{
  "abstract": "Background: Bacterial vaginosis (BV) is a vaginal dysbiosis associated with adverse reproductive and infectious outcomes. Current diagnostics identify BV after symptom onset. We evaluated whether artificial neural network (ANN) models of the vaginal microbiome could predict incident BV (iBV) up to 14 days before clinical diagnosis. Methods: ANNs were trained using 16S rRNA gene sequencing data from 1,201 longitudinal vaginal specimens collected from 58 women across two prospective cohorts. Models classified individual specimens as pre-iBV or healthy using the relative abundance of vaginal bacterial taxa. Model performance was assessed using a held-out participant-level test set, participant-level cross-validation, and external validation. SHAP analysis was used to identify taxa associated with model predictions. Results: On the held-out participant-level test set, the ANN achieved 93% accuracy (AUC = 0.97, sensitivity = 95%, specificity = 92%). Models using only five taxa (Lactobacillus crispatus, Gardnerella spp., L. iners, L. mulieris, and Megamonas spp.) maintained >91% accuracy, sensitivity, and specificity. SHAP analysis identified Lactobacillus spp. and Gardnerella spp. as the taxa most strongly associated with model predictions. Participant-level cross-validation produced lower and more variable performance (AUC = 0.826 ± 0.076; accuracy = 71.5% ± 8.2%), while external validation achieved approximately 80% balanced accuracy. Interpretation: Vaginal microbiome composition contains predictive information that can identify women at risk of iBV before clinical onset. Lower performance during participant-level cross-validation and external validation indicates that additional validation in larger, more diverse cohorts is required before clinical implementation. Author summary: Background",
  "abstract_shown": true,
  "affiliations": [
    "Department of Microbiology, Immunology, and Parasitology, Louisiana State University Health Sciences Center, New Orleans, Louisiana, United States of America",
    "Division of Infectious Diseases, Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, United States of America",
    "Division of Infectious Diseases, Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, United States of America",
    "Division of Laboratory Science, Department of Pathology, University of Alabama at Birmingham, Birmingham, Alabama, United States of America",
    "Division of Nephrology, Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, United States of America",
    "Department of Basic Medical Sciences, College of Medicine-Phoenix, University of Arizona, Phoenix, Arizona, United States of America",
    "Centre of Biological Engineering, Laboratory of Research in Biofilms Rosário Oliveira, Minho University, Braga, Portugal"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 16,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Jacob H. Elnaggar"
    },
    {
      "affs": [
        1
      ],
      "name": "John W. Lammons"
    },
    {
      "affs": [
        2
      ],
      "name": "Caleb M. Ardizzone"
    },
    {
      "affs": [
        3
      ],
      "name": "Kristal J. Aaron"
    },
    {
      "affs": [
        4
      ],
      "name": "Clayton Jacobs"
    },
    {
      "affs": [
        5
      ],
      "name": "Keonte J. Graves"
    },
    {
      "affs": [
        6
      ],
      "name": "Sheridan D. George"
    },
    {
      "name": "Megan Amerson-Brown"
    },
    {
      "name": "Meng Luo"
    },
    {
      "name": "Ashutosh Tamhane"
    },
    {
      "name": "Paweł Łaniewski"
    },
    {
      "name": "Alison J. Quayle"
    },
    {
      "name": "Melissa M. Herbst-Kralovetz"
    },
    {
      "name": "Nuno Cerca"
    },
    {
      "name": "Christina A. Muzny"
    },
    {
      "name": "Christopher M. Taylor"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dkx6nd63hhhf1.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pcbi.1014768",
  "fulltext_url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014768",
  "id": "dkx6nd63hhhf1",
  "issns": [
    "1553-7358"
  ],
  "landing_url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014768",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2404|Reproductive tract infections research"
    ],
    [
      "plos",
      "/Biology and life sciences/Computational biology/Computational neuroscience/Artificial neural networks"
    ],
    [
      "plos",
      "/Biology and life sciences/Genetics/Genomics/Microbial genomics/Microbiome"
    ],
    [
      "plos",
      "/Biology and life sciences/Microbiology/Medical microbiology/Microbiome"
    ],
    [
      "plos",
      "/Biology and life sciences/Microbiology/Microbial genomics/Microbiome"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Computational neuroscience/Artificial neural networks"
    ],
    [
      "plos",
      "/Biology and life sciences/Organisms/Bacteria/Gut bacteria/Lactobacillus"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Artificial neural networks"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning"
    ],
    [
      "plos",
      "/Medicine and health sciences/Epidemiology/Medical risk factors"
    ],
    [
      "plos",
      "/Medicine and health sciences/Medical conditions/Infectious diseases/Sexually transmitted diseases/Bacterial vaginosis"
    ],
    [
      "plos",
      "/Medicine and health sciences/Pathology and laboratory medicine/Pathogenesis"
    ],
    [
      "plos",
      "/Medicine and health sciences/Public and occupational health/Preventive medicine"
    ],
    [
      "plos",
      "/Medicine and health sciences/Urology/Genitourinary infections/Bacterial vaginosis"
    ]
  ],
  "openalex": "W7213315381",
  "pdf_url": "https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1014768&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-15",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/dkx6nd63hhhf1.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:49Z",
      "native_id": "10.1371/journal.pcbi.1014768",
      "source": "plos",
      "url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014768"
    }
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  "subjects": [
    {
      "field": "biology",
      "origin": "plos:computational biology",
      "subfield": "bioinformatics"
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    {
      "field": "biology",
      "origin": "plos:microbiology",
      "subfield": "microbiology"
    },
    {
      "field": "medicine",
      "origin": "plos:epidemiology",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Predicting bacterial vaginosis incidence using artificial neural networks",
  "title_text": "Predicting bacterial vaginosis incidence using artificial neural networks",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:49Z",
  "url": "https://research.draconitic.com/paper/dkx6nd63hhhf1",
  "venue": "PLOS Computational Biology",
  "versions": []
}