The surgical options for patients with single-sided Menière\'s disease and functional deafness are challenging. Our case series reports the outcomes of surgical treatments of patients with single-sided Menière\'s disease and functional deafness. These patients have undergone a one-staged occlusion of all semi-circular canals and cochlear implantation. Five patients (four female and one male; 62 ± 8.2 years with a range from 50 to 72 years) with single-sided Menière\'s disease and functional deafness were included in this study. In all cases, the patients suffered from frequent rotational vertigo episodes for many years. Other treatment options (e.g., medication) had not yet been successful. Preoperatively, the Dizziness Handicap Inventory (DHI) of all patients indicated severe emotional, physical, and functional deficits. Patients showed a functional (near-total) deafness of the affected ear in all cases. All patients were supplied with cochlear implants in combination with a triple occlusion of all semi-circular canals in a one-stage procedure. After a short period of increased dizziness following surgery and after the activation of the cochlear implant and CI rehabilitation (auditory-verbal therapy), vertigo control and an adequate audiological outcome were achieved. The DHI showed a constant decrease after surgery. The combination of a triple semi-circular canal occlusion and cochlear implantation can be an efficient treatment for patients with single-sided Menière\'s disease.

OBJECTIVE: This study investigated the feasibility of a new image analysis technique (radiomics) on conventional MRI for the computer-aided diagnosis of Menière\'s disease.
METHODS: A retrospective, multicentric diagnostic case-control study was performed. This study included 120 patients with unilateral or bilateral Menière\'s disease and 140 controls from four centers in the Netherlands and Belgium. Multiple radiomic features were extracted from conventional MRI scans and used to train a machine learning-based, multi-layer perceptron classification model to distinguish patients with Menière\'s disease from controls. The primary outcomes were accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the classification model.
RESULTS: The classification accuracy of the machine learning model on the test set was 82%, with a sensitivity of 83%, and a specificity of 82%. The positive and negative predictive values were 71%, and 90%, respectively.
CONCLUSIONS: The multi-layer perceptron classification model yielded a precise, high-diagnostic performance in identifying patients with Menière\'s disease based on radiomic features extracted from conventional T2-weighted MRI scans. In the future, radiomics might serve as a fast and noninvasive decision support system, next to clinical evaluation in the diagnosis of Menière\'s disease.