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

OBJECTIVE

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.