ORIGINAL RESEARCH article
Front. Microbiol.
Sec. Systems Microbiology
Volume 16 - 2025 | doi: 10.3389/fmicb.2025.1471436
This article is part of the Research TopicArtificial Intelligence and mNGS in Pathogenic Microorganism ResearchView all 8 articles
Rapid and accurate recognition of erythrocytic stage parasites of Plasmodium falciparum via a deep learning-based YOLOv3 platform
Provisionally accepted
- 1Jiangnan University, Wuxi, China
- 2Department of Human Parasitology, School of Basic Medical Sciences, Hubei University of Medicine, Shiyan, China
- 3Internet of Things Engineering College, Jiangnan University, Wuxi, Jiangsu Province, China
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Background: Malaria remains a fatal global infectious disease, with the erythrocytic stage of Plasmodium falciparum being its main pathogenic phase. Early diagnosis is critical for effective treatment. This study developed and evaluated an artificial intelligence-assisted diagnosis (AI-assisted diagnostic) tool for malaria parasites. Methods: The peripheral blood samples of malaria patients were collected. Thin blood film smear were prepared, stained and examined by microscopic. After manual confirmation and validation with qPCR, the images of infected red blood cells (iRBCs) of P. falciparum were captured. Using a sliding window method, each original image was cropped into 20 small images (518×486 pixels). Selected iRBCs were classified, and P. falciparum was detected using the YOLOv3 deep learning-based object detection algorithm. Results: A total of 262 images were tested. The YOLOv3 model detected 358 P. falciparum-containing iRBCs, with a false negative rate of 1.68% (6 missed iRBCs) and false positive rate of 3.91% (14 misreported iRBCs), yielding an overall recognition accuracy of 94.41%.. Conclusion: The developed AI-assisted diagnostic tool exhibits robust efficiency and accuracy in Plasmodium falciparum recognition in clinical thin blood smears. It provides a feasible technical support for malaria control in resource-limited settings.
Keywords: Malaria, Plasmodium falciparum, You Only Look Once (YOLO), artificial intelligence, deep learning
Received: 27 Jul 2024; Accepted: 15 Oct 2025.
Copyright: © 2025 He, Zhu, Geng, Zhu, Wu, Xie, Li and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Jian Li, yxlijian@163.com
Hailin Yang, bioprocessor@126.com
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