UCLA researchers have developed a*new laser-based tech**logy to rapidly screen blood samples for the presence of Cancer cells. The label-free system measures 16 different physical characteristics of each cell and analyzes the data to identify whether the cell is cancerous. **t having to introduce any labeling chemicals and being gentle on the cells, the technique leaves the cells alive and available for further inspection using other means.
It relies on a Photonic Time Stretch microscope and a computer that runs deep learning Artificial Intelligence algorithms. The microscope can take millions of images per second thanks to unusual optics that produce*high quality shots*even at this speed. The deep learning system can actually run a variety of algorithms and the researchers tested a few to see which are better than others at spotting Cancer cells.
From the study abstract in Scientific Reports:
Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including Artificial neural network, support vector machine, logistic regression, and a **vel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon Cancer cells, as well as lipid accumulating algal strains for biofuel production. This system opens up a new path to data-driven phe**typic diag**sis and better understanding of the heterogeneous gene expressions in cells.
04-16-2016, 02:19 PM
Photonic Time Stretch Microscopy Combined with Artificial Intelligence Spots Cancer C
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