ArticleScientific data2023
Tryp: a dataset of microscopy images of unstained thick blood smears for trypanosome detection.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Advances and Challenges in the Diagnosis of Vector-Borne Protozoal Infections in Veterinary Medicine.Pathogens (Basel, Switzerland) · 2026Review
- AIR-LEISH: A Dataset of Giemsa-Stained Microscopy Images for AI-based Leishmania amastigotes Detection.Scientific data · 2026Article
- Artificial intelligence-powered microscopy: Transforming the landscape of parasitology.Journal of microscopy · 2026Review
- Unstained Blood Smear Analysis: A Review of Rule-Based, Machine Learning, and Deep Learning Techniques.Journal of biophotonics · 2025Review
- YOLO-Tryppa: A Novel YOLO-Based Approach for Rapid and Accurate Detection of Small Trypanosoma Parasites.Journal of imaging · 2025Article
- Tryp: a dataset of microscopy images of unstained thick blood smears for trypanosome detection.Scientific data · 2023Article
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Authors and funding
10 authors.
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Abstract
Trypanosomiasis, a neglected tropical disease (NTD), challenges communities in sub-Saharan Africa and Latin America. The World Health Organization underscores the need for practical, field-adaptable diagnostics and rapid screening tools to address the negative impact of NTDs. While artificial intelligence has shown promising results in disease screening, the lack of curated datasets impedes progress. In response to this challenge, we developed the Tryp dataset, comprising microscopy images of unstained thick blood smears containing the Trypanosoma brucei brucei parasite. The Tryp dataset provides bounding box annotations for tightly enclosed regions containing the parasite for 3,085 positive images, and 93 images collected from negative blood samples. The Tryp dataset represents the largest of its kind. Furthermore, we provide a benchmark on three leading deep learning-based object detection techniques that demonstrate the feasibility of AI for this task. Overall, the availability of the Tryp dataset is expected to facilitate research advancements in diagnostic screening for this disease, which may lead to improved healthcare outcomes for the communities impacted.
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