Machine learning throws away the buckets to better understand red blood cell quality

What is this research about?

In Canada, red blood cells for transfusion are stored in the refrigerator at 1-6°C for up to 42 days, after which they are discarded. During storage, the cells change as they metabolize and age. This leads to accumulated degradation of their function and safety, which is seen as the cells change shape from a smooth disc to spiky sphere then a smooth sphere. This red blood cell “storage lesion” can be analyzed by laboratory tests. For example, cell shape is usually measured by experts who prepare the cells, spread them on a slide, look at them using a microscope and categorize their shape according to standard definitions, which places each cell into one of six shape sub-classes or “buckets”. These data are used to give a “morphology index” (MI) for the red blood cells. Using this traditional method, the loss of quality of red blood cells during storage has been very well characterized by researchers. However, the method is complex, time- and labour-intensive, prone to subjective bias, and limited by small sample sizes. The researchers aimed to address these limitations and come up with better methods to assess cell quality using label-free imaging and deep convoluted neural network learning algorithms.

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