Psychiatry

Computer science

Neurology

Radiology

Radiomics

Neuroradiology

Meniere's disease

Predictive value

Internal medicine

Artificial intelligence

Medicine

MENIERE DISEASE

Disease

Magnetic resonance imaging

Machine learning

Pathology

La radiologia medica

This study investigated the feasibility of a new image analysis technique (radiomics) on conventional MRI for the computer-aided diagnosis of Menière's disease.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.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.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.

A non-invasive, automated diagnosis of Menière’s disease using radiomics and machine learning on conventional magnetic resonance imaging: A multicentric, case-controlled feasibility study