Lastly, the necessity for skilled personnel, particularly problematic in resource-constrained environments where the disease is endemic, can significantly impede effective disease screening and diagnostic practices due to limited access to trained professionals 5. Secondly, the inherent subjectivity of the approach can lead to inconsistencies in result interpretation, thereby compromising the sensitivity and overall diagnostic accuracy of the technique. Firstly, the labor-intensive nature of manual microscopy necessitates a significant commitment of time and resources, potentially causing delays in diagnosis and treatment in settings with high disease prevalence. To end the neglect of attaining the SDGs, the WHO 2021–2030 roadmap for NTDs has identified the development of effective field-adaptable diagnostics and rapid screening tools as a prerequisite for meeting their trypanosomiasis targets by 2030 5.ĭespite its prevalence in the screening and diagnosis of trypanosomiasis, manual microscopy presents notable limitations, including its labor-intensive nature, low sensitivity, and the requirement for skilled personnel 5, 6, 7, 8. This impact is especially concerning as it disproportionately affects the most impoverished, vulnerable, and marginalized populations, impeding the achievement of the third United Nations Sustainable Development Goal (SDG) of ensuring good health and well-being. NTDs exert devastating human, social, and economic burdens on over one billion people worldwide, causing approximately 200,000 fatalities each year 5. If left untreated, HAT is usually chronic and fatal, with infected individuals frequently succumbing within six months 4. Tsetse flies in sub-Saharan African nations are the primary vector for HAT transmission. HAT, commonly referred to as sleeping sickness, is caused by two species of the Trypanosoma brucei parasite, namely T. This disease is mainly found in Latin America, affecting approximately six million individuals worldwide 3. Chagas disease, also known as American trypanosomiasis, is caused by the parasite Trypanosoma cruzi and is primarily transmitted by infected triatomine bugs. The World Health Organization (WHO) has categorized two forms of this condition, namely Chagas disease and human African trypanosomiasis (HAT), as neglected tropical diseases (NTDs) 1, 2. Trypanosomiasis is a debilitating disease caused by pathogenic species of the trypanosome parasite. 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. Furthermore, we provide a benchmark on three leading deep learning-based object detection techniques that demonstrate the feasibility of AI for this task. The Tryp dataset represents the largest of its kind. 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. In response to this challenge, we developed the Tryp dataset, comprising microscopy images of unstained thick blood smears containing the Trypanosoma brucei brucei parasite. While artificial intelligence has shown promising results in disease screening, the lack of curated datasets impedes progress. The World Health Organization underscores the need for practical, field-adaptable diagnostics and rapid screening tools to address the negative impact of NTDs. Trypanosomiasis, a neglected tropical disease (NTD), challenges communities in sub-Saharan Africa and Latin America.
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