Identifying cancer based on blood samples can be surprisingly challenging. Often, doctors add chemicals to a sample that can make the cancerous cells visible, but that makes the sample impossible to use in other tests. Other techniques identify cancerous cells based on their abnormal structure, but those take more time (those cells are often rare in a given sample) and can misidentify healthy misshapen cells as cancerous. Now researchers at University of California Los Angeles have developed a technique that combines a special microscope with an artificial intelligence algorithm to non-destructively identify cancer in a sample. Not only can that reduce the time and energy needed to diagnose cancer (and, thus, allow doctors to treat it more quickly), but could also be an asset for the field of precision medicine.

Which are the cancerous cells in this image? With a new technique that combines a microscope and deep learning software, it might be easier than ever to tell the difference. Identifying cancer based on blood samples can be surprisingly challenging. Often, doctors add chemicals to a sample that can make the cancerous cells visible, but that makes the sample impossible to use in other tests. Other techniques identify cancerous cells based on their abnormal structure, but those take more time (those cells are often rare in a given sample) and can misidentify healthy misshapen cells as cancerous.