{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T19:50:44Z","timestamp":1792785044953,"version":"4.5.0"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T00:00:00Z","timestamp":1790553600000},"content-version":"vor","delay-in-days":90,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"UK Department for Environment, Food and Rural Affairs (DEFRA) and the devolved Scottish and Welsh administrations","award":["SV3500"],"award-info":[{"award-number":["SV3500"]}],"id":[{"id":"https:\/\/ror.org\/00tnppw48","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Rabies remains a major neglected disease, causing tens of thousands of deaths annually in endemic regions. Cell culture-based assays are central to rabies diagnostics and antibody quantification, yet interpretation relies on manual fluorescence image reading, which is labour-intensive, subjective, and difficult to scale. Here, we present, what is to our knowledge, the first AI-based workflow for detecting rabies virus infection in BHK-21 cell cultures and quantifying antibody titres. Three state-of-the-art architectures\u2013ResNet, Swin Transformer V2, and MambaOut\u2013were trained on a large dataset of 2344 fluorescence images (36,822 annotated patches) capturing biological and technical variability. Evaluation at patch, image, and well levels showed MambaOut consistently outperformed other models, achieving 0.996 accuracy, 0.993 F1-score, and 0.999 AUC-ROC at the well level. To assess applicability, models were tested on unseen images from the WHO\/WOAH-recommended fluorescent antibody virus neutralisation assay. MambaOut demonstrated complete agreement with ground truth in predicting antibody concentrations, confirming workflow reliability for vaccine assessment and pet travel schemes. These results establish a foundation for AI-assisted rabies diagnostics and open opportunities for applying deep learning to other cell culture-based viral assays. AI-driven automation for high-throughput image analysis offers a scalable and time-efficient solution for diagnostic laboratories, particularly in endemic countries.<\/jats:p>","DOI":"10.1038\/s41598-026-57955-8","type":"journal-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T10:14:54Z","timestamp":1782814494000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep learning enables rabies virus detection and antibody quantification in cell culture"],"prefix":"10.1038","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6077-7706","authenticated-orcid":false,"given":"Joan","family":"Amaya-Cuesta","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Syed Sameed","family":"Husain","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guanghui","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lorraine Marie","family":"McElhinney","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miroslaw","family":"Bober","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,30]]},"reference":[{"issue":"4","key":"57955_CR1","doi-asserted-by":"publisher","first-page":"0003709","DOI":"10.1371\/journal.pntd.0003709","volume":"9","author":"K Hampson","year":"2015","unstructured":"Hampson, K. et al. Estimating the global burden of endemic canine rabies. PLoS Negl. Trop. Dis. 9(4), 0003709 (2015).","journal-title":"PLoS Negl. Trop. Dis."},{"issue":"12","key":"57955_CR2","doi-asserted-by":"publisher","first-page":"0006105","DOI":"10.1371\/journal.pntd.0006105","volume":"11","author":"JA Benavides","year":"2017","unstructured":"Benavides, J. A., Rojas Paniagua, E., Hampson, K., Valderrama, W. & Streicker, D. G. Quantifying the burden of vampire bat rabies in peruvian livestock. PLoS Negl. Trop. Dis. 11(12), 0006105 (2017).","journal-title":"PLoS Negl. Trop. Dis."},{"key":"57955_CR3","doi-asserted-by":"crossref","unstructured":"Fooks, A. R. et al. Rabies. Nat. Rev. Dis. Prim. 3, 1 (2017)","DOI":"10.1038\/nrdp.2017.91"},{"issue":"5","key":"57955_CR4","first-page":"289","volume":"1","author":"CE Rupprecht","year":"2018","unstructured":"Rupprecht, C. E., Fooks, A. R. & Abela-Ridder, B. Laboratory techniques in rabies. World Health Organ. 1(5), 289 (2018).","journal-title":"World Health Organ."},{"key":"57955_CR5","doi-asserted-by":"crossref","unstructured":"Andriasyan, V. et al. Microscopy deep learning predicts virus infections and reveals mechanics of lytic-infected cells. Iscience 24, 6 (2021).","DOI":"10.1016\/j.isci.2021.102543"},{"issue":"1","key":"57955_CR6","doi-asserted-by":"publisher","first-page":"5112","DOI":"10.1038\/s41467-024-49444-1","volume":"15","author":"A Petkidis","year":"2024","unstructured":"Petkidis, A., Andriasyan, V., Murer, L., Volle, R. & Greber, U. F. A versatile automated pipeline for quantifying virus infectivity by label-free light microscopy and artificial intelligence. Nat. Commun. 15(1), 5112 (2024).","journal-title":"Nat. Commun."},{"issue":"1","key":"57955_CR7","doi-asserted-by":"publisher","first-page":"5904","DOI":"10.1038\/s41598-025-89639-0","volume":"15","author":"Z Akkutay-Yoldar","year":"2025","unstructured":"Akkutay-Yoldar, Z. et al. A web-based artificial intelligence system for label-free virus classification and detection of cytopathic effects. Sci. Rep. 15(1), 5904 (2025).","journal-title":"Sci. Rep."},{"issue":"1","key":"57955_CR8","doi-asserted-by":"publisher","first-page":"20937","DOI":"10.1038\/s41598-020-77170-3","volume":"10","author":"RK Long","year":"2020","unstructured":"Long, R. K. et al. Super resolution microscopy and deep learning identify zika virus reorganization of the endoplasmic reticulum. Sci. Rep. 10(1), 20937 (2020).","journal-title":"Sci. Rep."},{"key":"57955_CR9","doi-asserted-by":"publisher","first-page":"109976","DOI":"10.1016\/j.compbiomed.2025.109976","volume":"190","author":"YK Won","year":"2025","unstructured":"Won, Y. K., Kim, C. H., Jeon, J., Cha, J. & Lim, D. H. Deep learning by vision transformer to classify bacterial and fungal keratitis using different types of anterior segment images. Comput. Biol. Med. 190, 109976 (2025).","journal-title":"Comput. Biol. 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Zero by 30: the global strategic plan to end human deaths from dog-mediated rabies by 2030. In Zero by 30: the Global Strategic Plan to End Human Deaths from Dog-mediated Rabies by 2030 (2018)."},{"issue":"5","key":"57955_CR25","first-page":"535","volume":"48","author":"JS Smith","year":"1973","unstructured":"Smith, J. S., Yager, P. A. & Baer, G. M. A rapid reproducible test for determining rabies neutralizing antibody. Bull. World Health Organ. 48(5), 535 (1973).","journal-title":"Bull. World Health Organ."},{"key":"57955_CR26","doi-asserted-by":"crossref","unstructured":"Dacheux, L. & Bourhy, H. Chapter three - virus isolation in cell culture: The rabies tissue culture infection test. In Current Laboratory Techniques in Rabies Diagnosis 25\u201331 (Academic Press, Elsevier, 2015).","DOI":"10.1016\/B978-0-12-801919-1.00003-8"},{"key":"57955_CR27","doi-asserted-by":"crossref","unstructured":"Chapter eight - direct fluorescent antibody test for rabies diagnosis. 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No animals were used or harmed during the course of this research.","order":2,"name":"Ethics","label":"Ethical approval","group":{"name":"EthicsHeading","label":"Ethics"}}],"article-number":"29998"}}