Once outside the body, stored blood begins degrading until, by day 42, they’re no longer usable. Until now, assessing its quality has required laborious microscopic examination by human experts. A new study reveals two methodologies that combine machine learning and state-of-the-art imaging to automate the process and eliminate human bias. If standardized, it could ensure more consistent, accurate assessments, with increased efficiency and better patient outcomes.
Machines rival expert analysis of stored red blood cell quality
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