ArticleScientific reports2022
Prediction of hearing recovery in unilateral sudden sensorineural hearing loss using artificial intelligence.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Audiogram Configuration Predicts Treatment Response in Sudden Sensorineural Hearing Loss.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2026Article
- Harnessing Machine Learning for Personalized Care of Patients With Idiopathic Sudden Sensorineural Hearing Loss: A Multicenter Cohort Study.Clinical and experimental otorhinolaryngology · 2026Article
- Development of a machine learning model for prognostic prediction of severe sudden sensorineural hearing loss with hyperbaric oxygen therapy.Frontiers in neurology · 2025Article
- Application of Artificial Intelligence in Otology: Past, Present, and Future.Journal of clinical medicine · 2024Article
- Prediction of hearing recovery with deep learning algorithm in sudden sensorineural hearing loss.Scientific reports · 2024Article
- Predicting Sudden Sensorineural Hearing Loss Recovery with Patient-Personalized Seigel's Criteria Using Machine Learning.Diagnostics (Basel, Switzerland) · 2024Article
- Increased risk of sudden sensorineural hearing loss in patients with cervical spondylosis.Scientific reports · 2024Article
- Contemporary Review of Idiopathic Sudden Sensorineural Hearing Loss: Management and Prognosis.Journal of audiology & otology · 2024Review
- Impact of hyperbaric oxygen as initial treatment on idiopathic sudden sensorineural hearing loss prognosis: a propensity scores matching analysis.Brazilian journal of otorhinolaryngologyArticle
- Hearing recovery prediction and prognostic factors of idiopathic sudden sensorineural hearing loss: a retrospective analysis with a deep neural network model.Brazilian journal of otorhinolaryngologyArticle
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Authors and funding
6 authors.
Funding
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Abstract
Despite the significance of predicting the prognosis of idiopathic sudden sensorineural hearing loss (ISSNHL), no predictive models have been established. This study used artificial intelligence to develop prognosis models to predict recovery from ISSNHL. We retrospectively reviewed the medical data of 453 patients with ISSNHL (men, 220; women, 233; mean age, 50.3 years) who underwent treatment at a tertiary hospital between January 2021 and December 2019 and were followed up after 1 month. According to Siegel's criteria, 203 patients recovered in 1 month. Demographic characteristics, clinical and laboratory data, and pure-tone audiometry were analyzed. Logistic regression (baseline), a support vector machine, extreme gradient boosting, a light gradient boosting machine, and multilayer perceptron were used. The outcomes were the area under the receiver operating characteristic curve (AUROC) primarily, area under the precision-recall curve, Brier score, balanced accuracy, and F1 score. The light gradient boosting machine model had the best AUROC and balanced accuracy. Together with multilayer perceptron, it was also significantly superior to logistic regression in terms of AUROC. Using the SHapley Additive exPlanation method, we found that the initial audiogram shape is the most important prognostic factor. Machine/deep learning methods were successfully established to predict the prognosis of ISSNHL.
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