Physics Department, Islamia College University Peshawar, KPK, Pakistan; Agri. & Biophotonics Division, National Institute of Lasers and Optronics (NILOP), Lehtrar road, Islamabad, Pakistan.
Agri. & Biophotonics Division, National Institute of Lasers and Optronics (NILOP), Lehtrar road, Islamabad, Pakistan.
Photodiagnosis Photodyn Ther. 2018 Dec;24:286-291. doi: 10.1016/j.pdpdt.2018.10.014. Epub 2018 Oct 22.
We present the effectiveness of Raman spectroscopy (RS) in combination with machine learning for screening and analysis of blood sera collected from tuberculosis patients. Blood samples of 60 patients have confirmed active pulmonary tuberculosis and 14 samples of healthy age matched control were used in the current study. Spectra from entire sera samples were acquired using 785 nm laser Raman system. Support Vector Machine (SVM) together with Principal Component Analysis (PCA) has been used for highlighting variations spectral intensities between healthy and pathological samples. SVM model using Gaussian radial basis is able to discriminate between healthy and diseased patients based on the differences in the concentration of essential biomolecules such as lactate, β-carotene, and amide-I. Diagnostic accuracy of 92%, with precision, specificity and sensitivity of 95%, 98% and 81%, respectively, were achieved considering PC3 and PC4. Automatic analysis of the variations in the concentration of these molecules together with chemometrics can effectively be utilized for an early screening of tuberculosis through minimum invasion.
我们展示了拉曼光谱（RS）与机器学习相结合在筛查和分析来自肺结核患者的血清中的效果。本研究使用了 60 名确诊为活动性肺结核的患者血液样本和 14 名年龄匹配的健康对照样本。使用 785nm 激光拉曼系统获得了整个血清样本的光谱。支持向量机（SVM）与主成分分析（PCA）一起用于突出健康和病理样本之间的光谱强度变化。基于乳酸盐、β-胡萝卜素和酰胺-I 等必需生物分子浓度的差异，使用高斯径向基的 SVM 模型能够区分健康患者和患病患者。考虑到 PC3 和 PC4，诊断准确率达到 92%，精度、特异性和敏感性分别为 95%、98%和 81%。通过最小的侵入性，这些分子浓度变化的自动分析与化学计量学的结合可以有效地用于结核病的早期筛查。
