论文标题
SARS-COV-2基于横向流动设备的图像分析的解释
SARS-CoV-2 Result Interpretation based on Image Analysis of Lateral Flow Devices
论文作者
论文摘要
现在通常用于检测SARS-COV-2的存在的广泛使用的基因定量技术，侧流动器（LFD）。它可以控制和预防病毒传播。根据病毒载荷，LFD具有不同的敏感性和对普通用户的自我测试，提出了额外的挑战以解释结果。随着机器学习算法的发展，图像处理和分析已经实现了前所未有的增长。在这项跨学科研究中，我们采用了计算机视觉和机器学习领域的新型图像分析方法来研究LFD控制区域的视觉特征。在这里，我们会自动为任何包含LFD的图像的结果得出结果，为正，负或不确定。这将减轻人类参与卫生工作者和看法偏见的负担。
The widely used gene quantisation technique, Lateral Flow Device (LFD), is now commonly used to detect the presence of SARS-CoV-2. It is enabling the control and prevention of the spread of the virus. Depending on the viral load, LFD have different sensitivity and self-test for normal user present additional challenge to interpret the result. With the evolution of machine learning algorithms, image processing and analysis has seen unprecedented growth. In this interdisciplinary study, we employ novel image analysis methods of computer vision and machine learning field to study visual features of the control region of LFD. Here, we automatically derive results for any image containing LFD into positive, negative or inconclusive. This will reduce the burden of human involvement of health workers and perception bias.
