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This study included 120 patients with unilateral or bilateral Meni\u00e8re\u2019s 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\u00a0patients with Meni\u00e8re\u2019s disease from controls. The primary outcomes were accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the classification model.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>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.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>The multi-layer perceptron classification model yielded a precise, high-diagnostic performance in identifying patients with Meni\u00e8re\u2019s 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 Meni\u00e8re\u2019s disease.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s11547-021-01425-w","type":"journal-article","created":{"date-parts":[[2021,11,25]],"date-time":"2021-11-25T10:02:58Z","timestamp":1637834578000},"page":"72-82","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["A non-invasive, automated diagnosis of Meni\u00e8re\u2019s disease using radiomics and machine learning on conventional magnetic resonance imaging: A multicentric, case-controlled feasibility study"],"prefix":"10.1007","volume":"127","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9629-5509","authenticated-orcid":false,"given":"Marly F. J. 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