Objective prediction of pharyngeal swallow dysfunction in dysphagia through artificial neural network modeling.

Journal: Neurogastroenterology and motility

PMID: 26891061

Abstract

BACKGROUND: Pharyngeal pressure-flow analysis (PFA) of high resolution impedance-manometry (HRIM) with calculation of the swallow risk index (SRI) can quantify swallow dysfunction predisposing to aspiration. We explored the potential use of artificial neural networks (ANN) to model the relationship between PFA swallow metrics and aspiration and to predict swallow dysfunction.

Authors

S Kritas

Gastroenterology Unit, Women's & Children's Health Network, Adelaide, SA, Australia.
E Dejaeger

Gerontology and Geriatrics, University Hospitals Leuven, Leuven, Belgium.
J Tack

Translational Research Center for Gastrointestinal Diseases (TARGID), KU Leuven, Leuven, Belgium.
T Omari

Department of Human Physiology, College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia.
N Rommel

Neurosciences, ExpORL, KU Leuven, Leuven, Belgium.

Keywords

Adolescent Adult Aged Aged, 80 and over Computer Simulation Cross-Sectional Studies Deglutition Disorders Female Humans Male Middle Aged Neural Networks, Computer Pharynx Young Adult

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View on PubMed Access via DOI PubMed (26891061)

Objective prediction of pharyngeal swallow dysfunction in dysphagia through artificial neural network modeling.

Abstract

Authors

Keywords

External Resources

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