Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Chang, Young-Soo, Park, Heesung, Hong, Sung Hwa, Chung, Won-Ho, Cho, Yang-Sun, Moon, Il Joon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
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
_version_ 1783423512770248704
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
work_keys_str_mv AT changyoungsoo predictingcochleardeadregionsinpatientswithhearinglossthroughamachinelearningbasedapproachapreliminarystudy
AT parkheesung predictingcochleardeadregionsinpatientswithhearinglossthroughamachinelearningbasedapproachapreliminarystudy
AT hongsunghwa predictingcochleardeadregionsinpatientswithhearinglossthroughamachinelearningbasedapproachapreliminarystudy
AT chungwonho predictingcochleardeadregionsinpatientswithhearinglossthroughamachinelearningbasedapproachapreliminarystudy
AT choyangsun predictingcochleardeadregionsinpatientswithhearinglossthroughamachinelearningbasedapproachapreliminarystudy
AT mooniljoon predictingcochleardeadregionsinpatientswithhearinglossthroughamachinelearningbasedapproachapreliminarystudy