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.

ObjectiveTo evaluate the performance of a machine learning model and the effects of major prognostic factors on hearing outcomes following intact canal wall (ICW) mastoidectomy with tympanoplasty.Study DesignRetrospective cross‐sectional study.SettingTertiary hospital.MethodsA total of 484 patients with chronic otitis media who underwent ICW tympanomastoidectomy between January 2007 and December 2020 were included in this study. Successful hearing outcomes were defined by a postoperative air‐bone gap (ABG) of ≤20 dB and preoperative air conduction (AC)‐postoperative AC value of ≥15 dB according to the Korean Otological Society guidelines for outcome reporting after chronic otitis media surgery. The light gradient boosting machine (LightGBM) and multilayer perceptron (MLP) models were tested as artificial intelligence models and compared using logistic regression. The main outcome assessed was the successful hearing outcome after surgery, measured using the area under the receiver operating characteristic curve (AUROC).ResultsIn the analysis using the postoperative ABG criterion, the LightGBM exhibited a significantly higher AUROC compared to those of the baseline model (mean, 0.811). According to the difference between preoperative and postoperative AC, the MLP showed a significantly higher AUROC than those of the baseline model (mean, 0.795).ConclusionThis study analyzed multiple factors that could affect the hearing outcome using different artificial intelligence models and found that preoperative hearing status was the most important factor. Our findings provide additional information regarding postoperative hearing for clinicians.