slide quality. Further software improvement is required to improve sensitivity at low parasitaemia and parasite density estimations. Trial registration ClinicalTrials.gov number NCT03512678. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12936-022-04146-1. BioMed Central 2022-04-12 /pmc/articles/PMC9004086/ /pubmed/35413904 http://dx.doi.org/10.1186/s12936-022-04146-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Das, Debashish
Vongpromek, Ranitha
Assawariyathipat, Thanawat
Srinamon, Ketsanee
Kennon, Kalynn
Stepniewska, Kasia
Ghose, Aniruddha
Sayeed, Abdullah Abu
Faiz, M. Abul
Netto, Rebeca Linhares Abreu
Siqueira, Andre
Yerbanga, Serge R.
Ouédraogo, Jean Bosco
Callery, James J.
Peto, Thomas J.
Tripura, Rupam
Koukouikila-Koussounda, Felix
Ntoumi, Francine
Ong’echa, John Michael
Ogutu, Bernhards
Ghimire, Prakash
Marfurt, Jutta
Ley, Benedikt
Seck, Amadou
Ndiaye, Magatte
Moodley, Bhavani
Sun, Lisa Ming
Archasuksan, Laypaw
Proux, Stephane
Nsobya, Sam L.
Rosenthal, Philip J.
Horning, Matthew P.
McGuire, Shawn K.
Mehanian, Courosh
Burkot, Stephen
Delahunt, Charles B.
Bachman, Christine
Price, Ric N.
Dondorp, Arjen M.
Chappuis, François
Guérin, Philippe J.
Dhorda, Mehul
Field evaluation of the diagnostic performance of EasyScan GO: a digital malaria microscopy device based on machine-learning
title Field evaluation of the diagnostic performance of EasyScan GO: a digital malaria microscopy device based on machine-learning
title_full Field evaluation of the diagnostic performance of EasyScan GO: a digital malaria microscopy device based on machine-learning
title_fullStr Field evaluation of the diagnostic performance of EasyScan GO: a digital malaria microscopy device based on machine-learning
title_full_unstemmed Field evaluation of the diagnostic performance of EasyScan GO: a digital malaria microscopy device based on machine-learning
title_short Field evaluation of the diagnostic performance of EasyScan GO: a digital malaria microscopy device based on machine-learning
title_sort field evaluation of the diagnostic performance of easyscan go: a digital malaria microscopy device based on machine-learning
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9004086/
https://www.ncbi.nlm.nih.gov/pubmed/35413904
http://dx.doi.org/10.1186/s12936-022-04146-1
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