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https://hdl.handle.net/2440/82026
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dc.contributor.author | Hoffman, M. | - |
dc.contributor.author | Mielens, J. | - |
dc.contributor.author | Omari, T. | - |
dc.contributor.author | Rommel, N. | - |
dc.contributor.author | Jiang, J. | - |
dc.contributor.author | McCulloch, T. | - |
dc.date.issued | 2013 | - |
dc.identifier.citation | The Laryngoscope, 2013; 123(3):713-720 | - |
dc.identifier.issn | 0023-852X | - |
dc.identifier.issn | 0023-852X | - |
dc.identifier.uri | http://hdl.handle.net/2440/82026 | - |
dc.description.abstract | <h4>Objectives/hypothesis</h4>To use classification algorithms to classify swallows as safe, penetration, or aspiration based on measurements obtained from pharyngeal high-resolution manometry (HRM) with impedance.<h4>Study design</h4>Case series evaluating new method of data analysis.<h4>Methods</h4>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.<h4>Results</h4>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.<h4>Conclusions</h4>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. | - |
dc.description.statementofresponsibility | Matthew R. Hoffman, Jason D. Mielens, Taher I. Omari, Nathalie Rommel, Jack J. Jiang, Timothy M. McCulloch | - |
dc.language.iso | en | - |
dc.publisher | Lippincott Williams & Wilkins | - |
dc.rights | Copyright © 2012 The American Laryngological, Rhinological, and Otological Society, Inc. | - |
dc.source.uri | http://dx.doi.org/10.1002/lary.23655 | - |
dc.subject | Artificial neural network | - |
dc.subject | classification model | - |
dc.subject | high-resolution manometry | - |
dc.subject | impedance | - |
dc.subject | aspiration | - |
dc.subject | dysphagia | - |
dc.subject | Level of Evidence: 4 | - |
dc.title | Artificial neural network classification of pharyngeal high-resolution manometry with impedance data | - |
dc.type | Journal article | - |
dc.identifier.doi | 10.1002/lary.23655 | - |
dc.relation.grant | http://purl.org/au-research/grants/nhmrc/1009344 | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Omari, T. [0000-0001-5108-7378] | - |
Appears in Collections: | Aurora harvest Paediatrics publications |
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