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, Wingyan; Cho, Yang-Sun; Moon, Il Joon

doi:10.1371/journal.pone.0217790

Cited by 19 publications

(19 citation statements)

References 33 publications

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“…Chang et al. developed three ML models, based on the algorithms of classification tree, logistic regression (LR) and random forest (RF), to predict the cochlear dead areas of 380 hearing loss patients with different causes [37] . By screening noise-induced hearing loss (NIHL)-associated single nucleotide polymorphisms (SNPs), Zang et al.…”

Section: Discussionmentioning

confidence: 99%

Machine learning-based genetic diagnosis models for hereditary hearing loss by the GJB2, SLC26A4 and MT-RNR1 variants

Luo

et al. 2021

eBioMedicine

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Background Hereditary hearing loss (HHL) is the most common sensory deficit, which highly afflicts humans. With gene sequencing technology development, more variants will be identified and support genetic diagnoses, which is difficult for human experts to diagnose. This study aims to develop a machine learning-based genetic diagnosis model of HHL-related variants of GJB2, SLC26A4 and MT-RNR1 . Methods This case-control study included 1898 subjects, among which 1354 were HHL patients and 544 were carriers. Risk assessment models were established based on variants at 144 sites in three genes related to HHL by building six machine learning (ML) models. We compared the ML models with the genetic risk score (GRS) and expert interpretation (EI) to verify the clinical performance. Findings Among the six ML models, the support vector machine (SVM) showed the best performance. For the prediction of HHL-related gene sites in subjects with variants, the area under the receiver operating characteristic (AUC) of the SVM model was 0.803 (0.680–0.814) in the 10-fold stratified cross-validation and 0.751 (0.635–0.779) in external validation. The predicted results were better than both EI and GRS. Furthermore, 11 sites were identified as the smallest feature set that can be accurately predicted. Interpretation The developed SVM model has great potential to be an efficient and effective tool for HHL prediction when high throughput sequencing data are available.

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Section: Discussionmentioning

confidence: 99%

Machine learning-based genetic diagnosis models for hereditary hearing loss by the GJB2, SLC26A4 and MT-RNR1 variants

Luo

et al. 2021

eBioMedicine

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“…Another study constructed a large database ( n = 2,420,330) to analyzed the impact of diverse noise to the generation of NIHL using ANN but unraveled the unsatisfactory performance with less than 65% accuracy, which was no better than LR model (Kim et al 2011 ). The accuracies of some algorithms were also investigated in several studies which either tried to predict hearing loss with specific etiologies, such as sudden hearing loss (Bing et al 2018 ; Park et al 2020 ), ototoxic hearing loss (Tomiazzi et al 2019 ) and cochlear dead regions (Chang et al 2019 ), or predict SNHL by specific auditory measures, such as OAE (de Waal et al 2002 ; Liu et al 2020 ; Ziavra et al 2004 ) and ABR (Acır et al 2006 ; Molina et al 2016 ). Similarly, five studies did not evaluate or describe the significance of input to cochlear dead regions (Chang et al 2019 ; de Waal et al 2002 ; Liu et al 2020 ; Tomiazzi et al 2019 ; Ziavra et al 2004 ).…”

Section: Discussionmentioning

confidence: 99%

“…The accuracies of some algorithms were also investigated in several studies which either tried to predict hearing loss with specific etiologies, such as sudden hearing loss (Bing et al 2018 ; Park et al 2020 ), ototoxic hearing loss (Tomiazzi et al 2019 ) and cochlear dead regions (Chang et al 2019 ), or predict SNHL by specific auditory measures, such as OAE (de Waal et al 2002 ; Liu et al 2020 ; Ziavra et al 2004 ) and ABR (Acır et al 2006 ; Molina et al 2016 ). Similarly, five studies did not evaluate or describe the significance of input to cochlear dead regions (Chang et al 2019 ; de Waal et al 2002 ; Liu et al 2020 ; Tomiazzi et al 2019 ; Ziavra et al 2004 ). Therefore, the validity of the accuracy metric is highly dependent on the transparency of model development and input selection.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, different studies also applied more than one model to predict other types of hearing loss or hearing-related problems. Three types of algorithms achieved good performance in predicting sudden hearing loss (Bing et al 2018 ; Park et al 2020 ), ototoxic hearing loss (Tomiazzi et al 2019 ) and/or SNHL with specific risk factors (Chang et al 2019 ). Also, several studies successfully applied ANN or SVM to clarify different types of SNHL based on the morphology of OAE (de Waal et al 2002 ; Liu et al 2020 ; Ziavra et al 2004 ) or ABR (Acır et al 2006 ).…”

Section: Discussionmentioning

confidence: 99%

“…Machine learning (ML) has been widely applied to automatically identify inter-correlations between data that would normally require a great deal of manpower and be difficult to define manually (McKearney and MacKinnon 2019 ). The application of ML to the field of Audiology has shown promise, because of its effectiveness in analyzing non-linear relationships between data such as predicting hearing thresholds of patients who are exposed to specific risk factors (Chang et al 2019 ). Abdollahi et al ( 2018 ) constructed eight ML models to predict sensorineural hearing loss (SNHL) after chemoradiotherapy, of which five had over 70% accuracies and precisions.…”

Section: Introductionmentioning

confidence: 99%

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Contributions and limitations of using machine learning to predict noise-induced hearing loss

Chen

Cao

Grais

et al. 2021

Int Arch Occup Environ Health

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Purpose Noise-induced hearing loss (NIHL) is a global issue that impacts people’s life and health. The current review aims to clarify the contributions and limitations of applying machine learning (ML) to predict NIHL by analyzing the performance of different ML techniques and the procedure of model construction. Methods The authors searched PubMed, EMBASE and Scopus on November 26, 2020. Results Eight studies were recruited in the current review following defined inclusion and exclusion criteria. Sample size in the selected studies ranged between 150 and 10,567. The most popular models were artificial neural networks (n = 4), random forests (n = 3) and support vector machines (n = 3). Features mostly correlated with NIHL and used in the models were: age (n = 6), duration of noise exposure (n = 5) and noise exposure level (n = 4). Five included studies used either split-sample validation (n = 3) or ten-fold cross-validation (n = 2). Assessment of accuracy ranged in value from 75.3% to 99% with a low prediction error/root-mean-square error in 3 studies. Only 2 studies measured discrimination risk using the receiver operating characteristic (ROC) curve and/or the area under ROC curve. Conclusion In spite of high accuracy and low prediction error of machine learning models, some improvement can be expected from larger sample sizes, multiple algorithm use, completed reports of model construction and the sufficient evaluation of calibration and discrimination risk.

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Automated hearing impairment diagnosis using machine‐learning: An open‐source software development undergraduate research project

Taylor,

Sheikh

2024

Comp Applic In Engineering

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Approximately 700 million people will have disabling hearing loss by 2050. Underdeveloped and developing countries, which encompass a considerable proportion of people with disabling hearing impairment, have a sparse number of audiologists and otolaryngologists. The lack of specialists leaves most hearing impairments undiagnosed for a long time, resulting in negative societal and economic impacts. In this article, we propose an automated hearing impairment diagnosis software—based on machine learning—to support audiologists and otolaryngologists in accurately and efficiently diagnosing and classifying hearing loss. We present the design, implementation, and performance analysis of an open‐source automated hearing impairment diagnosis software, which consists of two modules: a hearing test data‐generation module and a machine‐learning model. The data‐generation module produces a diverse and exhaustive data set for training and evaluating the machine‐learning model. By employing multiclass and ultilabel classification techniques to learn from the hearing test data, the model can swiftly predict the type, degree, and configuration of hearing loss with high reliability. Our proposed machine‐learning model demonstrates promising results with a prediction time of 634 ms, a log‐loss reduction rate of 0.9848 and accuracy, precision, recall, and f1‐score of 1.0000—showing the model's applicability to assist audiologists and otolaryngologists in rapidly and accurately classifying the type, degree, and configuration of hearing loss. In addition to the technical contributions, this article also highlights the importance of involving undergraduate students in open‐source software development projects which have a direct impact on improving the quality of human life.

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Predicting cochlear dead regions in patients with hearing loss through a machine learning-based approach: A preliminary study

Cited by 19 publications

References 33 publications

Machine learning-based genetic diagnosis models for hereditary hearing loss by the GJB2, SLC26A4 and MT-RNR1 variants

Machine learning-based genetic diagnosis models for hereditary hearing loss by the GJB2, SLC26A4 and MT-RNR1 variants

Contributions and limitations of using machine learning to predict noise-induced hearing loss

Automated hearing impairment diagnosis using machine‐learning: An open‐source software development undergraduate research project

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