ArticleBiological imaging2021
Automated detection and staging of malaria parasites from cytological smears using convolutional neural networks.
Article in Biological imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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.
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Who cites it
16 citing papers in PubMed, 45 citations in OpenAlex.
- Deep learning applications in parasite microscopy: A scoping review.PLoS neglected tropical diseases · 2026Article
- Imaging malaria parasites across scales and time.Journal of microscopy · 2026Review
- An interactive mindmap of blood film images for automated malaria diagnosis: a comprehensive metadata repository of available datasets.Bioinformatics advances · 2026Review
- Exploring the broad spectrum of machine learning technologies in the food sector: A comprehensive review of techniques and practical applications.Current research in food science · 2026Review
- Article
- Edge Artificial Intelligence (AI) for real-time automatic quantification of filariasis in mobile microscopy.PLoS neglected tropical diseases · 2024Article
- Automatic patient-level recognition of fourMicrobiology spectrum · 2024Article
- Application of Deep Learning in Clinical Settings for Detecting and Classifying Malaria Parasites in Thin Blood Smears.Open forum infectious diseases · 2023Article
- Robust Image Processing Framework for Intelligent Multi-Stage Malaria Parasite Recognition of Thick and Thin Smear Images.Diagnostics (Basel, Switzerland) · 2023Article
- Efficient Malaria Parasite Detection From Diverse Images of Thick Blood Smears for Cross-Regional Model Accuracy.IEEE open journal of engineering in medicine and biology · 2023Article
- Reducing data dimension boosts neural network-based stage-specific malaria detection.Scientific reports · 2022Article
- Potent Virustatic Polymer-Lipid Nanomimics Block Viral Entry and Inhibit Malaria Parasites In Vivo.ACS central science · 2022Article
- Integrating artificial intelligence into haematology training and practice: Opportunities, threats and proposed solutions.British journal of haematology · 2022Article
- Advances and challenges in automated malaria diagnosis using digital microscopy imaging with artificial intelligence tools: A review.Frontiers in microbiology · 2022Review
- Biophysical Tools and Concepts Enable Understanding of Asexual Blood Stage Malaria.Frontiers in cellular and infection microbiology · 2022Review
- Machine Learning and Its Applications for Protozoal Pathogens and Protozoal Infectious Diseases.Frontiers in cellular and infection microbiology · 2022Review
Corrections and comments
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Authors and funding
20 authors at 10 institutions in 5 countries.
Funding
Abstract
Microscopic examination of blood smears remains the gold standard for laboratory inspection and diagnosis of malaria. Smear inspection is, however, time-consuming and dependent on trained microscopists with results varying in accuracy. We sought to develop an automated image analysis method to improve accuracy and standardization of smear inspection that retains capacity for expert confirmation and image archiving. Here, we present a machine learning method that achieves red blood cell (RBC) detection, differentiation between infected/uninfected cells, and parasite life stage categorization from unprocessed, heterogeneous smear images. Based on a pretrained Faster Region-Based Convolutional Neural Networks (R-CNN) model for RBC detection, our model performs accurately, with an average precision of 0.99 at an intersection-over-union threshold of 0.5. Application of a residual neural network-50 model to infected cells also performs accurately, with an area under the receiver operating characteristic curve of 0.98. Finally, combining our method with a regression model successfully recapitulates intraerythrocytic developmental cycle with accurate lifecycle stage categorization. Combined with a mobile-friendly web-based interface, called PlasmoCount, our method permits rapid navigation through and review of results for quality assurance. By standardizing assessment of Giemsa smears, our method markedly improves inspection reproducibility and presents a realistic route to both routine lab and future field-based automated malaria diagnosis.
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What OpenQuestion holds
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.