Artificial neural network classification of pharyngeal high-resolution manometry with impedance data.
OBJECTIVE
To use classification algorithms to classify swallows as safe, penetration, or aspiration based on measurements obtained from pharyngeal high-resolution manometry (HRM) with impedance.


METHODS
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.
Department of Surgery, Division of Otolaryngology-Head and Neck Surgery, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA.
Matthew R
MR
Hoffman
Jason D
JD
Mielens
Taher I
TI
Omari
Nathalie
N
Rommel
Jack J
JJ
Jiang
Timothy M
TM
McCulloch
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D003679:Deglutition / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D008365:Manometry / Q000145:classification*; D008875:Middle Aged; D016571:Neural Networks, Computer; D012372:ROC Curve