Fig. 2
Quantitative label-free detection of CML cells. (A) The experimental method of the quantitative label-free detection by GC was illustrated. The classification AI model was pre-trained by the waveform signals obtained from cells using GC. The mixture samples of the two cells were then evaluated by the pre-trained AI model to predict the ratio of each cell in the sample. (B) The correlation between the AI-predicted K562 ratio and actual mixing ratio (10–90%) of K562 in each sample. Three different experiments were performed to obtain the training and testing data, respectively. The data were shown in average ± SD. The r value was evaluated by Spearman’s rank correlation coefficient. (C) Quantitative label-free detection of CML patient leukocytes by GC. Mixture samples of the leukocytes from CML patients and healthy individual (10–90%) were evaluated by the pretrained AI model to calculate the ratio of CML cells in the samples. Leukocytes were divided into WBCs, granulocytes, and lymphocytes, and samples from three different patients were evaluated. F1 score indicates the classification accuracy of the healthy or CML leukocytes. The r value was evaluated by Spearman’s rank correlation coefficient. CML chronic myeloid leukemia, AI artificial intelligence, GC ghost cytometry, WBCs white blood cells.
