Objectives. Prognosticating idiopathic sudden sensorineural hearing loss (ISSNHL) is an important challenge. In our study, a dataset was split into training and test sets and cross-validation was implemented on the training set, thereby determining the hyperparameters for machine learning models with high test accuracy and low bias. The effectiveness of the following five machine learning models for predicting the hearing prognosis in patients with ISSNHL after 1 month of treatment was assessed: adaptive boosting, K-nearest neighbor, multilayer perceptron, random forest (RF), and support vector machine (SVM).Methods. The medical records of 523 patients with ISSNHL admitted to Korea University Ansan Hospital between January 2010 and October 2017 were retrospectively reviewed. In this study, we analyzed data from 227 patients (recovery, 106; no recovery, 121) after excluding those with missing data. To determine risk factors, statistical hypothesis tests (e.g., the two-sample t-test for continuous variables and the chi-square test for categorical variables) were conducted to compare patients who did or did not recover. Variables were selected using an RF model depending on two criteria (mean decreases in the Gini index and accuracy).Results. The SVM model using selected predictors achieved both the highest accuracy (75.36%) and the highest F-score (0.74) on the test set. The RF model with selected variables demonstrated the second-highest accuracy (73.91%) and F-score (0.74). The RF model with the original variables showed the same accuracy (73.91%) as that of the RF model with selected variables, but a lower F-score (0.73). All the tested models, except RF, demonstrated better performance after variable selection based on RF.Conclusion. The SVM model with selected predictors was the best-performing of the tested prediction models. The RF model with selected predictors was the second-best model. Therefore, machine learning models can be used to predict hearing recovery in patients with ISSNHL.

The aim of this study was to evaluate the protective effects of quercetin on cisplatin-induced hair cell damage in transgenic zebrafish embryos. Five days postfertilization zebrafish embryos were exposed to 1 mM cisplatin and quercetin at 10, 50, 100, or 200 μM for 4 h. Hair cells within neuromasts of the supraorbital, otic, and occipital lateral lines were analyzed by fluorescent microscopy (n = 10). Survival of hair cells was calculated as the average number of hair cells in the control group that were not exposed to cisplatin. Ultrastructural changes were evaluated using scanning electron microscopy. Hair cell damage in neuromasts was decreased by co-treatment of quercetin and cisplatin (quercetin 100 μM: 8.6 ± 1.1 cells; 1 mM cisplatin only: 5.0 ± 0.5 cells; n = 10, p < 0.05); apoptosis of hair cells examined by special stain was also decreased by quercetin. The ultrastructure of hair cells within neuromasts was preserved in zebrafish by the combination of quercetin (100 μM) and cisplatin (1 mM). In conclusion, quercetin showed protective effects against cisplatin-induced toxicity in a zebrafish model. The results of this study suggest the possibility of a protective role of quercetin against cisplatin-induced apoptotic cell death in zebrafish.

Despite the significance of predicting the prognosis of idiopathic sudden sensorineural hearing loss (ISSNHL), no predictive models have been established. This study used artificial intelligence to develop prognosis models to predict recovery from ISSNHL. We retrospectively reviewed the medical data of 453 patients with ISSNHL (men, 220; women, 233; mean age, 50.3 years) who underwent treatment at a tertiary hospital between January 2021 and December 2019 and were followed up after 1 month. According to Siegel’s criteria, 203 patients recovered in 1 month. Demographic characteristics, clinical and laboratory data, and pure-tone audiometry were analyzed. Logistic regression (baseline), a support vector machine, extreme gradient boosting, a light gradient boosting machine, and multilayer perceptron were used. The outcomes were the area under the receiver operating characteristic curve (AUROC) primarily, area under the precision-recall curve, Brier score, balanced accuracy, and F1 score. The light gradient boosting machine model had the best AUROC and balanced accuracy. Together with multilayer perceptron, it was also significantly superior to logistic regression in terms of AUROC. Using the SHapley Additive exPlanation method, we found that the initial audiogram shape is the most important prognostic factor. Machine/deep learning methods were successfully established to predict the prognosis of ISSNHL.