Classification of chemically modified red blood cells in microflow using machine learning video analysis
Classification of chemically modified red blood cells in microflow using machine learning video analysis
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DOI：
10.1039/d3sm01337e
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发表时间：
2023-12-04
期刊：
影响因子：
3.4
通讯作者：
Franke，T.
中科院分区：
文献类型：
--
作者：
Baskaran，R. K. Rajaram;Link，A.;Franke，T.
我们使用基于AI的视频分类器对天然和化学修饰的红细胞进行分类。使用TensorFlow视频分析，我们不仅可以捕获细胞的形态，还可以捕获单个红细胞的运动轨迹及其动态。我们以三种不同的方式对细胞进行化学修饰，以模拟不同的病理条件，并在天然细胞和修饰细胞之间获得超过90%的所有三个分类任务的分类准确率。与基于免疫分型的标准细胞仪不同，我们的微流控细胞仪可以通过分析红细胞的形状和流动来快速分类细胞，而无需任何荧光标记。
We classify native and chemically modified red blood cells with an AI based video classifier. Using TensorFlow video analysis enables us to capture not only the morphology of the cell but also the trajectories of motion of individual red blood cells and their dynamics. We chemically modify cells in three different ways to model different pathological conditions and obtain classification accuracies for all three classification tasks of more than 90% between native and modified cells. Unlike standard cytometers that are based on immunophenotyping our microfluidic cytometer allows to rapidly categorize cells without any fluorescence labels simply by analysing the shape and flow of red blood cells.