Objective To review the epidemiology, pathogenesis, diagnosis, and treatment of unilateral vestibular weakness (UVW), and critically assess the related evidence. Methods Literature research in Medline and other database sources until August 2020. Results The total number of included studies was 39. Conclusion The lifetime prevalence of UVW in the general population is 0.2%; the respective incidence is unknown. UVW frequently overlaps with other diagnoses; nevertheless, there is usually a history of a single, preceding, monophasic event. The respective criteria include unsteadiness of more than two months, clinical exclusion of BPPV, and exclusion of Meniere’s disease, central lesion, or bilateral vestibular dysfunction, and more than 25% inter-aural asymmetry in the caloric test. The latter represents the golden testing standard in suspected patients, and should be complemented with vestibulo-ocular reflex assessment via rotation-testing and video head-impulse test. Posturography can be useful to evaluate postural stability. Questionnaire-based assessments may assess symptom severity, the ensuing disability, and the subjective perception of patients’ overall balance status. MRI is advised in vertiginous patients in the presence of neurologic signs and symptoms, risk factors for cerebrovascular disease, or progressive unilateral hearing loss. Vestibular rehabilitation is effective in patients with UVW, whilst pharmacological treatment is of limited value.

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ObjectiveMeasuring the Vestibular-Ocular-Reflex (VOR) gains with the video head impulse test (vHIT) allows for accurate discrimination between peripheral and central causes of acute vestibular syndrome (AVS). In this study, we sought to investigate whether the accuracy of artificial intelligence (AI) based vestibular stroke classification applied in unprocessed vHIT data is comparable to VOR gain classification.MethodsWe performed a prospective study from July 2015 until April 2020 on all patients presenting at the emergency department (ED) with signs of an AVS. The patients underwent vHIT followed by a delayed MRI, which served as a gold standard for stroke confirmation. The MRI ground truth labels were then applied to train a recurrent neural network (long short-term memory architecture) that used eye- and head velocity time series extracted from the vHIT examinations.ResultsWe assessed 57 AVS patients, 39 acute unilateral vestibulopathy patients (AUVP) and 18 stroke patients. The overall sensitivity, specificity and accuracy for detecting stroke with a VOR gain cut-off of 0.57 was 88.8, 92.3, and 91.2%, respectively. The trained neural network was able to classify strokes with a sensitivity of 87.7%, a specificity of 88.4%, and an accuracy of 87.9% based on the unprocessed vHIT data. The accuracy of these two methods was not significantly different (p = 0.09).ConclusionAI can accurately diagnose a vestibular stroke by using unprocessed vHIT time series. The quantification of eye- and head movements with the use of machine learning and AI can serve in the future for an automated diagnosis in ED patients with acute dizziness. The application of different neural network architectures can potentially further improve performance and enable direct inference from raw video recordings.

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