{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T04:25:18Z","timestamp":1778646318066,"version":"3.51.4"},"reference-count":29,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2016,2,18]],"date-time":"2016-02-18T00:00:00Z","timestamp":1455753600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Neurogastroenterology Motil"],"published-print":{"date-parts":[[2016,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Pharyngeal pressure\u2010flow analysis (<jats:styled-content style=\"fixed-case\">PFA<\/jats:styled-content>) of high resolution impedance\u2010manometry (<jats:styled-content style=\"fixed-case\">HRIM<\/jats:styled-content>) with calculation of the swallow risk index (<jats:styled-content style=\"fixed-case\">SRI<\/jats:styled-content>) can quantify swallow dysfunction predisposing to aspiration. We explored the potential use of artificial neural networks (<jats:styled-content style=\"fixed-case\">ANN<\/jats:styled-content>) to model the relationship between <jats:styled-content style=\"fixed-case\">PFA<\/jats:styled-content> swallow metrics and aspiration and to predict swallow dysfunction.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Two hundred consecutive dysphagia patients referred for videofluoroscopy and <jats:styled-content style=\"fixed-case\">HRIM<\/jats:styled-content> were assessed. Presence of aspiration was scored and <jats:styled-content style=\"fixed-case\">PFA<\/jats:styled-content> software derived 13 metrics and the <jats:styled-content style=\"fixed-case\">SRI<\/jats:styled-content>. An <jats:styled-content style=\"fixed-case\">ANN<\/jats:styled-content> was created and optimized over training cycles to achieve optimal classification accuracy for matching inputs (<jats:styled-content style=\"fixed-case\">PFA<\/jats:styled-content> metrics) to output (presence of aspiration on videofluoroscopy). Application of the <jats:styled-content style=\"fixed-case\">ANN<\/jats:styled-content> returned a value between 0.00 and 1.00 reflecting the degree of swallow dysfunction.<\/jats:p><\/jats:sec><jats:sec><jats:title>Key Results<\/jats:title><jats:p>Twenty one patients were excluded due to insufficient number of swallows (&lt;4). Of 179, 58 aspirated and 27 had aspiration pneumonia history. The <jats:styled-content style=\"fixed-case\">SRI<\/jats:styled-content> was higher in aspirators (aspiration 24 [9, 41] <jats:italic>vs<\/jats:italic> no aspiration 7 [2, 18], <jats:italic>p<\/jats:italic>\u00a0&lt;\u00a00.001) and patients with pneumonia (pneumonia 27 [5, 42] <jats:italic>vs<\/jats:italic> no pneumonia 8 [3, 24], <jats:italic>p<\/jats:italic>\u00a0&lt;\u00a00.05). The <jats:styled-content style=\"fixed-case\">ANN<\/jats:styled-content> Predicted Risk was higher in aspirators (aspiration 0.57 [0.38, 0.82] <jats:italic>vs<\/jats:italic> no aspiration 0.13 [0.4, 0.25], <jats:italic>p<\/jats:italic>\u00a0&lt;\u00a00.001) and in patients with pneumonia (pneumonia 0.46 [0.18, 0.60] <jats:italic>vs<\/jats:italic> no pneumonia 0.18 [0.6, 0.49], <jats:italic>p<\/jats:italic>\u00a0&lt;\u00a00.01). Prognostic value of the <jats:styled-content style=\"fixed-case\">ANN<\/jats:styled-content> was superior to the <jats:styled-content style=\"fixed-case\">SRI<\/jats:styled-content>.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions &amp; Inferences<\/jats:title><jats:p>In a heterogeneous cohort of dysphagia patients, <jats:styled-content style=\"fixed-case\">PFA<\/jats:styled-content> with <jats:styled-content style=\"fixed-case\">ANN<\/jats:styled-content> modeling offers enhanced detection of clinically significant swallowing dysfunction, probably more accurately reflecting the complex interplay of swallow characteristics that causes aspiration.<\/jats:p><\/jats:sec>","DOI":"10.1111\/nmo.12730","type":"journal-article","created":{"date-parts":[[2016,2,20]],"date-time":"2016-02-20T07:51:43Z","timestamp":1455954703000},"page":"336-344","source":"Crossref","is-referenced-by-count":17,"title":["Objective prediction of pharyngeal swallow dysfunction in dysphagia through artificial neural network modeling"],"prefix":"10.1111","volume":"28","author":[{"given":"S.","family":"Kritas","sequence":"first","affiliation":[{"name":"Gastroenterology Unit Women's &amp; 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