ArticleMalaria journal2021
Performance of a fully-automated system on a WHO malaria microscopy evaluation slide set.
Article in Malaria journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it, 67 citations in OpenAlex.
- The prognostic and diagnostic value of intraleukocytic malaria pigment in patients with severe falciparum malaria.Nature communications · 2022Pooled it
- Artificial intelligence algorithm for real-time detection and counting of Trypanosoma cruzi parasites using smartphone microscopy.PLoS neglected tropical diseases · 2026Article
- Evaluating the diagnostic performance of miLab™ for detection of malaria parasites using nPCR as reference standard.Malaria journal · 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
- A comparison of the cost-effectiveness of the current standard of care and of the haematology analyser XN31-microscopy combination for diagnosing malaria in a nonendemic country.Memorias do Instituto Oswaldo Cruz · 2026Article
- An interactive mindmap of blood film images for automated malaria diagnosis: a comprehensive metadata repository of available datasets.Bioinformatics advances · 2026Review
- NTDscope: A multi-contrast portable microscope for disease diagnosis.PLOS global public health · 2026Article
- Proceedings of the Clinical Microbiology Open 2024: artificial intelligence applications in clinical microbiology.Journal of clinical microbiology · 2025Article
- Whole-Cell Thermal Sensor for the Detection ofACS sensors · 2025Article
- Evaluating ECAMM-based training efficacy for malaria microscopists in Hunan Province, China.Frontiers in public health · 2025Article
- 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
- PERCEPTION OF HEALTH CARE WORKERS ON ARTIFICIAL INTELLIGENCE BASED MALARIA DIAGNOSIS IN SOUTHWESTERN NIGERIA.Annals of Ibadan postgraduate medicine · 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
- 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
- The Impact of Artificial Intelligence on Microbial Diagnosis.Microorganisms · 2024Review
- Malaria Slide Bank to Strengthen and Improve the Quality of Malaria Diagnosis: A National Slide Repository in India.The American journal of tropical medicine and hygiene · 2024Article
- Edge Artificial Intelligence (AI) for real-time automatic quantification of filariasis in mobile microscopy.PLoS neglected tropical diseases · 2024Article
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Authors and funding
29 authors at 15 institutions in 9 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundManual microscopy remains a widely-used tool for malaria diagnosis and clinical studies, but it has inconsistent quality in the field due to variability in training and field practices. Automated diagnostic systems based on machine learning hold promise to improve quality and reproducibility of field microscopy. The World Health Organization (WHO) has designed a 55-slide set (WHO 55) for their External Competence Assessment of Malaria Microscopists (ECAMM) programme, which can also serve as a valuable benchmark for automated systems. The performance of a fully-automated malaria diagnostic system, EasyScan GO, on a WHO 55 slide set was evaluated.
methodsThe WHO 55 slide set is designed to evaluate microscopist competence in three areas of malaria diagnosis using Giemsa-stained blood films, focused on crucial field needs: malaria parasite detection, malaria parasite species identification (ID), and malaria parasite quantitation. The EasyScan GO is a fully-automated system that combines scanning of Giemsa-stained blood films with assessment algorithms to deliver malaria diagnoses. This system was tested on a WHO 55 slide set.
resultsThe EasyScan GO achieved 94.3 % detection accuracy, 82.9 % species ID accuracy, and 50 % quantitation accuracy, corresponding to WHO microscopy competence Levels 1, 2, and 1, respectively. This is, to our knowledge, the best performance of a fully-automated system on a WHO 55 set.
conclusionsEasyScan GO's expert ratings in detection and quantitation on the WHO 55 slide set point towards its potential value in drug efficacy use-cases, as well as in some case management situations with less stringent species ID needs. Improved runtime may enable use in general case management settings.
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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.