Real‐time intelligent classification of COVID‐19 and thrombosis via massive image‐based analysis of platelet aggregates
Real‐time intelligent classification of COVID‐19 and thrombosis via massive image‐based analysis of platelet aggregates
复制标题
通过基于大规模图像的血小板聚集体分析对 COVID-19 和血栓形成进行实时智能分类
DOI：
10.1002/cyto.a.24721
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发表时间：
2023
期刊：
影响因子：
3.7
通讯作者：
Goda Keisuke
中科院分区：
文献类型：
--
作者：
Zhang Chenqi;Herbig Maik;Zhou Yuqi;Nishikawa Masako;Shifat‐E‐Rabbi Mohammad;Kanno Hiroshi;Yang Ruoxi;Ibayashi Yuma;Xiao Ting‐Hui;Rohde Gustavo K.;Sato Masataka;Kodera Satoshi;Daimon Masao;Yatomi Yutaka;Goda Keisuke
微血管血栓形成是COVID-19的典型症状，与血栓形成相似。使用微流控成像流式细胞仪，我们测量了181个COVID-19样本和101个非COVID-19血栓形成样本的血液，总共产生了630万张明场图像。我们训练了一个卷积神经网络来区分单个血小板、血小板聚集体和白色血细胞，并分别对每个亚群进行了经典图像分析。基于每个群体的衍生单细胞特征，我们训练了机器学习模型，用于COVID-19和非COVID-19血栓形成之间的分类，从而使患者测试准确率达到75%。这一结果表明，COVID-19和非COVID-19血栓形成之间的血小板形成不同。所有的分析步骤都经过了效率优化，并在图像查看器napari的易于使用的插件中实现，允许在中档计算机上在几秒钟内执行整个分析，可用于真实的实时诊断。
Microvascular thrombosis is a typical symptom of COVID‐19 and shows similarities to thrombosis. Using a microfluidic imaging flow cytometer, we measured the blood of 181 COVID‐19 samples and 101 non‐COVID‐19 thrombosis samples, resulting in a total of 6.3 million bright‐field images. We trained a convolutional neural network to distinguish single platelets, platelet aggregates, and white blood cells and performed classical image analysis for each subpopulation individually. Based on derived single‐cell features for each population, we trained machine learning models for classification between COVID‐19 and non‐COVID‐19 thrombosis, resulting in a patient testing accuracy of 75%. This result indicates that platelet formation differs between COVID‐19 and non‐COVID‐19 thrombosis. All analysis steps were optimized for efficiency and implemented in an easy‐to‐use plugin for the image viewer napari, allowing the entire analysis to be performed within seconds on mid‐range computers, which could be used for real‐time diagnosis.