振荡微通道中红细胞的人工智能分析
AI based analysis of red blood cells in oscillating microchannels
AI总结：
本文提出利用TensorFlow深度学习模型分析振荡微通道中的红细胞形态，以超过90%的准确率区分健康红细胞与经甲醛改变粘弹性的红细胞，为血液学诊断提供非侵入性、无标记的定量表征方法。
摘要：
红细胞在体内毛细血管和体外微流控通道中的流动动力学十分复杂。根据流动条件及其粘弹性特性，细胞可呈现不同的形状，如盘状、降落伞状、拖鞋状以及各种中间状态。我们使用基于人工智能的分析方法，对振荡微通道中的红细胞（RBCs）进行分析，以区分健康红细胞与经甲醛处理以化学改变其粘弹性行为的红细胞。我们使用TensorFlow训练并验证了一个深度学习模型，并取得了超过90%的测试准确率。该方法是迈向对病变红细胞进行非侵入性、无标记表征的第一步，并将有助于血液学实验室的诊断目的。该方法基于单细胞分类提供了受影响细胞数量的定量数据。
英文摘要：
The flow dynamics of red blood cells in vivo in blood capillaries and in vitro in microfluidic channels is complex. Cells can obtain differnet shapes such as discoid, parachute, slipper-like shapes and various intermediate states depending on flow conditions and their viscoelastic properties. We use artificial intelligence based analysis of red blood cells (RBCs) in an oscillating mircorchannel to distinguish healthy red blood cells from red blood cells treated with formaldehyde to chemically modify their viscoelastic behavior. We used TensorFlow to train and validate a deep learning model and achived a testing accuracy of over 90%. This method is a first step to a non-invasive, label-free characterization of diseased red blood cells and will be useful for diagnostic purposes in haematology labs. This method provides quantitative data on the number of affected cells based on single cell classification.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
