Artificial neural network classification of pharyngeal high-resolution manometry with impedance data

Laryngoscope

Volumn 123, Issue 3, 2013, Pages 713-720

  Hoffman, Matthew R ^a   Mielens, Jason D ^a   Omari, Taher I ^b   Rommel, Nathalie ^c   Jiang, Jack J ^a   McCulloch, Timothy M ^a  

^a UNIVERSITY OF WISCONSIN SCHOOL OF MEDICINE AND PUBLIC HEALTH   (United States)

^b Children   (Australia)

^c KU LEUVEN UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

Artificial neural network; aspiration; classification model; dysphagia; high resolution manometry; impedance; Level of Evidence: 4

Indexed keywords

ACCURACY; ADULT; AGED; ARTICLE; ARTIFICIAL NEURAL NETWORK; ASPIRATION; CLINICAL ARTICLE; DYSPHAGIA; FEMALE; FLUOROSCOPY; HUMAN; IMPEDANCE; MALE; MANOMETRY; PHARYNGEAL HIGH RESOLUTION MANOMETRY; PHARYNX; PHARYNX EXAMINATION; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; SAFETY; SWALLOWING;

ADULT; AGED; AGED, 80 AND OVER; ALGORITHMS; DEGLUTITION; FEMALE; HUMANS; MALE; MANOMETRY; MIDDLE AGED; NEURAL NETWORKS (COMPUTER); ROC CURVE;

EID: 84874424303     PISSN: 0023852X     EISSN: 15314995     Source Type: Journal    
DOI: 10.1002/lary.23655     Document Type: Article

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