Artificial neural network classification of pharyngeal high-resolution manometry with impedance data.
Objectives/hypothesis: To use classification algorithms to classify swallows as safe, penetration, or aspiration based on measurements obtained from pharyngeal high-resolution manometry (HRM) with impedance.Study Design: Case series evaluating new method of data analysis.Methods: Multilayer perceptr...
| Publicado en: | Laryngoscope Vol. 123; no. 3; pp. 713 - 721 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Wiley-Blackwell
Mar2013
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104239687&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104239687 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 0023852X 1GR jtl: Laryngoscope issn: 0023852X maglogo: Y pubinfo: dt: Mar2013 vid: 123 iid: 3 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 104239687 NLM23070810 2012019985 10.1002/lary.23655 NLM23070810 PMC3648989 104239687 ppf: 713 ppct: 8 formats: tig: atl: Artificial neural network classification of pharyngeal high-resolution manometry with impedance data. aug: au: Hoffman, Matthew R Mielens, Jason D Omari, Taher I Rommel, Nathalie Jiang, Jack J McCulloch, Timothy M affil: Department of Surgery, Division of Otolaryngology-Head and Neck Surgery, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, U.S.A. sug: subj: Algorithms Deglutition Physiology Manometry Classification Neural Networks (Computer) Adult Aged Aged, 80 and Over Female Male Middle Age ROC Curve Funding Source Human Adult: 19-44 years Aged: 65+ years Aged, 80 & over Middle Aged: 45-64 years Female Male ab: Objectives/hypothesis: To use classification algorithms to classify swallows as safe, penetration, or aspiration based on measurements obtained from pharyngeal high-resolution manometry (HRM) with impedance.Study Design: Case series evaluating new method of data analysis.Methods: Multilayer perceptron, an artificial neural network (ANN), was evaluated for its ability to classify swallows as safe, penetration, or aspiration. Data were collected from 25 disordered subjects swallowing 5- or 10-mL boluses. Following extraction of relevant parameters, a subset of the data was used to train the models, and the remaining swallows were then independently classified by the ANN.Results: A classification accuracy of 89.4 ± 2.4% was achieved when including all parameters. Including only manometry-related parameters yielded a classification accuracy of 85.0 ± 6.0%, whereas including only impedance-related parameters yielded a classification accuracy of 76.0 ± 4.9%. Receiver operating characteristic analysis yielded areas under the curve of 0.8912 for safe, 0.8187 for aspiration, and 0.8014 for penetration.Conclusions: Classification models show high accuracy in classifying swallows from dysphagic patients as safe or unsafe. HRM-impedance with ANN represents one method that could be used clinically to screen for patients at risk for penetration or aspiration. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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