Rare Anemias: Are Their Names Just Smoke and Mirrors?

Simionato, Greta; Wijk, Richard van; Quint, Stephan; Wagner, Christian; Bianchi, Paola; Kaestner, Lars

doi:10.3389/fphys.2021.690604

Cited by 1 publication

(1 citation statement)

References 47 publications

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“…The latter can be exploited in 3D fluorescence microscopy in stasis [57] and flow [58] . As a result, many RBC-related diseases have derived their names from the shape of the RBCs [59] ranging from sickle cell disease [60] to neuroacanthocytosis syndrome [61] . Furthermore, it is known from rheological and ektacytometric cell population measurements that a crucial parameter (also influenced by RBC storage time) is RBC deformability.…”

Section: Toward Single-cell Assays For Rbc Quality Controlmentioning

confidence: 99%

Big Data in Transfusion Medicine and Artificial Intelligence Analysis for Red Blood Cell Quality Control

Lopes¹,

Recktenwald²,

Simionato³

et al. 2023

Transfus Med Hemother

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Background: “Artificial intelligence” and “big data” increasingly take the step from just being interesting concepts to being relevant or even part of our lives. This general statement holds also true for transfusion medicine. Besides all advancements in transfusion medicine, there is not yet an established red blood cell quality measure, which is generally applied. Summary: We highlight the usefulness of big data in transfusion medicine. Furthermore, we emphasize in the example of quality control of red blood cell units the application of artificial intelligence. Key Messages: A variety of concepts making use of big data and artificial intelligence are readily available but still await to be implemented into any clinical routine. For the quality control of red blood cell units, clinical validation is still required.

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