Risk evaluation and incidence prediction of endolymphatic hydrops using multilayer perceptron in patients with...

Endolymphatic hydrops (EH) has been visualized on magnetic resonance imaging (MRI) in patients with various inner ear diseases. The purpose of this study was to evaluate the prevalence and risk factors of significant EH on inner ear MRI in patients with 1 or more audiovestibular symptoms and to predict the incidence of significant EH using multivariate analysis and multilayer perceptron artificial neural network modeling. This retrospective study included a total of 135 patients with 1 or more audiovestibular symptoms who do not meet the diagnostic criteria for MD and underwent inner ear MRI at our institution from July 2021 to January 2024. The EH grade of each patient was evaluated, and "significant EH" was considered grade II or III. Of 135 patients with 1 or more audiovestibular symptoms, 48 patients (35.6%) presented with significant EH and 87 patients (64.4%) without significant EH on inner ear MRI. The prevalence of significant EH was higher in males, which was statistically significant (P = .007). The prevalence of significant EH was higher in the right ear, and the mean age of patients with significant EH was 1.94 years higher, but no statistical significance was observed (P = .660 and .456, retrospectively). The odds ratio for significant EH development was 2.696 (95% confidence interval: 1.296-5.607) times higher in men, which was statistically significant. Predicting the incidence of significant EH development using multivariate analysis, sex was the only variable that was statistically significant (P = .008). Based on a predictive model using multilayer perceptron (MLP), the classification accuracy of the model was 79.5%. In our study, the male gender could be related to the risk of developing significant EH in patients with audiovestibular symptoms. The accuracy of our suggested MLP model for predicting the incidence of significant EH was 79.5%, with sex being the highest predictor importance. In the future, inner ear MRI and MLP neural network modeling can be combined as a noninvasive and precise support system in the diagnosis of EH.