论文标题
拉曼光谱和基于机器学习的光学传感器，用于通过痰快速结核病诊断
Raman Spectroscopy and Machine Learning-based Optical Sensor for Rapid Tuberculosis Diagnosis via Sputum
论文作者
论文摘要
结核病（TB）是一种传染性疾病，每年在全球造成150万人死亡。结核病患者的早期诊断对于控制其扩散至关重要。但是，标准结核病诊断测试（例如痰培养物）需要数天到几周才能产生结果。在这里，我们演示了基于痰液样品以进行结核病检测的快速，便携，易于使用和非侵入性光学传感器。该探针使用拉曼光谱法检测患者痰液上清液中的结核病。我们在获得的拉曼数据上部署了机器学习算法，主成分分析（PCA），以增强检测灵敏度和特异性。在测试112名潜在结核病患者时，我们的结果表明，对真实阳性的开发探针的准确性为100％，对真（真）为93.4％。此外，探针正确识别了结核病药物的患者。我们预计我们的工作将导致一个可行且快速的结核病诊断平台。
Tuberculosis (TB) is a contagious disease that causes 1.5 million deaths per year globally. Early diagnosis of TB patients is critical to control its spread. However, standard TB diagnostic tests such as sputum culture take days to weeks to produce results. Here, we demonstrate a quick, portable, easy-to-use, and non-invasive optical sensor based on sputum samples for TB detection. The probe uses Raman spectroscopy to detect TB in a patient's sputum supernatant. We deploy a machine-learning algorithm, principal component analysis (PCA), on the acquired Raman data to enhance the detection sensitivity and specificity. On testing 112 potential TB patients, our results show that the developed probe's accuracy is 100% for true-positive and 93.4% for true-negative. Moreover, the probe correctly identifies patients on TB medication. We anticipate that our work will lead to a viable and rapid TB diagnostic platform.
