ArticleOpen forum infectious diseases2023
Application of Deep Learning in Clinical Settings for Detecting and Classifying Malaria Parasites in Thin Blood Smears.
Article in Open forum infectious diseases, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- 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
- A hybrid machine learning approach for automated malaria diagnosis from thin blood smear images.Parasites & vectors · 2026Article
- Automated Malaria Ring Form Classification in Blood Smear Images Using Ensemble Parallel Neural Networks.Journal of imaging · 2026Article
- Intelligent identification of medical and veterinary intracellular protozoa by using self-supervised learning.Parasites & vectors · 2026Article
- Application of artificial intelligence in geriatric infection: recent advances and prospects.Frontiers in cellular and infection microbiology · 2026Review
- An interactive mindmap of blood film images for automated malaria diagnosis: a comprehensive metadata repository of available datasets.Bioinformatics advances · 2026Review
- Application of image recognition technology in pathological diagnosis of blood smears.Clinical and experimental medicine · 2024Review
- Confronting the Disruption of the Infectious Diseases Workforce by Artificial Intelligence: What This Means for Us and What We Can Do About It.Open forum infectious diseases · 2024Review
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Background: Scarcity of annotated image data sets of thin blood smears makes expert-level differentiation among Methods: You Only Look Once v7 was used as the backbone network for training the artificial intelligence algorithm model. The training, validation, and test sets for each malaria parasite category were randomly selected. A comprehensive analysis was performed on 12 708 thin blood smear images of various infective stages of 12 546 malaria parasites, including Results: The total time to identify 1116 malaria parasites was 13 seconds, with an average analysis time of 0.01 seconds for each parasite in the test set. The average precision was 0.902, with a recall and precision of infected erythrocytes of 96.0% and 94.9%, respectively. Sensitivity and specificity exceeded 96.8% and 99.3%, with an area under the receiver operating characteristic curve >0.999. The highest sensitivity (97.8%) and specificity (99.8%) were observed for trophozoites and merozoites. Conclusions: The algorithm can help facilitate the clinical and morphologic examination of malaria parasites.
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