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Predicting cochlear dead regions in patients with hearing loss through a machine learning-based approach: A preliminary study
We propose a machine learning (ML)-based model for predicting cochlear dead regions (DRs) in patients with hearing loss of various etiologies. Five hundred and fifty-five ears from 380 patients (3,770 test samples) diagnosed with sensorineural hearing loss (SNHL) were analyzed. A threshold-equalizin...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6546232/ https://www.ncbi.nlm.nih.gov/pubmed/31158267 http://dx.doi.org/10.1371/journal.pone.0217790 |
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author | Chang, Young-Soo Park, Heesung Hong, Sung Hwa Chung, Won-Ho Cho, Yang-Sun Moon, Il Joon |
author_facet | Chang, Young-Soo Park, Heesung Hong, Sung Hwa Chung, Won-Ho Cho, Yang-Sun Moon, Il Joon |
author_sort | Chang, Young-Soo |
collection | PubMed |
description | We propose a machine learning (ML)-based model for predicting cochlear dead regions (DRs) in patients with hearing loss of various etiologies. Five hundred and fifty-five ears from 380 patients (3,770 test samples) diagnosed with sensorineural hearing loss (SNHL) were analyzed. A threshold-equalizing noise (TEN) test was applied to detect the presence of DRs. Data were collected on sex, age, side of the affected ear, hearing loss etiology, word recognition scores (WRS), and pure-tone thresholds at each frequency. According to the cause of hearing loss as diagnosed by the physician, we categorized the patients into six groups: 1) SNHL with unknown etiology; 2) sudden sensorineural hearing loss (SSNHL); 3) vestibular schwannoma (VS); 4) Meniere's disease (MD); 5) noise-induced hearing loss (NIHL); or 6) presbycusis or age-related hearing loss (ARHL). To develop a predictive model, we performed recursive partitioning and regression for classification, logistic regression, and random forest. The overall prevalence of one or more DRs in test ears was 20.36% (113 ears). Among the 3,770 test samples, the overall frequency-specific prevalence of DR was 6.7%. WRS, pure-tone thresholds at each frequency, disease type (VS or MD), and frequency information were useful for predicting DRs. Sex and age were not associated with detecting DRs. Based on these results, we suggest possible predictive factors for determining the presence of DRs. To improve the predictive power of the model, a more flexible model or more clinical features, such as the duration of hearing loss or risk factors for developing DRs, may be needed. |
format | Online Article Text |
id | pubmed-6546232 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-65462322019-06-17 Predicting cochlear dead regions in patients with hearing loss through a machine learning-based approach: A preliminary study Chang, Young-Soo Park, Heesung Hong, Sung Hwa Chung, Won-Ho Cho, Yang-Sun Moon, Il Joon PLoS One Research Article We propose a machine learning (ML)-based model for predicting cochlear dead regions (DRs) in patients with hearing loss of various etiologies. Five hundred and fifty-five ears from 380 patients (3,770 test samples) diagnosed with sensorineural hearing loss (SNHL) were analyzed. A threshold-equalizing noise (TEN) test was applied to detect the presence of DRs. Data were collected on sex, age, side of the affected ear, hearing loss etiology, word recognition scores (WRS), and pure-tone thresholds at each frequency. According to the cause of hearing loss as diagnosed by the physician, we categorized the patients into six groups: 1) SNHL with unknown etiology; 2) sudden sensorineural hearing loss (SSNHL); 3) vestibular schwannoma (VS); 4) Meniere's disease (MD); 5) noise-induced hearing loss (NIHL); or 6) presbycusis or age-related hearing loss (ARHL). To develop a predictive model, we performed recursive partitioning and regression for classification, logistic regression, and random forest. The overall prevalence of one or more DRs in test ears was 20.36% (113 ears). Among the 3,770 test samples, the overall frequency-specific prevalence of DR was 6.7%. WRS, pure-tone thresholds at each frequency, disease type (VS or MD), and frequency information were useful for predicting DRs. Sex and age were not associated with detecting DRs. Based on these results, we suggest possible predictive factors for determining the presence of DRs. To improve the predictive power of the model, a more flexible model or more clinical features, such as the duration of hearing loss or risk factors for developing DRs, may be needed. Public Library of Science 2019-06-03 /pmc/articles/PMC6546232/ /pubmed/31158267 http://dx.doi.org/10.1371/journal.pone.0217790 Text en © 2019 Chang et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Chang, Young-Soo Park, Heesung Hong, Sung Hwa Chung, Won-Ho Cho, Yang-Sun Moon, Il Joon Predicting cochlear dead regions in patients with hearing loss through a machine learning-based approach: A preliminary study |
title | Predicting cochlear dead regions in patients with hearing loss through a machine learning-based approach: A preliminary study |
title_full | Predicting cochlear dead regions in patients with hearing loss through a machine learning-based approach: A preliminary study |
title_fullStr | Predicting cochlear dead regions in patients with hearing loss through a machine learning-based approach: A preliminary study |
title_full_unstemmed | Predicting cochlear dead regions in patients with hearing loss through a machine learning-based approach: A preliminary study |
title_short | Predicting cochlear dead regions in patients with hearing loss through a machine learning-based approach: A preliminary study |
title_sort | predicting cochlear dead regions in patients with hearing loss through a machine learning-based approach: a preliminary study |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6546232/ https://www.ncbi.nlm.nih.gov/pubmed/31158267 http://dx.doi.org/10.1371/journal.pone.0217790 |
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