Observational studyMalaria journal2022
Field evaluation of the diagnostic performance of EasyScan GO: a digital malaria microscopy device based on machine-learning.
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.
What it found
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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.
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.
A Multi-Centric Evaluation of a Device for Automated Malaria Microscopy (EasyScan Go)
Who cites it
28 citing papers in PubMed, 52 citations in OpenAlex.
- Deep learning applications in parasite microscopy: A scoping review.PLoS neglected tropical diseases · 2026Article
- Mesolens imaging in microbiology.Essays in biochemistry · 2026Review
- Artificial intelligence algorithm for real-time detection and counting of Trypanosoma cruzi parasites using smartphone microscopy.PLoS neglected tropical diseases · 2026Article
- Artificial intelligence-powered microscopy: Transforming the landscape of parasitology.Journal of microscopy · 2026Review
- Diagnostic Accuracy of Utilizing Artificial Intelligence for Malaria Diagnostic: A Systematic Review and Meta-Analysis.Infectious disease reports · 2026Review
- AI-Supported Digital Microscopy Diagnostics in Primary Health Care Laboratories: Scoping Review.Journal of medical Internet research · 2026Article
- NTDscope: A multi-contrast portable microscope for disease diagnosis.PLOS global public health · 2026Article
- The Landscape of Malaria in a Five-Decade Institution in the State of Amazonas: Historical Trends, Scientific Contributions, and Research Priorities.Revista da Sociedade Brasileira de Medicina Tropical · 2026Review
- Remoscope: a label-free imaging cytometer for malaria diagnostics.Transactions of the Royal Society of Tropical Medicine and Hygiene · 2025Article
- Multi-contrast machine learning improves schistosomiasis diagnostic performance.PLoS neglected tropical diseases · 2025Article
- Machine learning in point-of-care testing: innovations, challenges, and opportunities.Nature communications · 2025Review
- Evaluation of an Artificial Intelligence-Based Tool and a Universal Low-Cost Robotized Microscope for the Automated Diagnosis of Malaria.International journal of environmental research and public health · 2024Article
- Application of Machine Learning in a Rodent Malaria Model for Rapid, Accurate, and Consistent Parasite Counts.The American journal of tropical medicine and hygiene · 2024Article
- Current Status of Malaria Control and Elimination in Africa: Epidemiology, Diagnosis, Treatment, Progress and Challenges.Journal of epidemiology and global health · 2024Review
- Revolutionary Point-of-Care Wearable Diagnostics for Early Disease Detection and Biomarker Discovery through Intelligent Technologies.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- Application of image recognition technology in pathological diagnosis of blood smears.Clinical and experimental medicine · 2024Review
- The role of immune-inflammatory markers in children with complicated and uncomplicated malaria in Enugu, Nigeria.BMC immunology · 2024Article
- Diagnostic accuracy of an automated microscope solution (miLab™) in detecting malaria parasites in symptomatic patients at point-of-care in Sudan: a case-control study.Malaria journal · 2024Article
- The Walter Reed Project, Kisumu Field Station: Impact of Research on Malaria Policy, Management, and Prevention.The American journal of tropical medicine and hygiene · 2024Article
- Automatic patient-level recognition of fourMicrobiology spectrum · 2024Article
Corrections and comments
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Authors and funding
42 authors at 17 institutions in 14 countries.
Funding
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.
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