Literature DB >> 26891061

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

S Kritas1,2,3, E Dejaeger4,5, J Tack3, T Omari1,6, N Rommel2,3.   

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.
METHODS: Two hundred consecutive dysphagia patients referred for videofluoroscopy and HRIM were assessed. Presence of aspiration was scored and PFA software derived 13 metrics and the SRI. An ANN was created and optimized over training cycles to achieve optimal classification accuracy for matching inputs (PFA metrics) to output (presence of aspiration on videofluoroscopy). Application of the ANN returned a value between 0.00 and 1.00 reflecting the degree of swallow dysfunction. KEY
RESULTS: Twenty one patients were excluded due to insufficient number of swallows (<4). Of 179, 58 aspirated and 27 had aspiration pneumonia history. The SRI was higher in aspirators (aspiration 24 [9, 41] vs no aspiration 7 [2, 18], p < 0.001) and patients with pneumonia (pneumonia 27 [5, 42] vs no pneumonia 8 [3, 24], p < 0.05). The ANN Predicted Risk was higher in aspirators (aspiration 0.57 [0.38, 0.82] vs no aspiration 0.13 [0.4, 0.25], p < 0.001) and in patients with pneumonia (pneumonia 0.46 [0.18, 0.60] vs no pneumonia 0.18 [0.6, 0.49], p < 0.01). Prognostic value of the ANN was superior to the SRI. CONCLUSIONS & INFERENCES: In a heterogeneous cohort of dysphagia patients, PFA with ANN modeling offers enhanced detection of clinically significant swallowing dysfunction, probably more accurately reflecting the complex interplay of swallow characteristics that causes aspiration.
© 2016 John Wiley & Sons Ltd.

Entities:  

Keywords:  dysphagia; high resolution manometry; impedance measurement; swallowing

Mesh:

Year:  2016        PMID: 26891061     DOI: 10.1111/nmo.12730

Source DB:  PubMed          Journal:  Neurogastroenterol Motil        ISSN: 1350-1925            Impact factor:   3.598


  4 in total

1.  Identification of swallowing disorders in early and mid-stage Parkinson's disease using pattern recognition of pharyngeal high-resolution manometry data.

Authors:  C A Jones; M R Hoffman; L Lin; S Abdelhalim; J J Jiang; T M McCulloch
Journal:  Neurogastroenterol Motil       Date:  2017-11-16       Impact factor: 3.598

Review 2.  High-Resolution Pharyngeal Manometry and Impedance: Protocols and Metrics-Recommendations of a High-Resolution Pharyngeal Manometry International Working Group.

Authors:  Taher I Omari; Michelle Ciucci; Kristin Gozdzikowska; Ester Hernández; Katherine Hutcheson; Corinne Jones; Julia Maclean; Nogah Nativ-Zeltzer; Emily Plowman; Nicole Rogus-Pulia; Nathalie Rommel; Ashli O'Rourke
Journal:  Dysphagia       Date:  2019-06-05       Impact factor: 3.438

3.  SLP-Perceived Technical and Patient-Centered Factors Associated with Pharyngeal High-Resolution Manometry.

Authors:  Corinne A Jones; Nicole M Rogus-Pulia; Angela L Forgues; Jason Orne; Cameron L Macdonald; Nadine P Connor; Timothy M McCulloch
Journal:  Dysphagia       Date:  2018-10-31       Impact factor: 3.438

4.  Comparison of three data mining models for prediction of advanced schistosomiasis prognosis in the Hubei province.

Authors:  Guo Li; Xiaorong Zhou; Jianbing Liu; Yuanqi Chen; Hengtao Zhang; Yanyan Chen; Jianhua Liu; Hongbo Jiang; Junjing Yang; Shaofa Nie
Journal:  PLoS Negl Trop Dis       Date:  2018-02-15
  4 in total

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