Research on noise-induced hearing loss based on functional and structural MRI using machine learning methods
Abstract Noise-induced hearing loss (NIHL) is a common occupational condition. The aim of this study was to develop a classification model for NIHL on the basis of both functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI) by applying machine learning methods....
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-87168-4 |
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| author | Minghui Lv Liping Wang Ranran Huang Aijie Wang Yunxin Li Guowei Zhang |
| author_facet | Minghui Lv Liping Wang Ranran Huang Aijie Wang Yunxin Li Guowei Zhang |
| author_sort | Minghui Lv |
| collection | DOAJ |
| description | Abstract Noise-induced hearing loss (NIHL) is a common occupational condition. The aim of this study was to develop a classification model for NIHL on the basis of both functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI) by applying machine learning methods. fMRI indices such as the amplitude of low-frequency fluctuation (ALFF), fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), degree of centrality (DC), and sMRI indices such as gray matter volume (GMV), white matter volume (WMV), and cortical thickness were extracted from each brain region. The least absolute shrinkage and selection operator was used to reduce and select the optimal features. The support vector machine (SVM), random forest (RF) and logistic regression (LR) algorithms, were used to establish the classification model for NIHL. Finally, the SVM model based on combined fMRI indices, achieved the best performance, with area under the receiver operating characteristic curve of 0.97 and an accuracy of 95%. The SVM classification model that integrates fMRI indicators has the greatest potential for identifying NIHL patients and healthy people, revealing the complementary role of fMRI indicators in classification and indicating that it is necessary to include multiple indicators of the brain when establishing a classification model. |
| format | Article |
| id | doaj-art-9091510e42b44c63a9982f9b70d40dd1 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-9091510e42b44c63a9982f9b70d40dd12025-01-26T12:25:14ZengNature PortfolioScientific Reports2045-23222025-01-0115111510.1038/s41598-025-87168-4Research on noise-induced hearing loss based on functional and structural MRI using machine learning methodsMinghui Lv0Liping Wang1Ranran Huang2Aijie Wang3Yunxin Li4Guowei Zhang5Imaging Department, Yantaishan HospitalImaging Department, Yantaishan HospitalImaging Department, Yantaishan HospitalImaging Department, Yantaishan HospitalImaging Department, Yantaishan HospitalImaging Department, Yantaishan HospitalAbstract Noise-induced hearing loss (NIHL) is a common occupational condition. The aim of this study was to develop a classification model for NIHL on the basis of both functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI) by applying machine learning methods. fMRI indices such as the amplitude of low-frequency fluctuation (ALFF), fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), degree of centrality (DC), and sMRI indices such as gray matter volume (GMV), white matter volume (WMV), and cortical thickness were extracted from each brain region. The least absolute shrinkage and selection operator was used to reduce and select the optimal features. The support vector machine (SVM), random forest (RF) and logistic regression (LR) algorithms, were used to establish the classification model for NIHL. Finally, the SVM model based on combined fMRI indices, achieved the best performance, with area under the receiver operating characteristic curve of 0.97 and an accuracy of 95%. The SVM classification model that integrates fMRI indicators has the greatest potential for identifying NIHL patients and healthy people, revealing the complementary role of fMRI indicators in classification and indicating that it is necessary to include multiple indicators of the brain when establishing a classification model.https://doi.org/10.1038/s41598-025-87168-4Noise-induced hearing lossFunctional magnetic resonance imagingStructural magnetic resonance imagingMachine learning |
| spellingShingle | Minghui Lv Liping Wang Ranran Huang Aijie Wang Yunxin Li Guowei Zhang Research on noise-induced hearing loss based on functional and structural MRI using machine learning methods Scientific Reports Noise-induced hearing loss Functional magnetic resonance imaging Structural magnetic resonance imaging Machine learning |
| title | Research on noise-induced hearing loss based on functional and structural MRI using machine learning methods |
| title_full | Research on noise-induced hearing loss based on functional and structural MRI using machine learning methods |
| title_fullStr | Research on noise-induced hearing loss based on functional and structural MRI using machine learning methods |
| title_full_unstemmed | Research on noise-induced hearing loss based on functional and structural MRI using machine learning methods |
| title_short | Research on noise-induced hearing loss based on functional and structural MRI using machine learning methods |
| title_sort | research on noise induced hearing loss based on functional and structural mri using machine learning methods |
| topic | Noise-induced hearing loss Functional magnetic resonance imaging Structural magnetic resonance imaging Machine learning |
| url | https://doi.org/10.1038/s41598-025-87168-4 |
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