Characteristics of wideband tympanometry in patients with Ménière's disease based on neutral network
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摘要: 目的 基于神经网络构建宽频声导抗对梅尼埃病的预测模型，并评价其预测性能。方法 纳入64例确诊梅尼埃病患者，内耳钆增强磁共振造影均提示患耳膜迷路积水，同时选取40例正常青年人作为对照。分析梅尼埃病患者和正常青年人的宽频声导抗测试结果，采用MATLAB 2021a软件建立神经网络模型，通过准确率、阳性预测值、阴性预测值、约登指数、灵敏度、特异度、受试者工作特征曲线及曲线下面积(AUC)评估模型的性能。结果 成功建立了用宽频声导抗预测梅尼埃病的单隐层前馈神经网络模型，其输入层有104个特征值，隐层有13个神经元节点，输出层有1个输出神经元。该模型准确率83.2%，阳性预测值为80.7%，阴性预测值为84.3%，灵敏度为76.5%，特异度为83.7%，约登指数为0.602，模型AUC为0.855。结论 基于神经网络，根据宽频声导抗测试结果构建了准确率较高的梅尼埃病预测模型，为诊断梅尼埃病提供了参考。Abstract: Objective To construct a prediction model for Ménière's disease based on neural network and evaluate its prediction ability.Methods Sixty-four patients with Ménière's disease underwent gadolinium enhanced magnetic resonance imaging of inner ear which showed endolymphatic hydrops. Meanwhile, 40 healthy adults were enrolled as controls. The database of wideband tympanometry of patients and control subjects was analyzed, and the neural network model was established by MATLAB 2021a software. The prediction ability of the model was evaluated by accuracy, positive predictive value, negative predictive value, the Youden index, sensitivity, specificity, receiver operating characteristic curve and area under curve (AUC).Results A feedforward network model was built with a single hidden layer to predict Ménière's disease with wideband tympanometry. There were 104 features in the input layer, 13 neuron nodes in the hidden layer and 1 output neuron in the output layer. The accuracy of the model was 83.2%, the positive predictive value was 80.7%, the negative predictive value was 84.3%, the sensitivity was 76.5%, the specificity was 83.7%, the Youden index was 0.602, and the AUC was 0.855.Conclusion Based on neural network, the prediction model of Ménière's disease with high accuracy was constructed according to the results of wideband tympanometry, which provided reference for the diagnose of Ménière's disease.
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