College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China.
College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China; Key Laboratory of signal detection and processing, Xinjiang University, Urumqi 830046, Xinjiang, China.
Photodiagnosis Photodyn Ther. 2021 Sep;35:102382. doi: 10.1016/j.pdpdt.2021.102382. Epub 2021 Jun 6.
Hyperthyroidism and hypothyroidism may cause a series of clinical complications have a high incidence, and early diagnosis is beneficial to treatment. Based on Raman spectroscopy and deep learning algorithms, we propose a rapid screening method to distinguish serum samples of hyperthyroidism patients, hypothyroidism patients and control subjects. We collected 99 serum samples, including 38 cases from hyperthyroidism patients, 32 cases from hypothyroidism patients and 29 cases from control subjects. By comparing and analyzing the Raman spectra of the three, we found differences in the peak intensity of the spectra, indicating that Raman spectra can be used for the subsequent identification of diseases. After collecting the spectral data, Vancouver Raman algorithm (VRA) was used to remove the fluorescence background of the data, and kernel principal component analysis (KPCA) was used to extract the spectral data features with a cumulative explained variance ratio of 0.9999. Then, five neural network models, the adjusted AlexNet, LSTM-CNN, IndRNNCNN, the adjusted GoogLeNet and the adjusted ResNet, were constructed for classifications. The total accuracy was 91%, 84%, 82%, 75% and 71% respectively. The results of our study show that it is feasible to use Raman spectroscopy combined with deep learning to distinguish hyperthyroidism, hypothyroidism and control subjects. After comparing the models, we found that as the neural network deepens and the complexity of the model increases, the classification effect of Raman spectroscopy gradually deteriorates, and we put forward three conjectures for this.
甲状腺功能亢进症和甲状腺功能减退症可能会导致一系列临床并发症，发病率较高，早期诊断有利于治疗。基于拉曼光谱和深度学习算法，我们提出了一种快速筛选方法，以区分甲状腺功能亢进症患者、甲状腺功能减退症患者和对照组的血清样本。我们收集了 99 份血清样本，其中 38 份来自甲状腺功能亢进症患者，32 份来自甲状腺功能减退症患者，29 份来自对照组。通过比较和分析这三组的拉曼光谱，我们发现光谱的峰强度存在差异，表明拉曼光谱可用于后续疾病的识别。在收集光谱数据后，使用温哥华拉曼算法 (VRA) 去除数据的荧光背景，并使用核主成分分析 (KPCA) 提取光谱数据特征，累积解释方差比为 0.9999。然后，构建了五个神经网络模型，即调整后的 AlexNet、LSTM-CNN、IndRNNCNN、调整后的 GoogLeNet 和调整后的 ResNet，用于分类。总准确率分别为 91%、84%、82%、75%和 71%。我们的研究结果表明，使用拉曼光谱结合深度学习来区分甲状腺功能亢进症、甲状腺功能减退症和对照组是可行的。在比较模型后，我们发现随着神经网络的加深和模型复杂度的增加，拉曼光谱的分类效果逐渐恶化，我们为此提出了三个假设。
