ReviewComputational and structural biotechnology journal2022
Deep learning for microscopic examination of protozoan parasites.
Review in Computational and structural biotechnology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 45 citations in OpenAlex.
- An expert-level vision-language model for multitask diagnostic morphology in clinical laboratories.NPJ digital medicine · 2026Article
- The Evolution and Applications of Molecular Diagnostics in Veterinary and Clinical Medicine: From Traditional Methods to Emerging Technologies.Molecular biotechnology · 2026Review
- Decision-support system for live detection of Leishmania parasites from microscopic images with deep learning.BMC infectious diseases · 2026Article
- Intestinal parasitic infections and risk factors among schoolchildren in Taiz City Yemen.Scientific reports · 2026Article
- Enhanced YOLO-based framework and benchmarking for automated Plasmodium vivax detection.Parasitology research · 2026Article
- Intelligent identification of medical and veterinary intracellular protozoa by using self-supervised learning.Parasites & vectors · 2026Article
- Pathogenic Significance of Trypanosomatids: Progress in Drug Resistance, Control Strategies, and Artificial Intelligence.Interdisciplinary perspectives on infectious diseases · 2026Review
- Application of artificial intelligence in geriatric infection: recent advances and prospects.Frontiers in cellular and infection microbiology · 2026Review
- Biomolecular Interaction Prediction: The Era of AI.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Machine learning for predicting Plasmodium liver stage development in vitro using microscopy imaging.Computational and structural biotechnology journal · 2024Article
- Identification of veterinary and medically important blood parasites using contrastive loss-based self-supervised learning.Veterinary world · 2024Article
- Emerging Trends and Technologies Used for the Identification, Detection, and Characterisation of Plant-Parasitic Nematode Infestation in Crops.Plants (Basel, Switzerland) · 2024Review
- Enhancing parasitic organism detection in microscopy images through deep learning and fine-tuned optimizer.Scientific reports · 2024Article
- Development of a low-cost robotized 3D-prototype for automated optical microscopy diagnosis: An open-source system.PloS one · 2024Article
- Multiclass malaria parasite recognition based on transformer models and a generative adversarial network.Scientific reports · 2023Article
- An Efficient and Effective Framework for Intestinal Parasite Egg Detection Using YOLOv5.Diagnostics (Basel, Switzerland) · 2023Article
- Parasitic egg recognition using convolution and attention network.Scientific reports · 2023Article
- Eggsplorer: a rapid plant-insect resistance determination tool using an automated whitefly egg quantification algorithm.Plant methods · 2023Article
- Evaluating the Performance of Deep Learning Frameworks for Malaria Parasite Detection Using Microscopic Images of Peripheral Blood Smears.Diagnostics (Basel, Switzerland) · 2022Article
- Deep tech innovation for parasite diagnosis: New dimensions and opportunities.Tropical parasitologyArticle
Corrections and comments
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Authors and funding
9 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The infectious and parasitic diseases represent a major threat to public health and are among the main causes of morbidity and mortality. The complex and divergent life cycles of parasites present major difficulties associated with the diagnosis of these organisms by microscopic examination. Deep learning has shown extraordinary performance in biomedical image analysis including various parasites diagnosis in the past few years. Here we summarize advances of deep learning in the field of protozoan parasites microscopic examination, focusing on publicly available microscopic image datasets of protozoan parasites. In the end, we summarize the challenges and future trends, which deep learning faces in protozoan parasite diagnosis.
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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.