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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.
Afiliação
  • Das D; Infectious Diseases Data Observatory (IDDO), Oxford, UK.
  • Vongpromek R; WorldWide Antimalarial Resistance Network (WWARN), Oxford, UK.
  • Assawariyathipat T; Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
  • Srinamon K; Institute of Global Health, University of Geneva, Geneva, Switzerland.
  • Kennon K; Infectious Diseases Data Observatory (IDDO), Oxford, UK.
  • Stepniewska K; WorldWide Antimalarial Resistance Network (WWARN), Oxford, UK.
  • Ghose A; Faculty of Tropical Medicine, Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand.
  • Sayeed AA; Infectious Diseases Data Observatory (IDDO), Oxford, UK.
  • Faiz MA; WorldWide Antimalarial Resistance Network (WWARN), Oxford, UK.
  • Netto RLA; Faculty of Tropical Medicine, Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand.
  • Siqueira A; Faculty of Tropical Medicine, Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand.
  • Yerbanga SR; Infectious Diseases Data Observatory (IDDO), Oxford, UK.
  • Ouédraogo JB; WorldWide Antimalarial Resistance Network (WWARN), Oxford, UK.
  • Callery JJ; Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
  • Peto TJ; Infectious Diseases Data Observatory (IDDO), Oxford, UK.
  • Tripura R; WorldWide Antimalarial Resistance Network (WWARN), Oxford, UK.
  • Koukouikila-Koussounda F; Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
  • Ntoumi F; Chittagong Medical College (CMC), Chattogram, Bangladesh.
  • Ong'echa JM; Chittagong Medical College (CMC), Chattogram, Bangladesh.
  • Ogutu B; Dev Care Foundation, Dhaka, Bangladesh.
  • Ghimire P; Fundação de Medicina Tropical Dr Heitor Vieira Dourado, Manaus, Amazonas, Brazil.
  • Marfurt J; Oswaldo Cruz Foundation (Fiocruz), Rio de Janeiro, Brazil.
  • Ley B; Institut Des Sciences Et Techniques (INSTech), Bobo-Dioulasso, Burkina Faso.
  • Seck A; Institut Des Sciences Et Techniques (INSTech), Bobo-Dioulasso, Burkina Faso.
  • Ndiaye M; Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
  • Moodley B; Faculty of Tropical Medicine, Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand.
  • Sun LM; Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
  • Archasuksan L; Faculty of Tropical Medicine, Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand.
  • Proux S; Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
  • Nsobya SL; Faculty of Tropical Medicine, Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand.
  • Rosenthal PJ; Fondation Congolaise Pour La Recherche Médicale (FCRM), Brazzaville, Congo.
  • Horning MP; Fondation Congolaise Pour La Recherche Médicale (FCRM), Brazzaville, Congo.
  • McGuire SK; Kenya Medical Research Institute (KEMRI), Nairobi, Kenya.
  • Mehanian C; Kenya Medical Research Institute (KEMRI), Nairobi, Kenya.
  • Burkot S; Tribhuvan University, Kathmandu, Nepal.
  • Delahunt CB; Global and Tropical Health Division, Menzies School of Health Research, Charles Darwin University, Darwin, NT, Australia.
  • Bachman C; Global and Tropical Health Division, Menzies School of Health Research, Charles Darwin University, Darwin, NT, Australia.
  • Price RN; Faculty of Medicine, University Cheikh Anta Diop (UCAD), Dakar, Senegal.
  • Dondorp AM; Faculty of Medicine, University Cheikh Anta Diop (UCAD), Dakar, Senegal.
  • Chappuis F; Parasitology Reference Laboratory, National Institute for Communicable Diseases, Division of the National Health Laboratory Service, Johannesburg, South Africa.
  • Guérin PJ; Parasitology Reference Laboratory, National Institute for Communicable Diseases, Division of the National Health Laboratory Service, Johannesburg, South Africa.
  • Dhorda M; Shoklo Malaria Research Unit, Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Mae Sot, Thailand.
Malar J ; 21(1): 122, 2022 Apr 12.
Article em En | MEDLINE | ID: mdl-35413904
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.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Malária Falciparum / Malária Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Malária Falciparum / Malária Idioma: En Ano de publicação: 2022 Tipo de documento: Article