Lv Minghui, Wang Liping, Huang Ranran, Wang Aijie, Li Yunxin, Zhang Guowei
Imaging Department, Yantaishan Hospital, Yantai, China.
Sci Rep. 2025 Jan 26;15(1):3289. doi: 10.1038/s41598-025-87168-4.
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.
噪声性听力损失（NIHL）是一种常见的职业性疾病。本研究的目的是通过应用机器学习方法，基于功能磁共振成像（fMRI）和结构磁共振成像（sMRI）开发一种NIHL分类模型。从每个脑区提取fMRI指标，如低频波动幅度（ALFF）、低频波动分数幅度（fALFF）、局部一致性（ReHo）、中心度（DC），以及sMRI指标，如灰质体积（GMV）、白质体积（WMV）和皮质厚度。使用最小绝对收缩和选择算子来减少和选择最优特征。采用支持向量机（SVM）、随机森林（RF）和逻辑回归（LR）算法建立NIHL分类模型。最后，基于联合fMRI指标的SVM模型表现最佳，受试者工作特征曲线下面积为0.97，准确率为95%。整合fMRI指标的SVM分类模型在识别NIHL患者和健康人方面具有最大潜力，揭示了fMRI指标在分类中的互补作用，并表明在建立分类模型时纳入大脑的多个指标是必要的。
