{
  "abstract": "Early and reliable screening for oropharyngeal dysphagia symptoms in at-risk populations is a crucial first stage in effective management. This study aims to propose a machine learning model that can predict whether individuals are at risk of having confirmed dysphagia based on diagnostic accuracy. This was an exploratory cross-sectional diagnostic accuracy study. Adults and older adults were screened and assessed, and a videofluoroscopy swallowing study was performed to confirm dysphagia in participants with complaints about swallowing and signs or symptoms identified in the clinical assessment by a speech therapist. Eight algorithms were tested based on a theoretical model to evaluate their diagnostic accuracy: Naive Bayes, K-Nearest Neighbors, Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, Multilayer Perceptron, and Convolutional Neural Networks. The Pandas library in Python was used for data visualization and manipulation. In total, 465 participants were included, of whom 153 had dysphagia. A screening with 15 items related to personal, health, and oral health characteristics is proposed. The final four best-performing models were Naive Bayes (accuracy 0.76, sensitivity 0.84), Logistic Regression (accuracy 0.79, sensitivity 0.82), Support Vector Machine (accuracy 0.78, sensitivity 0.79), and Convolutional Neural Network (accuracy 0.78, sensitivity 0.74). The use of artificial intelligence based on different machine-learning approaches is promising for dysphagia screening, demonstrating adequate diagnostic accuracy. Future studies should be conducted to externally validate the proposed screening models. Author summary: Early identification of oropharyngeal dysphagia is critical for clinical management. This study developed and tested machine learning models to predict dysphagia using clinical screening data validated by videofluoroscopy (n = 465). A 15-item screening model achieved accuracies of 0.76–0.79 and sensitivities of 0.74–0.84 across top-performing algorithms. Artificial intelligence shows promise for dysphagia screening, warranting further external validation.",
  "abstract_shown": true,
  "affiliations": [
    "Graduate Program in Dentistry, Faculty of Dentistry, Federal University of Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil",
    "Graduate Program in Information Technology and Healthcare Management, Federal University of Health Sciences of Porto Alegre, Porto Alegre, Rio Grande do Sul, Brazil",
    "Department of Speech-Language Pathology, Federal University of Health Sciences of Porto Alegre, Porto Alegre, Rio Grande do Sul, Brazil",
    "Department of Exact and Applied Social Sciences, Federal University of Health Sciences of Porto Alegre, Porto Alegre, Rio Grande do Sul, Brazil",
    "Department of Preventive and Social Dentistry, Faculty of Dentistry, Federal University of Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil",
    "Department of Epidemiology and Health Promotion, College of Dentistry, New York University, New York, New York, United States of America"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Rafaela Soares Rech"
    },
    {
      "affs": [
        1
      ],
      "name": "Carla Diniz Lopes Becker"
    },
    {
      "affs": [
        2
      ],
      "name": "Dieine Estela Bernieri Schiavon"
    },
    {
      "affs": [
        3
      ],
      "name": "Danielli Pires Vieira"
    },
    {
      "affs": [
        4
      ],
      "name": "Juliana Balbinot Hilgert"
    },
    {
      "affs": [
        5
      ],
      "name": "Fernando Neves Hugo"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cb3056x4a8erj.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pdig.0001755",
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  "id": "cb3056x4a8erj",
  "issns": [
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  "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",
      "3616|Dysphagia Assessment and Management"
    ],
    [
      "plos",
      "/Biology and life sciences/Physiology/Physiological processes/Ingestion/Swallowing"
    ],
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      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning"
    ],
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      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning/Support vector machines"
    ],
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      "plos",
      "/Medicine and health sciences/Epidemiology/Medical risk factors"
    ],
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      "/Medicine and health sciences/Medical conditions/Cerebrovascular diseases/Stroke"
    ],
    [
      "plos",
      "/Medicine and health sciences/Neurology/Cerebrovascular diseases/Stroke"
    ],
    [
      "plos",
      "/Medicine and health sciences/Otorhinolaryngology/Laryngology/Dysphagia"
    ],
    [
      "plos",
      "/Medicine and health sciences/Otorhinolaryngology/Laryngology/Speech-language pathology"
    ],
    [
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      "/Medicine and health sciences/Vascular medicine/Stroke"
    ],
    [
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      "/Physical sciences/Mathematics/Discrete mathematics/Combinatorics/Permutation"
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  "pdf_url": "https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0001755&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-29",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/cb3056x4a8erj.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:37Z",
      "native_id": "10.1371/journal.pdig.0001755",
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      "url": "https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001755"
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  "subjects": [
    {
      "field": "medicine",
      "origin": "plos:epidemiology",
      "subfield": "epidemiology-public-health"
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    {
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      "origin": "plos:neurology",
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      "field": "medicine",
      "origin": "plos:physiology",
      "subfield": "physiology"
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  ],
  "title": "Artificial intelligence for dysphagia screening: A machine learning approach",
  "title_text": "Artificial intelligence for dysphagia screening: A machine learning approach",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:37Z",
  "url": "https://research.draconitic.com/paper/cb3056x4a8erj",
  "venue": "PLOS Digital Health",
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}