{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T18:00:12Z","timestamp":1790704812865,"version":"4.1.0"},"reference-count":40,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T00:00:00Z","timestamp":1790640000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004263","name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado do Rio Grande do Sul","doi-asserted-by":"publisher","award":["21\/2551-0000628"],"award-info":[{"award-number":["21\/2551-0000628"]}],"id":[{"id":"10.13039\/501100004263","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["CNPq\/MCTI\/FNDCT Call No. 18\/2021 - Track A - Emerging Groups"],"award-info":[{"award-number":["CNPq\/MCTI\/FNDCT Call No. 18\/2021 - Track A - Emerging Groups"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004263","name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado do Rio Grande do Sul","doi-asserted-by":"publisher","award":["23\/2551-0000133-0"],"award-info":[{"award-number":["23\/2551-0000133-0"]}],"id":[{"id":"10.13039\/501100004263","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosdigitalhealth.org"],"crossmark-restriction":false},"short-container-title":["PLOS Digit Health"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.1371\/journal.pdig.0001755","type":"journal-article","created":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T17:37:42Z","timestamp":1790703462000},"page":"e0001755","update-policy":"https:\/\/doi.org\/10.1371\/journal.pdig.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence for dysphagia screening: A machine learning approach"],"prefix":"10.1371","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3207-0180","authenticated-orcid":true,"given":"Rafaela Soares","family":"Rech","sequence":"first","affiliation":[{"name":"Federal University of Rio Grande do Sul","place":["Porto Alegre, Rio Grande do Sul, Brazil"],"department":["Graduate Program in Dentistry, Faculty of Dentistry"]},{"name":"Federal University of Health Sciences of Porto Alegre","place":["Porto Alegre, Rio Grande do Sul, Brazil"],"department":["Graduate Program in Information Technology and Healthcare Management"]},{"name":"Federal University of Health Sciences of Porto Alegre","place":["Porto Alegre, Rio Grande do Sul, Brazil"],"department":["Department of Speech-Language Pathology"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carla Diniz","family":"Lopes Becker","sequence":"additional","affiliation":[{"name":"Federal University of Health Sciences of Porto Alegre","place":["Porto Alegre, Rio Grande do Sul, Brazil"],"department":["Graduate Program in Information Technology and Healthcare Management"]},{"name":"Federal University of Health Sciences of Porto Alegre","place":["Porto Alegre, Rio Grande do Sul, Brazil"],"department":["Department of Exact and Applied Social Sciences"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8090-2386","authenticated-orcid":true,"given":"Dieine Estela","family":"Bernieri Schiavon","sequence":"additional","affiliation":[{"name":"Federal University of Rio Grande do Sul","place":["Porto Alegre, Rio Grande do Sul, Brazil"],"department":["Graduate Program in Dentistry, Faculty of Dentistry"]},{"name":"Federal University of Health Sciences of Porto Alegre","place":["Porto Alegre, Rio Grande do Sul, Brazil"],"department":["Graduate Program in Information Technology and Healthcare Management"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3690-6034","authenticated-orcid":true,"given":"Danielli Pires","family":"Vieira","sequence":"additional","affiliation":[{"name":"Federal University of Rio Grande do Sul","place":["Porto Alegre, Rio Grande do Sul, Brazil"],"department":["Graduate Program in Dentistry, Faculty of Dentistry"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juliana Balbinot","family":"Hilgert","sequence":"additional","affiliation":[{"name":"Federal University of Rio Grande do 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