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首页> 外文期刊>Laser Physics: An International Journal devoted to Theoretical and Experimental Laser Research and Application >Laser Raman detection of platelets for early and differential diagnosis of Alzheimer's disease based on an adaptive Gaussian process classification algorithm
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Laser Raman detection of platelets for early and differential diagnosis of Alzheimer's disease based on an adaptive Gaussian process classification algorithm

机译：基于自适应高斯过程分类算法的血小板拉曼激光检测，用于阿尔茨海默氏病的早期和鉴别诊断

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摘要

Early and differential diagnosis of Alzheimer's disease (AD) has puzzled many clinicians. In this work, laser Raman spectroscopy (LRS) was developed to diagnose AD from platelet samples from AD transgenic mice and non-transgenic controls of different ages. An adaptive Gaussian process (GP) classification algorithm was used to re-establish the classification models of early AD, advanced AD and the control group with just two features and the capacity for noise reduction. Compared with the previous multilayer perceptron network method, the GP showed much better classification performance with the same feature set. Besides, spectra of platelets isolated from AD and Parkinson's disease (PD) mice were also discriminated. Spectral data from 4 month AD (n = 39) and 12 month AD (n = 104) platelets, as well as control data (n = 135), were collected. Prospective application of the algorithm to the data set resulted in a sensitivity of 80%, a specificity of about 100% and a Matthews correlation coefficient of 0.81. Samples from PD (n = 120) platelets were also collected for differentiation from 12 month AD. The results suggest that platelet LRS detection analysis with the GP appears to be an easier and more accurate method than current ones for early and differential diagnosis of AD.
机译：阿尔茨海默氏病（AD）的早期和鉴别诊断使许多临床医生感到困惑。在这项工作中，开发了激光拉曼光谱（LRS），以从来自AD转基因小鼠和不同年龄的非转基因对照的血小板样本中诊断AD。使用自适应高斯过程（GP）分类算法来重建早期AD，高级AD和对照组的分类模型，该模型仅具有两个功能和降噪能力。与以前的多层感知器网络方法相比，GP在具有相同功能集的情况下显示出更好的分类性能。此外，还区分了从AD和帕金森氏病（PD）小鼠分离的血小板的光谱。收集来自AD 4个月（n = 39）和AD 12个月（n = 104）血小板的光谱数据以及对照数据（n = 135）。该算法在数据集上的预期应用导致了80％的灵敏度，约100％的特异性和Matthews相关系数0.81。还收集了来自PD（n = 120）血小板的样品，用于从12个月的AD分化。结果表明，使用GP进行血小板LRS检测分析似乎比当前的方法更容易，更准确，可用于AD的早期和鉴别诊断。

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