Virus detection and identification in minutes using single-particle imaging and deep learning
Virus detection and identification in minutes using single-particle imaging and deep learning
复制标题
使用单粒子成像和深度学习在几分钟内检测和识别病毒
DOI：
10.1101/2020.10.13.20212035
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Shiaelis N
中科院分区：
文献类型：
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作者：
Shiaelis N
近几十年来，以当前的COVID-19大流行为代表的病毒爆发的频率和规模不断增加，导致对快速和敏感的病毒诊断方法的迫切需求。在这里，我们提出了一种用于病毒检测和识别的方法，该方法使用卷积神经网络来区分不同病毒的单个完整颗粒的显微镜图像。我们的检测方法在不到五分钟的时间内实现标记、成像和病毒鉴定，并且不需要任何裂解、纯化或扩增步骤。经过训练的神经网络能够高准确度地区分SARS-CoV-2与阴性临床样本以及流感和季节性人类冠状病毒等其他常见呼吸道病原体。单粒子成像与深度学习相结合，为传统的病毒诊断方法提供了一种有前途的替代方案，并有可能产生重大影响。
The increasing frequency and magnitude of viral outbreaks in recent decades, epitomized by the current COVID-19 pandemic, has resulted in an urgent need for rapid and sensitive viral diagnostic methods. Here, we present a methodology for virus detection and identification that uses a convolutional neural network to distinguish between microscopy images of single intact particles of different viruses. Our assay achieves labeling, imaging and virus identification in less than five minutes and does not require any lysis, purification or amplification steps. The trained neural network was able to differentiate SARS-CoV-2 from negative clinical samples, as well as from other common respiratory pathogens such as influenza and seasonal human coronaviruses, with high accuracy. Single-particle imaging combined with deep learning offers a promising alternative to traditional viral diagnostic methods, and has the potential for significant impact.