ArticlePatterns (New York, N.Y.)2023
AIDMAN: An AI-based object detection system for malaria diagnosis from smartphone thin-blood-smear images.
Article in Patterns (New York, N.Y.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
- Deep learning applications in parasite microscopy: A scoping review.PLoS neglected tropical diseases · 2026Article
- Noninvasive malaria detection beyond blood sampling.Sensors & diagnostics · 2026Review
- Automated Malaria Ring Form Classification in Blood Smear Images Using Ensemble Parallel Neural Networks.Journal of imaging · 2026Article
- Diagnostic Accuracy of Utilizing Artificial Intelligence for Malaria Diagnostic: A Systematic Review and Meta-Analysis.Infectious disease reports · 2026Review
- Automated microscopy for malaria diagnosis in a reference laboratory in nonendemic settings.Parasites & vectors · 2026Article
- An interactive mindmap of blood film images for automated malaria diagnosis: a comprehensive metadata repository of available datasets.Bioinformatics advances · 2026Review
- Smartphone-based biosensing: a review of optical imaging, microfluidic integration, and AI-enhanced analysis.Mikrochimica acta · 2025Review
- Exploring Biomarkers for Malaria: Advances in Early Detection and Asymptomatic Diagnosis.Biosensors · 2025Review
- Predictive for patients with pneumonia in pediatric intensive care unit.Frontiers in pediatrics · 2025Article
- Stain-free artificial intelligence-assisted light microscopy for the identification of blood cells in microfluidic flow.Frontiers in bioinformatics · 2025Article
- Blood smear imagery dataset for malaria parasite detection: A case of Tanzania.Data in brief · 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
- Towards automatic farrowing monitoring-A Noisy Student approach for improving detection performance of newborn piglets.PloS one · 2024Article
- AIDMAN: An AI-based object detection system for malaria diagnosis from smartphone thin-blood-smear images.Patterns (New York, N.Y.) · 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
- Artificial intelligence in parasitic disease control: A paradigm shift in health care.Tropical parasitologyArticle
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Authors and funding
11 authors.
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Abstract
Malaria is a significant public health concern, with ∼95% of cases occurring in Africa, but accurate and timely diagnosis is problematic in remote and low-income areas. Here, we developed an artificial intelligence-based object detection system for malaria diagnosis (AIDMAN). In this system, the YOLOv5 model is used to detect cells in a thin blood smear. An attentional aligner model (AAM) is then applied for cellular classification that consists of multi-scale features, a local context aligner, and multi-scale attention. Finally, a convolutional neural network classifier is applied for diagnosis using blood-smear images, reducing interference caused by false positive cells. The results demonstrate that AIDMAN handles interference well, with a diagnostic accuracy of 98.62% for cells and 97% for blood-smear images. The prospective clinical validation accuracy of 98.44% is comparable to that of microscopists. AIDMAN shows clinically acceptable detection of malaria parasites and could aid malaria diagnosis, especially in areas lacking experienced parasitologists and equipment.
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