{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T11:31:08Z","timestamp":1781091068405,"version":"3.54.1"},"reference-count":41,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2020,7,14]],"date-time":"2020-07-14T00:00:00Z","timestamp":1594684800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100005193","name":"american academy of otolaryngology-head and neck surgery","doi-asserted-by":"publisher","award":["Centralized Otolaryngology Research Efforts (CORE) \u2013 AAO-HNSF Resident Research Award 2018-2019"],"award-info":[{"award-number":["Centralized Otolaryngology Research Efforts (CORE) \u2013 AAO-HNSF Resident Research Award 2018-2019"]}],"id":[{"id":"10.13039\/100005193","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000054","name":"national cancer institute","doi-asserted-by":"publisher","award":["Cancer Center Support Grant P30 CA168524"],"award-info":[{"award-number":["Cancer Center Support Grant P30 CA168524"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000057","name":"national institute of general medical sciences","doi-asserted-by":"publisher","award":["award P20GM103418"],"award-info":[{"award-number":["award P20GM103418"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aao-hnsfjournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Otolaryngol.--head neck surg."],"published-print":{"date-parts":[[2021,2]]},"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>Diagnosis and treatment of M\u00e9ni\u00e8re\u2019s disease remains a significant challenge because of our inability to understand what is occurring on a molecular level. MicroRNA (miRNA) perilymph profiling is a safe methodology and may serve as a \u201cliquid biopsy\u201d equivalent. We used machine learning (ML) to evaluate miRNA expression profiles of various inner ear pathologies to predict diagnosis of M\u00e9ni\u00e8re\u2019s disease.<\/jats:p><\/jats:sec><jats:sec><jats:title>Study Design<\/jats:title><jats:p>Prospective cohort study.<\/jats:p><\/jats:sec><jats:sec><jats:title>Setting<\/jats:title><jats:p>Tertiary academic hospital.<\/jats:p><\/jats:sec><jats:sec><jats:title>Subjects and Methods<\/jats:title><jats:p>Perilymph was collected during labyrinthectomy (M\u00e9ni\u00e8re\u2019s disease, n = 5), stapedotomy (otosclerosis, n = 5), and cochlear implantation (sensorineural hearing loss [SNHL], n = 9). miRNA was isolated and analyzed with the Affymetrix miRNA 4.0 array. Various ML classification models were evaluated with an 80\/20 train\/test split and cross\u2010validation. Permutation feature importance was performed to understand miRNAs that were critical to the classification models.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In terms of miRNA profiles for conductive hearing loss versus M\u00e9ni\u00e8re\u2019s, 4\u00a0models were able to differentiate and identify the 2 disease classes with 100% accuracy. The top\u2010performing models used the same miRNAs in their decision classification model but with different weighted values. All candidate models for SNHL versus M\u00e9ni\u00e8re\u2019s performed significantly worse, with the best models achieving 66% accuracy. M\u00e9ni\u00e8re\u2019s models showed unique features distinct from SNHL.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>We can use ML to build M\u00e9ni\u00e8re\u2019s\u2010specific prediction models using miRNA profile alone. However, ML models were less accurate in predicting SNHL from M\u00e9ni\u00e8re\u2019s, likely from overlap of miRNA biomarkers. The power of this technique is that it identifies biomarkers without knowledge of the pathophysiology, potentially leading to identification of novel biomarkers and diagnostic tests.<\/jats:p><\/jats:sec>","DOI":"10.1177\/0194599820940649","type":"journal-article","created":{"date-parts":[[2020,7,14]],"date-time":"2020-07-14T12:00:22Z","timestamp":1594728022000},"page":"399-406","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["MicroRNA Profiling as a Methodology to Diagnose M\u00e9ni\u00e8re\u2019s Disease: Potential Application of Machine Learning"],"prefix":"10.1002","volume":"164","author":[{"given":"Matthew","family":"Shew","sequence":"first","affiliation":[{"name":"Department of Otolaryngology\u2013Head and Neck Surgery Washington University School of Medicine in St Louis St Louis Missouri 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