Management algorithms and artificial intelligence systems for cardiopulmonary bypass
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. Differe...
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| Veröffentlicht in: | Perfusion 2022-11, Vol.37 (8), p.765-772 |
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| container_title | Perfusion |
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| creator | Condello, Ignazio Santarpino, Giuseppe Nasso, Giuseppe Moscarelli, Marco Fiore, Flavio Speziale, Giuseppe |
| description | 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. |
| doi_str_mv | 10.1177/02676591211030762 |
| format | Article |
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| ispartof | Perfusion, 2022-11, Vol.37 (8), p.765-772 |
| issn | 0267-6591 1477-111X |
| language | eng |
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| source | SAGE Journals Online |
| subjects | Algorithms Artificial intelligence Error reduction Heart surgery Human error Management Optimization Parameter identification |
| title | Management algorithms and artificial intelligence systems for cardiopulmonary bypass |
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