痰
肺结核
拉曼光谱
医学
结核病诊断
诊断试验
诊断准确性
结核分枝杆菌
人工智能
内科学
病理
计算机科学
光学
兽医学
物理
作者
Ubaid Ullah,Zarfishan Tahir,Obaidullah Qazi,Shaper Mirza,M. Imran Cheema
出处
期刊:Tuberculosis
[Elsevier]
日期:2022-09-01
卷期号:136: 102251-102251
被引量:5
标识
DOI:10.1016/j.tube.2022.102251
摘要
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.
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