Clinical Vestibular Testing Assessed With Machine-Learning Algorithms

doi:10.1001/jamaoto.2014.3519

Clinical Vestibular Testing Assessed With Machine-Learning Algorithms

2015-04-01

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DOI：10.1001/jamaoto.2014.3519

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IMPORTANCE Dizziness and imbalance are common clinical problems, and accurate diagnosis depends on determining whether damage is localized to the peripheral vestibular system. Vestibular testing guides this determination, but the accuracy of the different tests is not known. OBJECTIVE To determine how well each element of the vestibular test battery segregates patients with normal peripheral vestibular function from those with unilateral reductions in vestibular function. DESIGN, SETTING, AND PARTICIPANTS Retrospective analysis of vestibular test batteries in 8080 patients. Clinical medical records were reviewed for a subset of individuals with the reviewers blinded to the vestibular test data. INTERVENTIONS A group of machine-learning classifiers were trained using vestibular test data from persons who were manually labeled as having normal vestibular function or unilateral vestibular damage based on a review of their medical records. The optimal trained classifier was then used to categorize patients whose diagnoses were unknown, allowing us to determine the information content of each element of the vestibular test battery. MAIN OUTCOMES AND MEASURES The information provided by each element of the vestibular test battery to segregate individuals with normal vestibular function from those with unilateral vestibular damage. RESULTS The time constant calculated from the rotational test ranked first in information content, and measures that were related physiologically to the rotational time constant were 10 of the top 12 highest-ranked variables. The caloric canal paresis ranked eighth, and the other elements of the test battery provided minimal additional information. The sensitivity of the rotational time constant was 77.2%, and the sensitivity of the caloric canal paresis was 59.6%; the specificity of the rotational time constant was 89.0%, and the specificity of the caloric canal paresis was 64.9%. The diagnostic accuracy of the vestibular test battery increased from 72.4% to 93.4% when the data were analyzed with the optimal machine-learning classifier. CONCLUSIONS AND RELEVANCE Rotational testing should be considered the primary test to diagnose unilateral peripheral vestibular damage in patients with dizziness or imbalance. Most physicians, however, continue to rely on caloric tests to guide their diagnoses. Our results support a significant shift in the approach used to determine diagnoses in patients with vestibular symptoms.

Keyword:

SINUSOIDAL HARMONIC ACCELERATION

HEAD IMPULSE TEST

VESTIBULOOCULAR REFLEX

CALORIC TEST

CLASSIFICATION

PREVALENCE

NYSTAGMUS

ADULTS

ENG

Clinical Vestibular Testing Assessed With Machine-Learning Algorithms

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