Artificial intelligence for early stroke diagnosis in acute vestibular syndrome

Athanasia Korda; Wilhelm Wimmer; Thomas Wyss; Efterpi Michailidou; Ewa Zamaro; Franca Wagner; Marco D Caversaccio; Georgios Mantokoudis

doi:10.3389/fneur.2022.919777

Artificial intelligence for early stroke diagnosis in acute vestibular syndrome

Front Neurol. 2022 Sep 8:13:919777. doi: 10.3389/fneur.2022.919777. eCollection 2022.

Authors

Athanasia Korda¹, Wilhelm Wimmer^{1

2}, Thomas Wyss¹, Efterpi Michailidou¹, Ewa Zamaro¹, Franca Wagner³, Marco D Caversaccio¹, Georgios Mantokoudis¹

Affiliations

¹ Department of Otorhinolaryngology, Head and Neck Surgery, Inselspital, University Hospital Bern and University of Bern, Bern, Switzerland.
² Hearing Research Laboratory, ARTORG Center, University of Bern, Bern, Switzerland.
³ University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, University Hospital Bern and University of Bern, Bern, Switzerland.

Abstract

Objective: Measuring 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.

Methods: We 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.

Results: We 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).

Conclusion: AI 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.

Keywords: artificial intelligence; emergency department; stroke diagnosis; vertigo; video head impulse test.