{
  "abstract": "Primary ciliary dyskinesia (PCD) is a genetic condition causing dysmotile cilia and abnormal mucociliary clearance. Despite advances in understanding disease pathogenesis, diagnosis remains challenging. Here, we show that machine learning analysis of cilia-driven particle transport can objectively detect primary ciliary dyskinesia-related dysfunction. Using high-speed video microscopy, we captured fluorescent microsphere movement on cultured multiciliated cells from 14 individuals with primary ciliary dyskinesia and 10 healthy donors (602 videos). We developed CiliaTracks, an open-source computational framework that extracts measures of micropsphere movement, visualizes transport patterns, and classifies cilia-propelled particle transport. Quantitative and visual analyses reveal distinct motility profiles between PCD and non-primary ciliary dyskinesia and normal recordings. Classical machine learning models and a convolutional neural network classify recordings with 95-97% accuracy and differentiate affected from normal samples. Machine learning provides an objective, accurate framework for characterizing ciliary dysfunction, offering potential as a diagnostic tool for primary ciliary dyskinesia.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "name": "Nicholas Hadas"
    },
    {
      "name": "Huihui Xu"
    },
    {
      "name": "Shambhawee Neupane"
    },
    {
      "name": "Wang K. Twan"
    },
    {
      "name": "Ahmed Elgamal"
    },
    {
      "name": "Andrew Lunel",
      "orcid": "0000-0002-5582-6884"
    },
    {
      "name": "Moe R. Mahjoub",
      "orcid": "0000-0001-8129-7464"
    },
    {
      "name": "Amjad Horani",
      "orcid": "0000-0002-5352-1948"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8jnajt06waayp.bib",
  "cited_by": 0,
  "doi": "10.1038/s42003-026-11149-0",
  "fulltext_url": null,
  "id": "8jnajt06waayp",
  "issns": [
    "2399-3642"
  ],
  "landing_url": "https://www.nature.com/articles/s42003-026-11149-0",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2740|Cystic Fibrosis Research Advances"
    ]
  ],
  "openalex": "W7221553313",
  "pdf_url": "https://www.nature.com/articles/s42003-026-11149-0_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-12",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/8jnajt06waayp.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:50Z",
      "native_id": "10.1038/s42003-026-11149-0",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s42003-026-11149-0"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2740|Cystic Fibrosis Research Advances",
      "subfield": "general"
    }
  ],
  "title": "Machine learning analysis of cilia driven particle transport distinguishes primary ciliary dyskinesia from normal cilia",
  "title_text": "Machine learning analysis of cilia driven particle transport distinguishes primary ciliary dyskinesia from normal cilia",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:50Z",
  "url": "https://research.draconitic.com/paper/8jnajt06waayp",
  "venue": "Communications Biology",
  "versions": []
}