ReviewTranslational research : the journal of laboratory and clinical medicine2018
Image analysis and machine learning for detecting malaria.
Review in Translational research : the journal of laboratory and clinical medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 109 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
109 citing papers in PubMed, 536 citations in OpenAlex.
- PathoSafe: Sub-1 KB Out-of-Distribution Rejection and Cluster-Aware Certification for Point-of-Care Pathogen Biosensing.Biosensors · 2026Article
- Deep Learning-Based Malaria Parasite Image Classification on Real Microscopy Data.Infectious disease reports · 2026Article
- Image Processing and Deep Convolutional Neural Network Method for Automated Malaria Parasite Detection in Thin Blood Slide Images.Diagnostics (Basel, Switzerland) · 2026Article
- MalariaNet: A Microcontroller-Deployable Malaria-Microscopy Detector for Point-of-Care Biosensing Under Leakage-Free Evaluation.Biosensors · 2026Article
- Enhanced YOLO-based framework and benchmarking for automated Plasmodium vivax detection.Parasitology research · 2026Article
- LLM-Assisted Scoping Review of Artificial Intelligence in Brazilian Public Health: Lessons from Transfer and Federated Learning for Resource-Constrained Settings.International journal of environmental research and public health · 2026Article
- Automated microscopy for malaria diagnosis in a reference laboratory in nonendemic settings.Parasites & vectors · 2026Article
- Intelligent automation cognitive system for accurate malaria diagnosis using digital blood smears.PloS one · 2026Article
- An interactive mindmap of blood film images for automated malaria diagnosis: a comprehensive metadata repository of available datasets.Bioinformatics advances · 2026Review
- Framework for developing explainable artificial intelligence models for neglected tropical disease diagnosis in low-resource settings.Frontiers in digital health · 2026Article
- Comprehensive diagnostic approaches to feline toxoplasmosis: Bridging traditional methods and emerging technologies.Virulence · 2025Review
- Remoscope: a label-free imaging cytometer for malaria diagnostics.Transactions of the Royal Society of Tropical Medicine and Hygiene · 2025Article
- Image-Based Detection and Classification of Malaria Parasites and Leukocytes with Quality Assessment of Romanowsky-Stained Blood Smears.Sensors (Basel, Switzerland) · 2025Article
- Mass spectrometry-based metabolomics uncovers distinct metabolic signatures and potential therapeutic targets in Plasmodium knowlesi.PloS one · 2025Article
- An optimized transfer learning approach integrating deep convolutional feature extractors for malaria parasite classification in erythrocyte microscopy.Frontiers in medicine · 2025Article
- Enhanced Detection of Leishmania Parasites in Microscopic Images Using Machine Learning Models.Sensors (Basel, Switzerland) · 2024Article
- An Efficient Deep Learning Approach for Malaria Parasite Detection in Microscopic Images.Diagnostics (Basel, Switzerland) · 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
- Digital Holographic Microscopy in Veterinary Medicine-A Feasibility Study to Analyze Label-Free Leukocytes in Blood and Milk of Dairy Cows.Animals : an open access journal from MDPI · 2024Article
- CAM: a novel aid system to analyse the coloration quality of thick blood smears using image processing and machine learning techniques.Malaria journal · 2024Article
49 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 4 institutions in 3 countries.
Funding
Abstract
Malaria remains a major burden on global health, with roughly 200 million cases worldwide and more than 400,000 deaths per year. Besides biomedical research and political efforts, modern information technology is playing a key role in many attempts at fighting the disease. One of the barriers toward a successful mortality reduction has been inadequate malaria diagnosis in particular. To improve diagnosis, image analysis software and machine learning methods have been used to quantify parasitemia in microscopic blood slides. This article gives an overview of these techniques and discusses the current developments in image analysis and machine learning for microscopic malaria diagnosis. We organize the different approaches published in the literature according to the techniques used for imaging, image preprocessing, parasite detection and cell segmentation, feature computation, and automatic cell classification. Readers will find the different techniques listed in tables, with the relevant articles cited next to them, for both thin and thick blood smear images. We also discussed the latest developments in sections devoted to deep learning and smartphone technology for future malaria diagnosis.
Indexed as
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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.