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Machines rival expert analysis of stored red blood cell quality: New ai strategies automate assessments of stored blood, remove human subjectivity:

Each year, nearly 120 million units^* of donated blood flow from donor veins into storage bags at collection centres around the world. The fluid is packed, processed and reserved for later use. But once outside the body, stored red blood cells (RBCs) undergo continuous deterioration. By day 42 in most countries, the products are no longer usable.

For years, labs have used expert microscopic examinations to assess the quality of stored blood. How viable is a unit by day 24? How about day 37? Depending on what technicians' eyes perceive, answers may vary. This manual process is laborious, complex and subjective.

Now, after three years of research, a study published in the Proceedings of the National Academy of Sciences unveils two new strategies to automate the process and achieve objective RBC quality scoring -- with results that match and even surpass expert assessment.

The methodologies showcase the potential in combining artificial intelligence with state-of-the-art imaging to solve a longstanding biomedical problem. If standardized, it could ensure more consistent, accurate assessments, with increased efficiency and better patient outcomes.

[...] "People used to ask what the alternative is to the manual process," says Kolios. "Now, we've developed an approach that integrates cutting-edge developments from several disciplines, including computational biology, transfusion medicine, and medical physics. It's a testament to how technology and science are now interconnecting to solve today's biomedical problems."

*Data reported by the World Health Organization

Journal Reference:
Minh Doan, et. al. Objective Assessment of Stored Blood Quality by Deep Learning. PNAS, 2020 DOI: 10.1073/pnas.2001227117

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