Modern machine-learning can support diagnostic differentiation of central and peripheral acute vestibular disorders.

Journal of Neurology
Seyed-Ahmad AhmadiAndreas Zwergal

Abstract

Diagnostic classification of central vs. peripheral etiologies in acute vestibular disorders remains a challenge in the emergency setting. Novel machine-learning methods may help to support diagnostic decisions. In the current study, we tested the performance of standard and machine-learning approaches in the classification of consecutive patients with acute central or peripheral vestibular disorders. 40 Patients with vestibular stroke (19 with and 21 without acute vestibular syndrome (AVS), defined by the presence of spontaneous nystagmus) and 68 patients with peripheral AVS due to vestibular neuritis were recruited in the emergency department, in the context of the prospective EMVERT trial (EMergency VERTigo). All patients received a standardized neuro-otological examination including videooculography and posturography in the acute symptomatic stage and an MRI within 7 days after symptom onset. Diagnostic performance of state-of-the-art scores, such as HINTS (Head Impulse, gaze-evoked Nystagmus, Test of Skew) and ABCD2 (Age, Blood, Clinical features, Duration, Diabetes), for the differentiation of vestibular stroke vs. peripheral AVS was compared to various machine-learning approaches: (i) linear logistic regression (LR), (ii...Continue Reading

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Citations

Aug 28, 2020·Frontiers in Neurology·Andreas ZwergalMarianne Dieterich
Apr 20, 2021·Fortschritte der Neurologie-Psychiatrie·Andreas Zwergal, Marianne Dieterich
Aug 3, 2021·Frontiers in Neurology·Carolin Hoyer, Kristina Szabo
Aug 21, 2021·Frontiers in Neurology·Gerome VivarSeyed-Ahmad Ahmadi
Nov 30, 2021·The Neurologist·Yachiyo MatsumuraTadashi Kitahara
Dec 11, 2021·Current Opinion in Neurology·Georgios MantokoudisDaniel R Gold

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Software Mentioned

ANN
ABCD
learn
vHIT
GMC
RF
HINTS
MultiGMC
scikit
EMVERT

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