{
  "abstract": "Rapid bacterial identification could help guide antimicrobial susceptibility testing (AST), but image-based classification must generalise across bacterial strains. We developed SC-MAPS, a multichannel microscopy dataset comprising 916,435 single cells from 13 bacterial species, with independent strains reserved for evaluation. Combining phase-contrast and fluorescence information using deep learning achieved 65.4% single-cell species-classification accuracy on held-out strains. To assess whether imperfect species predictions retained useful information for downstream testing, we defined eight antimicrobial-resistance-informed AST-routing groups. Pooling species probabilities increased routing accuracy to 77.1%, rising to 91.1% when predictions from five cells were combined and 96.2% with 20 cells. Synthetic mixtures revealed limitations in minority-species recovery, while exploratory analysis of unseen organisms demonstrated the potential for out-of-distribution detection. SC-MAPS provides a framework for investigating transferable bacterial phenotypes and developing image-based identification and AST-routing strategies.",
  "abstract_shown": true,
  "affiliations": [
    "University of Oxford"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "A. Tiwari"
    },
    {
      "name": "P. Turner"
    },
    {
      "name": "C. Feehily"
    },
    {
      "affs": [
        0
      ],
      "name": "C. Nellaker"
    },
    {
      "name": "N. Stoesser"
    },
    {
      "name": "A. Kapanidis"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1530qdw56vq38.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.23.26366325",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.10.23.26366325v1.full",
  "id": "1530qdw56vq38",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.10.23.26366325v1",
  "language": null,
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "infectious diseases"
    ],
    [
      "openalex",
      "1308|Bacterial Identification and Susceptibility Testing"
    ]
  ],
  "openalex": "W7223694612",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.10.23.26366325v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1530qdw56vq38.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:47Z",
      "native_id": "10.64898/2026.10.23.26366325",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.10.23.26366325v2"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:infectious diseases",
      "subfield": "infectious-disease"
    }
  ],
  "title": "SC-MAPS: Image-based deep learning for bacterial identification and antibiotic-susceptibility testing",
  "title_text": "SC-MAPS: Image-based deep learning for bacterial identification and antibiotic-susceptibility testing",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:54Z",
  "url": "https://research.draconitic.com/paper/1530qdw56vq38",
  "venue": "medRxiv",
  "versions": []
}