Characterizing and sorting cells based on image information at record high-throughput rates by integrating a novel ultrafast imaging technique with artificial intelligence.
How It Works
A) Image acquisition
Image information of each cell is recorded as a compressed temporal waveform with a single pixel detector as the cell passes through an illumination pattern projected onto a microchannel.
A trained AI model predicts the cell class based on the waveform.
The classified cells are gently isolated using fluid pressure.
Core technology: Ghost Cytometry (GC) – Machine Learning-based Sorting
2D image information is collected as cells pass through a structured illumination via proprietary optic designs
… and recorded as a compressed 1D temporal waveform “Ghost Motion Imaging” (GMI)
GMI waveforms are directly analyzed by machine learning-based models for high-speed analysis and sorting, bypassing image reconstruction (first for classifier training then actual analysis of samples)
Target cells (both live and fixed) are gently collected using fluid pressure
Workflow of supervised
The workflow outlines key steps involved in developing a machine learning model in ViCS technology.
Image information of each cell is acquired as a compressed temporal waveform using the novel imaging technique. The acquired waveforms are then labeled using biomarkers or other molecular labels.
A machine-learning model is developed based on the labeled waveforms.
3In Silico Labeling
The developed machine-learning model predicts labels by evaluating the waveforms of cells. This machine-learning approach enables image inference at an unprecedented speed.