{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T15:13:44Z","timestamp":1786115624235,"version":"build-2736575974"},"reference-count":23,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T00:00:00Z","timestamp":1785974400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cell. Infect. Microbiol."],"abstract":"<jats:p>\n                    Quantification of intracellular\n                    <jats:italic>Trypanosoma cruzi<\/jats:italic>\n                    infection remains a central, yet methodologically challenging step in Chagas disease research and early-stage drug discovery. Current approaches largely rely on manual microscopy-based counting or on genetically modified parasites, both of which present limitations in scalability, reproducibility, or accessibility. Here, we developed and validated a neural network (NN)-based pipeline for the automated quantification of infection rates and parasite burden in mammalian cells using images stained exclusively with DNA-binding fluorescent dyes. Two independently refined deep-learning models were fine-tuned to segment host cell nuclei and intracellular amastigotes, respectively, and subsequently integrated into a unified algorithm that assigns each parasite to its nearest host cell. The pipeline was evaluated using confocal images from six mammalian cell lines infected with two\n                    <jats:italic>T. cruzi<\/jats:italic>\n                    strains and compared against blinded manual quantification. Automated detection of both host nuclei and parasites showed high concordance with manual counts, with median deviations around 5% and similar distributions of parasite burden per cell. In contrast to morphology-based image analysis methods, our NN-based approach demonstrated improved robustness across diverse cell types and staining conditions, reduced parameter dependency, and independent segmentation of host and parasite objects, minimizing error propagation. Although minor biases in parasite-to-cell assignment were observed, the automated workflow accurately quantified the percentage of infected cells and the number of amastigotes per cell, demonstrating high detection performance and strong agreement with manual quantification as assessed by precision\/recall metrics and Bland\u2013Altman analysis. This accessible and scalable AI-assisted workflow provides a reproducible alternative to manual quantification and represents a methodological advance for standardized phenotypic screening of intracellular\n                    <jats:italic>T. cruzi<\/jats:italic>\n                    , supporting more robust and harmonized drug discovery efforts in Chagas disease.\n                  <\/jats:p>","DOI":"10.3389\/fcimb.2026.1885877","type":"journal-article","created":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T04:24:25Z","timestamp":1785990265000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["NN-assisted image analysis for quantifying intracellular Trypanosoma cruzi infection"],"prefix":"10.3389","volume":"16","author":[{"given":"Joaqu\u00edn","family":"Iolster","sequence":"first","affiliation":[{"name":"Instituto de Investigaciones en Ingenier\u00eda Gen\u00e9tica y Biolog\u00eda Molecular \u201cDr. H\u00e9ctor N. Torres\u201d (INGEBI), Consejo Nacional de Investigaciones Cient\u00edficas y T\u00e9cnicas (CONICET)","place":["Buenos Aires, Argentina"]},{"name":"Departamento de Fisiolog\u00eda, Biolog\u00eda Molecular y Celular, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires","place":["Buenos Aires, Argentina"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Salom\u00e9 Catalina","family":"Vilchez Larrea","sequence":"additional","affiliation":[{"name":"Instituto de Investigaciones en Ingenier\u00eda Gen\u00e9tica y Biolog\u00eda Molecular \u201cDr. H\u00e9ctor N. Torres\u201d (INGEBI), Consejo Nacional de Investigaciones Cient\u00edficas y T\u00e9cnicas (CONICET)","place":["Buenos Aires, Argentina"]},{"name":"Departamento de Fisiolog\u00eda, Biolog\u00eda Molecular y Celular, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires","place":["Buenos Aires, Argentina"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guillermo Daniel","family":"Alonso","sequence":"additional","affiliation":[{"name":"Instituto de Investigaciones en Ingenier\u00eda Gen\u00e9tica y Biolog\u00eda Molecular \u201cDr. H\u00e9ctor N. Torres\u201d (INGEBI), Consejo Nacional de Investigaciones Cient\u00edficas y T\u00e9cnicas (CONICET)","place":["Buenos Aires, Argentina"]},{"name":"Departamento de Fisiolog\u00eda, Biolog\u00eda Molecular y Celular, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires","place":["Buenos Aires, Argentina"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,6]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"63210","DOI":"10.3791\/63210","article-title":"In vitro drug screening against all life cycle stages of Trypanosoma cruzi using parasites expressing \u03b2-galactosidase","author":"Alonso","year":"2021","journal-title":"J. Vis. Exp."},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pntd.0003259","article-title":"High throughput screening for anti\u2013Trypanosoma cruzi drug discovery","volume":"8","author":"Alonso-Padilla","year":"2014","journal-title":"PloS Negl.Trop. Dis."},{"key":"B3","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1038\/s41579-019-0238-x","article-title":"Protozoan persister-like cells and drug treatment failure","volume":"17","author":"Barrett","year":"2019","journal-title":"Nat. Rev. Microbiol."},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.3390\/idr18010011","article-title":"Diagnostic accuracy of utilizing artificial intelligence for malaria diagnostic: A systematic review and meta-analysis","volume":"18","author":"Faratisha","year":"2026","journal-title":"Infect. Dis. 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Dis."},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.3390\/biom12020240","article-title":"AutoCellANLS: An automated analysis system for mycobacteria-infected cells based on unstained micrograph","volume":"12","author":"Zhuang","year":"2022","journal-title":"Biomolecules"}],"container-title":["Frontiers in Cellular and Infection Microbiology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fcimb.2026.1885877\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T04:24:26Z","timestamp":1785990266000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fcimb.2026.1885877\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,6]]},"references-count":23,"alternative-id":["10.3389\/fcimb.2026.1885877"],"URL":"https:\/\/doi.org\/10.3389\/fcimb.2026.1885877","relation":{},"ISSN":["2235-2988"],"issn-type":[{"value":"2235-2988","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,6]]},"article-number":"1885877"}}