ArticleFrontiers in microbiology2024
Recognition of parasitic helminth eggs via a deep learning-based platform.
Article in Frontiers in microbiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Deep learning applications in parasite microscopy: A scoping review.PLoS neglected tropical diseases · 2026Article
- Comparative Evaluation of Deep Learning Models for Diagnosis of Helminth Infections.Journal of personalized medicine · 2025Article
- Clinical challenges and technological breakthroughs in helminthic therapy for diabetes.Frontiers in immunology · 2025Review
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12 authors.
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Abstract
Introduction: Accurate and rapid diagnosis is crucial for the effective treatment of parasitosis. Traditional etiological methods, especially microscopic examination, are time-consuming, labor-intensive, and prone to false or missed detections. In response to these challenges, this study explores the use of artificial intelligence (AI) for the detection and classification of human parasite eggs through the YOLOv4 deep learning object detection algorithm. Methods: Eggs from species such as Results: The model demonstrated high recognition accuracy, achieving 100% for Discussion: The results show that this AI-assisted platform significantly reduces reliance on professional expertise while maintaining real-time efficiency and high accuracy, offering a powerful tool for the diagnosis and treatment of parasitosis. With further optimization, such as expanding training datasets and refining recognition algorithms, this AI system could become a key resource in both clinical and public health efforts to combat parasitic infections.
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