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

Abstract

Background 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. Methods 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, gazeevoked Nystagmus, Test of Skew) and ABCD 2 (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) non-linear random forest (RF), (iii) artificial neural network, and (iv) geometric deep learning (Single/MultiGMC). A prospective classification was simulated by tenfold cross-validation. We analyzed whether machine-estimated feature importances correlate with clinical experience. Results Machine-learning methods (e.g., MultiGMC) outperform univariate scores, such as HINTS or ABCD 2 , for differentiation of all vestibular strokes vs. peripheral AVS (MultiGMC area-under-the-curve (AUC): 0.96 vs. HINTS/ABCD 2 AUC: 0.71/0.58). HINTS performed similarly to MultiGMC for vestibular stroke with AVS (AUC: 0.86), but more poorly for vestibular stroke without AVS (AUC: 0.54). Machine-learning models learn to put different weights on particular features, each of which is relevant from a clinical viewpoint. Established non-linear machine-learning methods like RF and linear methods like LR are less powerful classification models (AUC: 0.89 vs. 0.62). Conclusions Established clinical scores (such as HINTS) provide a valuable baseline assessment for stroke detection in acute vestibular syndromes. In addition, machine-learning methods may have the potential to increase sensitivity and selectivity in the establishment of a correct diagnosis. Keywords Acute vestibular syndrome • HINTS • Machine-learning • MRI • Vestibular neuritis • Vestibular stroke Abbreviations ABCD 2 Age, blood pressure, clinical features, duration, diabetes ANN Artificial neural network AUC Area-under-the-curve AVS Acute vestibular syndrome CVRF Cardiovascular risk factors DT Decision tree DWI Diffusion weighted images ED Emergency department EMVERT EMergency VERTigo FLAIR Fluid attenuated inversion recovery GMC Geometric matrix completion HINTS Head impulse, gaze-evoked nystagmus, test of skew Seyed-Ahmad Ahmadi an...

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