Key result
AI algorithms aim to guide metabolic strategy during CPB to reduce human error.
Why the study?
To develop management algorithms and artificial intelligence systems to support operators in selecting the optimal strategy for metabolic management during cardiopulmonary bypass.
AI-based management algorithms may provide guidance for selecting the best metabolic strategy during cardiopulmonary bypass, potentially reducing human error.
Answers are grounded in this paper and cite their sources.
May guide CPB metabolic management but should not change practice; leaves open prospective validation of AI algorithms.
This article introduces management algorithms to support operators in choosing the best strategy for metabolic management during cardiopulmonary bypass using artificial intelligence systems. We developed algorithms for the identification of the optimal way for assessing metabolic parameters. Different management algorithms for extracorporeal procedures interfaced with metabolic monitoring systems already exist on the market and are applied in clinical practice. These algorithms could provide guidance for selecting the best metabolic strategy with the aim at reducing human error and optimizing management.
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Condello et al. (2021) studied Cardiopulmonary bypass. Artificial intelligence management algorithms was evaluated. Artificial intelligence-based management algorithms were developed to guide metabolic strategy during cardiopulmonary bypass, aiming to reduce human error and optimize care.
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