期刊
LARYNGOSCOPE
卷 123, 期 3, 页码 713-720出版社
WILEY-BLACKWELL
DOI: 10.1002/lary.23655
关键词
Artificial neural network; classification model; high-resolution manometry; impedance; aspiration; dysphagia
资金
- National Institutes of Health from the National Institute on Deafness and other Communicative Disorders [R21 DC011130A, T32 DC009401]
- NHMRC [1009344]
- Thrasher Research Fund, Salt Lake City, Utah
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. Laryngoscope, 2013
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