Artificial intelligence finds cancer cells more efficiently


By using a laser at nanosecond speeds, in combination with deep learning algorithms, a new microscope detects cancer cells more efficiently than standard methods. Scientists at the University of California, Los Angeles (UCLA) have developed a new technique for identifying cancer cells in blood samples, faster and more accurately than current standard methods. One common approach to testing for cancer involves doctors adding biochemicals to blood samples. These biochemicals attach biological “labels” to cancer cells, which enable instruments to detect and identify them. However, the biochemicals can damage cells and render the samples unusable for future analyses. Other techniques are available that don’t use labelling, but these can be inaccurate, because they only identify cancer cells based on a single physical characteristic. The new technique, demonstrated by the California NanoSystems Institute at UCLA, images cells without destroying them. Not only that, but it can identify up to 16 physical characteristics – including size, granularity and biomass – instead of just one. It combines two components that were invented at UCLA: a photonic time stretch microscope, for rapidly imaging cells in blood samples, and a deep learning program that identifies cancer cells with over 95 percent accuracy. The “photonic time stretch” was invented by Professor Barham Jalali, who holds a patent for this technology, and its use in microscopes is just one of many possible applications. It works by taking pictures of flowing blood cells using laser bursts in the way that a camera uses a flash.;

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