Abstract
Leishmaniases is a parasitic disease caused by the Leishmania parasite, transmitted by sandflies, affecting millions worldwide. Microscopic examination remains the standard method for detecting and quantifying intracellular parasite burden in leishmaniases research and Drug Discovery. This process is time-consuming and requires specific expertise. While Artificial Intelligence shows promise in automating this task, progress is limited by the lack of annotated datasets. To address this gap, we present AIR-LEISH, a dataset of 180 Giemsa-stained microscopic images with expert annotations containing 8,140 Leishmania amastigotes and 1511 macrophages. Images corresponded to samples from two infection models. The dataset was annotated to facilitate AI-based object detection and image segmentation tasks. We further demonstrated the potential of this dataset through training and testing two state-of-the-art architectures, namely YOLOv8 and U-Net. Both models demonstrated promising performance for automatic classification, detection and counting of amastigotes. The dataset is freely available on the Zenodo platform to accelerate the development of AI-based tools, facilitate advances in leishmaniases research and support collaborative initiatives for public health.
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Data availability
The datasets17 produced by this study are accessible on Zenodo under https://doi.org/10.5281/zenodo.17384855.
Code availability
The code and detailed documentation to reproduce the results presented in this study are publicly available at https://github.com/Harigua/AI_leish_microscopy under the GNU General Public Licence v3.0.
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Acknowledgements
This work has been produced with the financial assistance of the European Union (Grant no. DCI-PANAF/2020/420-028), through the African Research Initiative for Scientific Excellence (ARISE), pilot programme. ARISE is implemented by the African Academy of Sciences with support from the European Commission and the African Union Commission. The contents of this document are the sole responsibility of the author(s) and can under no circumstances be regarded as reflecting the position of the European Union, the African Academy of Sciences, and the African Union Commission. All authors acknowledge the support of the Ministry of Higher Education and Research of the Republic of Tunisia (LR16IPT04).
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Conceptualization: E.H.S..; Sample preparation: R.O.; Microscopy data acquisition: R.O., N.F.R., D.D., Y.Z.A.; Annotation: R.O., N.F.R.; Dataset curation: N.F.R.; Methodology: E.H.S., N.F.R.; Visualization: N.F.R., D.D.; Fund acquisition: E.H.S.; Resources: I.G., E.H.S.;Supervision: E.H.S., R.O.; Writing: R.O., E.H.S., N.F.R., D.D.; Manuscript review & editing: All authors.
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Oualha, R., Fekih-Romdhane, N., Driss, D. et al. AIR-LEISH: A Dataset of Giemsa-Stained Microscopy Images for AI-based Leishmania amastigotes Detection. Sci Data (2026). https://doi.org/10.1038/s41597-026-06676-8
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DOI: https://doi.org/10.1038/s41597-026-06676-8
