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

Marcelle G M Lopes; Steffen M Recktenwald; Greta Simionato; Hermann Eichler; Christian Wagner; Stephan Quint; Lars Kaestner

doi:10.1159/000530458

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

Transfus Med Hemother. 2023 May 25;50(3):163-173. doi: 10.1159/000530458. eCollection 2023 Jun.

Authors

Marcelle G M Lopes^{1

2}, Steffen M Recktenwald¹, Greta Simionato^{1

3}, Hermann Eichler⁴, Christian Wagner^{1

5}, Stephan Quint², Lars Kaestner^{1

6}

Affiliations

¹ Experimental Physics, Saarland University, Saarbrücken, Germany.
² Cysmic GmbH, Saarbrücken, Germany.
³ Institute for Clinical and Experimental Surgery, Saarland University, Saarbrücken, Germany.
⁴ Institute of Clinical Hemostaseology and Transfusion Medicine, Saarland University, Saarbrücken, Germany.
⁵ Physics and Materials Science Research Unit, University of Luxembourg, Luxembourg City, Luxembourg.
⁶ Theoretical Medicine and Biosciences, Saarland University, Saarbrücken, Germany.

Abstract

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.

Keywords: Erysense; Personalized transfusion; Red blood cell quality; Storage lesion.

Publication types

Review

Grants and funding

This work was supported by the European Framework Horizon 2020 under grant agreement number 860436 (EVIDENCE), the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation, Grant No. WA 1336/13) and the Saarland University through the program “Anschubfinanzierung”. Furthermore, we acknowledge support by the DFG and Saarland University within the funding programme Open Access Publishing.