{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T05:18:28Z","timestamp":1787894308037,"version":"build-2784847793"},"reference-count":38,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T00:00:00Z","timestamp":1787875200000},"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":"\n Introduction<\/jats:title>\n Accurate and timely identification of microorganisms is critical for guiding clinical decision-making, ensuring biosafety, and supporting microbiological research. Standard morphological assessments often require significant manual effort and specialized expertise, underscoring the need for automated, high-throughput diagnostic tools. This study presents an automated species-level bacterial classification pipeline that couples deep learning analysis of Gram-stained microscopy images with a clinical decision-support framework fueled by a curated microbiological metadata repository.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n A convolutional neural network (CNN) was trained on 2,034 brightfield microscopy images representing 33 bacterial species of clinical and industrial relevance across diverse taxa and imaging conditions. Preprocessing protocols included intensity normalization, spatial resizing, and on-the-fly data augmentation to ensure generalization. The preprocessed inputs were evaluated using a sequential CNN architecture optimized for multiscale feature extraction. The pipeline subsequently linked visual model predictions to an integrated repository containing eight laboratory-relevant metadata attributes per species, including Gram status, cellular morphology, oxygen requirement, biosafety level, and pathogenicity profiles.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n On an independent test set, the classification model demonstrated balanced diagnostic capability, achieving an accuracy of 0.84, a weighted F1-score of 0.84, and a Matthews correlation coefficient of 0.84 across all target classes. Morphologically distinctive species, including\n Actinomyces israelii, Candida albicans<\/jats:italic>\n , and\n Neisseria gonorrhoeae<\/jats:italic>\n , were classified with perfect precision and recall (1.00). Conversely, species exhibiting high intra-genus morphological overlap, particularly within the\n Lactobacillus<\/jats:italic>\n genus, demonstrated comparatively lower classification metrics due to shared phenotypic traits under light microscopy.\n <\/jats:p>\n <\/jats:sec>\n \n Discussion<\/jats:title>\n Coupling deep learning classification with contextual microbiological metadata yields standardized, interpretable diagnostic reports that bridge the gap between pure image recognition and actionable clinical intelligence. While morphological convergence remains a limitation for certain closely related taxa, the integration of structured metadata offers a reliable decision-support mechanism for laboratory and educational environments. This automated framework establishes a foundation for multimodal diagnostic pipelines that can incorporate biochemical assays, spectroscopic data, and expanded clinical cohorts in prospective validation studies.<\/jats:p>\n <\/jats:sec>","DOI":"10.3389\/fcimb.2026.1811365","type":"journal-article","created":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T04:34:36Z","timestamp":1787891676000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Automated microbial recognition from gram-stained micrographs via CNN and metadata decision support"],"prefix":"10.3389","volume":"16","author":[{"given":"Azime","family":"Erarslan","sequence":"first","affiliation":[{"name":"Department of Bioengineering, Faculty of Chemical and Metallurgical Engineering, Y\u0131ld\u0131z Technical University","place":["\u0130stanbul, T\u00fcrkiye"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fatih","family":"Ciftci","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Department of Biomedical Engineering, Fatih Sultan Mehmet Vak\u0131f University","place":["Istanbul, T\u00fcrkiye"]},{"name":"Biomedical Electronic Design Application and Research Center (BETAM), Fatih Sultan Mehmet Vak\u0131f University","place":["Istanbul, T\u00fcrkiye"]},{"name":"BioriginAI Research Group, Department of Biomedical Engineering, Fatih Sultan Mehmet Vak\u0131f University","place":["Istanbul, T\u00fcrkiye"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Javad","family":"Rahebi","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Istanbul Topkapi University","place":["Istanbul, T\u00fcrkiye"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,28]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1080\/15257770.2019.1645851","article-title":"Bacterial classification with convolutional neural networks based on different data reduction layers","volume":"39","author":"Abd","year":"2020","journal-title":"Nucleosides Nucleotides Nucleic Acids"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.3390\/laboratories2010008","article-title":"Medical laboratories in healthcare delivery: A systematic review of their roles and impact","volume":"2","author":"Adekoya","year":"2025","journal-title":"Laboratories"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.61476","article-title":"A comprehensive review of innovative paradigms in microbial detection and antimicrobial resistance: beyond traditional cultural methods","volume":"16","author":"Ahmad","year":"2024","journal-title":"Cureus"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.3390\/jimaging11020059","article-title":"Applications of artificial intelligence, deep learning, and machine learning to support the analysis of microscopic images of cells and tissues","volume":"11","author":"Ali","year":"2025","journal-title":"J. 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