Typing of acute leukemia by intelligent optical time-stretch imaging flow cytometry on a chip
Yueyun Weng, Hui Shen, Liye Mei, Li Liu, Yifan Yao, Rubing Li, Shubin Wei, Ruopeng Yan, Xiaolan Ruan, Du Wang, Yongchang Wei, Yunjie Deng, Yuqi Zhou, Ting‐Hui Xiao, Keisuke Goda, Sheng Liu, Fuling Zhou, Cheng Lei
Abstract
in a label-free manner. Then, to show the clinical utility of our method for which the features of clinical samples are diverse, we design and construct a deep convolutional neural network (CNN) to analyze the cellular images and determine the AL type of each sample. We measure 30 clinical samples composed of 7 acute lymphoblastic leukemia (ALL) samples, 17 acute myelogenous leukemia (AML) samples, and 6 samples from healthy donors, resulting in a total of 227 620 images acquired. Results show that our method can distinguish ALL and AML with an accuracy of 95.03%, which, to the best of our knowledge, is a record in label-free AL typing. In addition to AL typing, we believe that the high throughput, high accuracy, and label-free operation of our method make it a potential solution for cell analysis in scientific research and clinical settings.