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|Title:||Artificial neural network classification of pharyngeal high-resolution manometry with impedance data|
|Citation:||Laryngoscope, 2013; 123(3):713-720|
|Publisher:||Lippincott Williams & Wilkins|
|Matthew R. Hoffman, Jason D. Mielens, Taher I. Omari, Nathalie Rommel, Jack J. Jiang, Timothy M. McCulloch|
|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.|
|Keywords:||Artificial neural network; classification model; high-resolution manometry; impedance; aspiration; dysphagia; Level of Evidence: 4|
|Rights:||Copyright © 2012 The American Laryngological, Rhinological, and Otological Society, Inc.|
|Appears in Collections:||Paediatrics publications|
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