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Artificial neural network: a new diagnostic posturographic tool for disorders of stance.

AbstractOBJECTIVE:
To determine the accuracy of diagnoses made with artificial neural network techniques (ANNW) that identify postural sway patterns typical for balance disorders.
METHODS:
Body sway was measured by means of posturography during 10 test conditions of increasing difficulty. From a database of 676 subjects 60 training cases (TCs) and 60 validation cases (VCs) were selected in which the following diagnoses had been established clinically: normal subject (NS), postural phobic vertigo (PPV), anterior lobe cerebellar atrophy (CA), primary orthostatic tremor (OT), and acute unilateral vestibular neuritis (VN). A standard 3-layer feed-forward ANNW, using the backpropagation algorithm, was trained with TCs, validated with VCs, and its accuracy tested on 5 new cases.
RESULTS:
ANNW differentiated the established diagnoses with an overall sensitivity and specificity of 0.93. Sensitivity and specificity were 1 for NS and OT; for PPV, 0.87 and 0.96; for CA, 1 and 0.98; and for VN, 0.8 and 0.98, respectively. New subjects were identified with ANNW output variables of the true diagnoses between 0.73 and 1.
CONCLUSIONS:
ANNW differentiates postural sway patterns of several distinct clinical balance disorders with high sensitivity and specificity. Once designed and tested ANNW could be considered a black box, which each examiner can apply to predict a specific diagnosis even without a clinical examination.
SIGNIFICANCE:
A promising diagnostic tool for disorders of upright stance in selected neurological disorders.
AuthorsSiegbert Krafczyk, Simon Tietze, Walter Swoboda, Peter Valkovic, Thomas Brandt
JournalClinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology (Clin Neurophysiol) Vol. 117 Issue 8 Pg. 1692-8 (Aug 2006) ISSN: 1388-2457 [Print] Netherlands
PMID16797231 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)
Topics
  • Adolescent
  • Adult
  • Aged
  • Female
  • Humans
  • Male
  • Middle Aged
  • Neural Networks, Computer
  • Postural Balance (physiology)
  • Sensation Disorders (diagnosis)
  • Sensitivity and Specificity

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