{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T14:51:05Z","timestamp":1786978265297,"version":"build-2736575974"},"reference-count":30,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2024,8,28]],"date-time":"2024-08-28T00:00:00Z","timestamp":1724803200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["aao-hnsfjournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Otolaryngol.--head neck surg."],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To apply machine learning models based on air conduction thresholds of pure\u2010tone audiometry for automatic diagnosis of Meniere's disease (MD) and prediction of endolymphatic hydrops (EH).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Study Design<\/jats:title>\n                    <jats:p>Retrospective study.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Setting<\/jats:title>\n                    <jats:p>Tertiary medical center.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Gadolinium\u2010enhanced magnetic resonance imaging sequences and pure\u2010tone audiometry data were collected. Subsequently, basic and multiple analytical features were engineered based on the air conduction thresholds of pure\u2010tone audiometry. Later, 5\u00a0classical machine learning models were trained to diagnose MD using the engineered features. The models demonstrating excellent performance were also selected to predict EH. The model's effectiveness in MD diagnosis was compared with experienced otolaryngologists.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>First, the winning light gradient boosting (LGB) machine learning model trained by multiple features demonstrates a remarkable performance on the diagnosis of MD, achieving an accuracy rate of 87%, sensitivity of 83%, specificity of 90%, and a robust area under the receiver operating characteristic curve of 0.95, which compares favorably with experienced clinicians. Second, the LGB model, with an accuracy of 78% on EH prediction, outperformed the other 3\u00a0machine learning models. Finally, a feature importance analysis reveals a pivotal role of the specific pure\u2010tone audiometry features that are essential for both MD diagnosis and EH prediction. Highlighted features include standard deviation and mean of the whole\u2010frequency hearing, the peak of the audiogram, and hearing at low frequencies, notably at 250\u2009Hz.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>An efficient machine learning model based on pure\u2010tone audiometry features was produced to diagnose MD, which also showed the potential to predict the subtypes of EH. The innovative approach demonstrated a game\u2010changing strategy for MD screening and promising cost\u2010effective benefits for the health care enterprise.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/ohn.956","type":"journal-article","created":{"date-parts":[[2024,8,28]],"date-time":"2024-08-28T10:00:42Z","timestamp":1724839242000},"page":"233-242","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Applications of Machine Learning in Meniere's Disease Assessment Based on Pure\u2010Tone Audiometry"],"prefix":"10.1002","volume":"172","author":[{"given":"Xu","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Otorhinolaryngology Eye and ENT Hospital,\u00a0ENT Institute, Fudan University Shanghai China"},{"name":"Department of Otorhinolaryngology NHC Key Laboratory of Hearing Medicine, Fudan University Shanghai 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Otorhinolaryngology NHC Key Laboratory of Hearing Medicine, Fudan University Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suming","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Otorhinolaryngology Eye and ENT Hospital,\u00a0ENT Institute, Fudan University Shanghai China"},{"name":"Department of Otorhinolaryngology NHC Key Laboratory of Hearing Medicine, Fudan University Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhijian","family":"Dai","sequence":"additional","affiliation":[{"name":"Department of Otorhinolaryngology\u2013Head and Neck Surgery The Third Affiliated Hospital of Wenzhou Medical College Wenzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deping","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Otorhinolaryngology\u2013Head and Neck Surgery The Second Affiliated Hospital of Chongqing Medical University Chongqing 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