Evidence map›Paper›PMID 35413904›Full record

Observational studyMalaria journal2022

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

Debashish Das, Ranitha Vongpromek, Thanawat Assawariyathipat, Ketsanee Srinamon, Kalynn Kennon, Kasia Stepniewska, Aniruddha Ghose, Abdullah Abu Sayeed, M Abul Faiz, Rebeca Linhares Abreu Netto and 32 more

Registry-linked trialOpen access · goldAbstract readObservational Study
In one paragraph

Observational study in Malaria journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03512678 (A Multi-Centric Evaluation of a Device for Automated Malaria Microscopy), which is not on this map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed
5.9field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT03512678 completednot on this map

A Multi-Centric Evaluation of a Device for Automated Malaria Microscopy (EasyScan Go)

TypeobservationalSponsorUniversity of OxfordRan2018 to 2020Enrolled2,250ConditionsMalaria
3 · Its place in the literature

Who cites it

28 citing papers in PubMed, 52 citations in OpenAlex.

  1. Article
  2. Mesolens imaging in microbiology.Essays in biochemistry · 2026
    Review
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  5. Review
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  9. Remoscope: a label-free imaging cytometer for malaria diagnostics.Transactions of the Royal Society of Tropical Medicine and Hygiene · 2025
    Article
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  11. Review
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  19. Article
  20. Automatic patient-level recognition of fourMicrobiology spectrum · 2024
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

42 authors at 17 institutions in 14 countries.

Debashish DasInfectious Diseases Data Observatory (IDDO), Oxford, UK.
Ranitha VongpromekInfectious Diseases Data Observatory (IDDO), Oxford, UK.
Thanawat AssawariyathipatInfectious Diseases Data Observatory (IDDO), Oxford, UK.
Ketsanee SrinamonFaculty of Tropical Medicine, Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand.
Kalynn KennonInfectious Diseases Data Observatory (IDDO), Oxford, UK.
Kasia StepniewskaInfectious Diseases Data Observatory (IDDO), Oxford, UK.
Aniruddha GhoseChittagong Medical College (CMC), Chattogram, Bangladesh.
Abdullah Abu SayeedChittagong Medical College (CMC), Chattogram, Bangladesh.
M Abul FaizDev Care Foundation, Dhaka, Bangladesh.
Rebeca Linhares Abreu NettoFundação de Medicina Tropical Dr Heitor Vieira Dourado, Manaus, Amazonas, Brazil.
Andre SiqueiraOswaldo Cruz Foundation (Fiocruz), Rio de Janeiro, Brazil.
Serge R YerbangaInstitut Des Sciences Et Techniques (INSTech), Bobo-Dioulasso, Burkina Faso.
Jean Bosco OuédraogoInstitut Des Sciences Et Techniques (INSTech), Bobo-Dioulasso, Burkina Faso.
James J CalleryCentre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
Thomas J PetoCentre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
Rupam TripuraCentre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
Felix Koukouikila-KoussoundaFondation Congolaise Pour La Recherche Médicale (FCRM), Brazzaville, Congo.
Francine NtoumiFondation Congolaise Pour La Recherche Médicale (FCRM), Brazzaville, Congo.
John Michael Ong'echaKenya Medical Research Institute (KEMRI), Nairobi, Kenya.
Bernhards OgutuKenya Medical Research Institute (KEMRI), Nairobi, Kenya.
Prakash GhimireTribhuvan University, Kathmandu, Nepal.
Jutta MarfurtGlobal and Tropical Health Division, Menzies School of Health Research, Charles Darwin University, Darwin, NT, Australia.
Benedikt LeyGlobal and Tropical Health Division, Menzies School of Health Research, Charles Darwin University, Darwin, NT, Australia.
Amadou SeckFaculty of Medicine, University Cheikh Anta Diop (UCAD), Dakar, Senegal.
Magatte NdiayeFaculty of Medicine, University Cheikh Anta Diop (UCAD), Dakar, Senegal.
Bhavani MoodleyParasitology Reference Laboratory, National Institute for Communicable Diseases, Division of the National Health Laboratory Service, Johannesburg, South Africa.
Lisa Ming SunParasitology Reference Laboratory, National Institute for Communicable Diseases, Division of the National Health Laboratory Service, Johannesburg, South Africa.
Laypaw ArchasuksanShoklo Malaria Research Unit, Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Mae Sot, Thailand.
Stephane ProuxShoklo Malaria Research Unit, Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Mae Sot, Thailand.
Sam L NsobyaDepartment of Pathology, College of Health Science, Makerere University, Kampala, Uganda.
Philip J RosenthalUniversity of California, San Francisco, CA, USA.
Matthew P HorningGlobal Health Labs, Bellevue, WA, USA.
Shawn K McGuireGlobal Health Labs, Bellevue, WA, USA.
Courosh MehanianGlobal Health Labs, Bellevue, WA, USA.
Stephen BurkotGlobal Health Labs, Bellevue, WA, USA.
Charles B DelahuntGlobal Health Labs, Bellevue, WA, USA.
Christine BachmanGlobal Health Labs, Bellevue, WA, USA.
Ric N PriceCentre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
Arjen M DondorpCentre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
François ChappuisDivision of Tropical and Humanitarian Medicine, Geneva University Hospitals and University of Geneva, Geneva, Switzerland.
Philippe J GuérinInfectious Diseases Data Observatory (IDDO), Oxford, UK.
Mehul DhordaInfectious Diseases Data Observatory (IDDO), Oxford, UK. mehul.dhorda@wwarn.org.ORCID http://orcid.org/0000-0002-7849-3293
Mahidol University · THBellevue Hospital Center · USUniversity of Oxford · GBCharles Darwin University · AUCheikh Anta Diop University · SNChittagong Medical College · BDFondation Congolaise Pour La Recherche Médicale · CGKenya Medical Research Institute · KENational Health Laboratory Service · ZAUniversity of Geneva · CHChild Health Research Foundation · BDFundação de Medicina Tropical · BRFundação Oswaldo Cruz · BRInfectious Diseases Research Collaboration · UGTribhuvan University · NPUniversity of California, San Francisco · USUniversity of Oregon · US

Funding

Wellcome Trust 200909/Z/16/Z
6 · The paper itself

Abstract

backgroundMicroscopic 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.

methodsA 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.

resultsIn 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.

conclusionsThe 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.

Indexed as

MalariaMalaria, FalciparumDiagnostic Tests, RoutineHumansMachine LearningMicroscopyParasitemiaPlasmodium falciparumReproducibility of ResultsSensitivity and SpecificityArtificial intelligenceDiagnostic accuracyDigital microscopyLight microscopyMachine-learningMalaria

Identifiers

PMID35413904
PMCPMC9004086
OpenAlexW4223537630

What OpenQuestion holds

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LicenceCC BY
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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.