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

Date:
August 24, 2020
Source:
Ryerson University - Faculty of Science
Summary:
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.



FULL STORY ==========================================================================
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.

Trained machines match expert human assessment The interdisciplinary collaboration spanned five countries, twelve institutions and nineteen
authors, including universities, research institutes, and blood collection centres in Canada, USA, Switzerland, Germany and the UK. The research
was led by computational biologist Anne Carpenter of the Broad Institute
of Harvard and MIT, physicist Michael Kolios of Ryerson University's
Department of Physics, and Jason Acker of the Canadian Blood Services.



==========================================================================
They first investigated whether a neural network could be taught to
"see" in images of RBCs the same six categories of cell degradation as
human experts could. To generate the vast quantity of images required,
imaging flow cytometry played a crucial role. Joseph Sebastian, co-author
and Ryerson undergraduate then working under Kolios, explains.

"With this technique, RBCs are suspended and flowed through the cytometer,
an instrument that takes thousands of images of individual blood cells
per second.

We can then examine each RBC without handling or inadvertently damaging
them, which sometimes happens during microscopic examinations."
The researchers used 40,900 cell images to train the neural networks
on classifying RBCs into the six categories -- in a collection that is
now the world's largest, freely available database of RBCs individually annotated with the various categories of deterioration.

When tested, the machine learning algorithm achieved 77% agreement with
human experts. Although a 23% error rate might sound high, perfectly
matching an expert's judgment in this test is impossible: even human
experts agree only 83% of the time. Thus, this fully-supervised machine learning model could be effective to replace tedious visual examination
by humans with little loss of accuracy.

Even so, the team wondered: could a different strategy push the upper
limits of accuracy further?


========================================================================== Machines surpass human vision, detect cellular subtleties In the study's
second part, the researchers avoided human input altogether and devised
an alternative, "weakly-supervised" deep learning model in which neural networks learned about RBC degradation on their own.

Instead of being taught the six visual categories used by experts, the
machines learned solely by analyzing over one million images of RBCs,
unclassed and ordered only by blood storage duration time. Eventually,
the machines correctly discerned features in single RBCs that correspond
to the descent from healthy to unhealthy cells.

"Allowing the computer to teach itself the progression of stored red blood cells as they degrade is a really exciting development," says Carpenter, "particularly because it can capture more subtle changes in cells that
humans don't recognize." When tested against other relevant tests such
as a biochemical assay, the weakly-supervised trained machines predicted
RBC quality better than the current six-category assessment method used
by experts.

Deep learning strategies: Blood quality and beyond Further training
is still needed before the model is ready for clinical testing, but
the outlook is promising. The fully-supervised machine learning model
could soon automate and streamline the current manual method, minimizing
sample handling, discrepancies and procedural errors in blood quality assessments.

The second, alternative weakly-supervised framework may further eliminate
human subjectivity from the process. Objective, accurate blood quality predictions would allow doctors to better personalize blood products to patients. Beyond stored blood, the time-based deep learning strategy may
be transferable to other applications involving chronological progression,
such as the spread of cancer.

"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

========================================================================== Story Source: Materials provided by
Ryerson_University_-_Faculty_of_Science. Note: Content may be edited
for style and length.


========================================================================== Journal Reference:
1. Minh Doan, Joseph A. Sebastian, Juan C. Caicedo, Stefanie Siegert,
Aline
Roch, Tracey R. Turner, Olga Mykhailova, Ruben N. Pinto, Claire
Mcquin, Allen Goodman, Michael J. Parsons, Olaf Wolkenhauer, Holger
Hennig, Shantanu Singh, Anne Wilson, Jason P. Acker, Paul Rees,
Michael C.

Kolios, and Anne E. Carpenter. Objective Assessment of Stored Blood
Quality by Deep Learning. PNAS, 2020 DOI: 10.1073/pnas.2001227117 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/08/200824165630.htm

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