Biological matrices, reagents and turnaround-time: the full-circle of artificial intelligence in the pre-analytical Phase. Comment on Turcic A, et al., Machine learning to optimize cerebrospinal fluid dilution for analysis of MRZH reaction. CCLM 2024;62:436

Biological matrices, reagents and turnaround-time: the full-circle of artificial intelligence in the pre-analytical Phase. Comment on Turcic A, et al., Machine learning to optimize cerebrospinal fluid dilution for analysis of MRZH reaction. CCLM 2024;62:436–41

Published:2024-03-20 Issue:9 Volume:62 Page:e215-e217
ISSN:1434-6621
Container-title:Clinical Chemistry and Laboratory Medicine (CCLM)
language:en
Short-container-title:

Author:

Ialongo Cristiano¹^ORCID,Pieri Massimo²^ORCID

Affiliation:

1. Department of Experimental Medicine , 9311 University of Rome La Sapienza , Rome , Italy

2. Department of Experimental Medicine and Surgery , 9318 University of Rome “Tor Vergata” , Rome , Italy

Publisher

Walter de Gruyter GmbH

Link

https://www.degruyter.com/document/doi/10.1515/cclm-2024-0210/pdf

Reference7 articles.

1. Turcic, A, Stajduhar, A, Vogrinc, Z, Zaninovic, L, Rogic, D. Machine learning to optimize cerebrospinal fluid dilution for analysis of MRZH reaction. Clin Chem Lab Med 2024;62:436–41. https://doi.org/10.1515/cclm-2023-1013.

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3. Ialongo, C, Pieri, M, Bernardini, S. Smart management of sample dilution using an artificial neural network to achieve streamlined processes and saving resources: the automated nephelometric testing of serum free light chain as case study. Clin Chem Lab Med 2017;55:231–6. https://doi.org/10.1515/cclm-2016-0263.

4. Ialongo, C, Pieri, M, Bernardini, S. Artificial neural network for total laboratory automation to improve the management of sample dilution. J Lab Autom 2017;22:44–9. https://doi.org/10.1177/2211068216636635.

5. Ialongo, C, Porzio, O, Giambini, I, Bernardini, S. Total automation for the core laboratory: improving the turnaround time helps to reduce the volume of ordered STAT tests. J Lab Autom 2016;21:451–8. https://doi.org/10.1177/2211068215581488.