{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T07:31:37Z","timestamp":1791617497435,"version":"4.3.4"},"reference-count":22,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T00:00:00Z","timestamp":1769990400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T00:00:00Z","timestamp":1772755200000},"content-version":"vor","delay-in-days":32,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","award":["DCI-PANAF\/2020\/420-028"],"award-info":[{"award-number":["DCI-PANAF\/2020\/420-028"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","award":["DCI-PANAF\/2020\/420-028"],"award-info":[{"award-number":["DCI-PANAF\/2020\/420-028"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","award":["DCI-PANAF\/2020\/420-028"],"award-info":[{"award-number":["DCI-PANAF\/2020\/420-028"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","award":["DCI-PANAF\/2020\/420-028"],"award-info":[{"award-number":["DCI-PANAF\/2020\/420-028"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","award":["DCI-PANAF\/2020\/420-028"],"award-info":[{"award-number":["DCI-PANAF\/2020\/420-028"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","award":["DCI-PANAF\/2020\/420-028"],"award-info":[{"award-number":["DCI-PANAF\/2020\/420-028"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Ministry of Higher Education and Research of the Republic of Tunisia"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Data"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Leishmaniases is a parasitic disease caused by the\n                    <jats:italic>Leishmania<\/jats:italic>\n                    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\n                    <jats:italic>Leishmania<\/jats:italic>\n                    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.\n                  <\/jats:p>","DOI":"10.1038\/s41597-026-06676-8","type":"journal-article","created":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T06:54:29Z","timestamp":1770015269000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["AIR-LEISH: A Dataset of Giemsa-Stained Microscopy Images for AI-based Leishmania amastigotes Detection"],"prefix":"10.1038","volume":"13","author":[{"given":"Rafeh","family":"Oualha","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nesrine","family":"Fekih-Romdhane","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Donia","family":"Driss","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yosser Zina","family":"Abdelkrim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2763-8963","authenticated-orcid":false,"given":"Ikram","family":"Guizani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2974-9157","authenticated-orcid":false,"given":"Emna","family":"Harigua-Souiai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,2,2]]},"reference":[{"key":"6676_CR1","unstructured":"World Health Organization (WHO). https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/leishmaniasis."},{"key":"6676_CR2","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1186\/s13071-022-05524-z","volume":"15","author":"I Gow","year":"2022","unstructured":"Gow, I., Smith, N. C., Stark, D. & Ellis, J. Laboratory diagnostics for human Leishmania infections: a polymerase chain reaction-focussed review of detection and identification methods. Parasit Vectors 15, 412 (2022).","journal-title":"Parasit Vectors"},{"key":"6676_CR3","doi-asserted-by":"publisher","first-page":"1029","DOI":"10.1042\/BST20180391","volume":"47","author":"L Von Chamier","year":"2019","unstructured":"Von Chamier, L., Laine, R. F. & Henriques, R. Artificial intelligence for microscopy: what you should know. Biochemical Society Transactions 47, 1029\u20131040 (2019).","journal-title":"Biochemical Society Transactions"},{"key":"6676_CR4","doi-asserted-by":"publisher","DOI":"10.1186\/s12880-024-01333-1","volume":"24","author":"E Tekle","year":"2024","unstructured":"Tekle, E. et al. DeepLeish: a deep learning based support system for the detection of Leishmaniasis parasite from Giemsa-stained microscope images. BMC Med Imaging 24, 152 (2024).","journal-title":"BMC Med Imaging"},{"key":"6676_CR5","doi-asserted-by":"publisher","DOI":"10.1186\/s12879-024-09428-4","volume":"24","author":"A Sadeghi","year":"2024","unstructured":"Sadeghi, A. et al. A deep learning-based model for detecting Leishmania amastigotes in microscopic slides: a new approach to telemedicine. BMC Infect Dis 24, 551 (2024).","journal-title":"BMC Infect Dis"},{"key":"6676_CR6","doi-asserted-by":"publisher","first-page":"110313","DOI":"10.1016\/j.compbiomed.2025.110313","volume":"192","author":"S Gadri","year":"2025","unstructured":"Gadri, S., Bounab, S., Benazi, N. & Zerouak, F. A new diagnostic method and tool for cutaneous leishmaniasis based on artificial intelligence techniques. Computers in Biology and Medicine 192, 110313 (2025).","journal-title":"Computers in Biology and Medicine"},{"key":"6676_CR7","doi-asserted-by":"publisher","unstructured":"G\u00f3rriz, M. et al. Leishmaniasis Parasite Segmentation and Classification Using Deep Learning. in Articulated Motion and Deformable Objects (eds Perales, F. J. & Kittler, J.) 53\u201362, https:\/\/doi.org\/10.1007\/978-3-319-94544-6_6 (Springer International Publishing, Cham, 2018).","DOI":"10.1007\/978-3-319-94544-6_6"},{"key":"6676_CR8","doi-asserted-by":"publisher","first-page":"8180","DOI":"10.3390\/s24248180","volume":"24","author":"M Contreras-Ram\u00edrez","year":"2024","unstructured":"Contreras-Ram\u00edrez, M., Sora-Cardenas, J., Colorado-Salamanca, C., Ovalle-Bracho, C. & Su\u00e1rez, D. R. Enhanced Detection of Leishmania Parasites in Microscopic Images Using Machine Learning Models. Sensors (Basel) 24, 8180 (2024).","journal-title":"Sensors (Basel)"},{"key":"6676_CR9","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1109\/TLA.2023.10015224","volume":"21","author":"C Gon\u00e7alves","year":"2023","unstructured":"Gon\u00e7alves, C. et al. Computer Vision in Automatic Visceral Leishmaniasis Diagnosis: a Survey. IEEE Latin America Transactions 21, 310\u2013319 (2023).","journal-title":"IEEE Latin America Transactions"},{"key":"6676_CR10","doi-asserted-by":"crossref","unstructured":"Portuondo-Mallet, L. M. de la C., Mollineda-Diogo, N., Orozco-Morales, R. & Lorenzo-Ginori, J. V. Detection and counting of Leishmania intracellular parasites in microscopy images. Front. Med. Technol. 6 (2024).","DOI":"10.3389\/fmedt.2024.1360280"},{"key":"6676_CR11","unstructured":"Tryp: a dataset of microscopy images of unstained thick blood smears for trypanosome detection | Scientific Data. https:\/\/www.nature.com\/articles\/s41597-023-02608-y."},{"key":"6676_CR12","doi-asserted-by":"publisher","DOI":"10.1186\/s12879-020-05453-1","volume":"20","author":"H Yu","year":"2020","unstructured":"Yu, H. et al. Malaria Screener: a smartphone application for automated malaria screening. 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Identification of novel leishmanicidal molecules by virtual and biochemical screenings targeting Leishmania eukaryotic translation initiation factor 4A. PLoS Negl Trop Dis 12, e0006160 (2018).","journal-title":"PLoS Negl Trop Dis"},{"key":"6676_CR19","doi-asserted-by":"publisher","first-page":"1403589","DOI":"10.3389\/fcimb.2024.1403589","volume":"14","author":"R Oualha","year":"2024","unstructured":"Oualha, R., Abdelkrim, Y. Z., Guizani, I. & Harigua-Souiai, E. Approved drugs successfully repurposed against Leishmania based on machine learning predictions. Front. Cell. Infect. Microbiol. 14, 1403589 (2024).","journal-title":"Front. Cell. Infect. Microbiol."},{"key":"6676_CR20","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.molbiopara.2018.10.001","volume":"226","author":"YZ Abdelkrim","year":"2018","unstructured":"Abdelkrim, Y. Z. et al. The steroid derivative 6-aminocholestanol inhibits the DEAD-box helicase eIF4A (LieIF4A) from the Trypanosomatid parasite Leishmania by perturbing the RNA and ATP binding sites. Mol Biochem Parasitol 226, 9\u201319 (2018).","journal-title":"Mol Biochem Parasitol"},{"key":"6676_CR21","doi-asserted-by":"publisher","first-page":"5890","DOI":"10.3390\/molecules27185890","volume":"27","author":"YZ Abdelkrim","year":"2022","unstructured":"Abdelkrim, Y. Z. et al. Enzymatic and Molecular Characterization of Anti-Leishmania Molecules That Differently Target Leishmania and Mammalian eIF4A Proteins, LieIF4A and eIF4AMus. Molecules 27, 5890 (2022).","journal-title":"Molecules"},{"key":"6676_CR22","doi-asserted-by":"publisher","first-page":"153","DOI":"10.3389\/fcimb.2019.00153","volume":"9","author":"R Oualha","year":"2019","unstructured":"Oualha, R. et al. Infection of Human Neutrophils With Leishmania infantum or Leishmania major Strains Triggers Activation and Differential Cytokines Release. 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