Evaluation of the Weaning Process in Patients Undergoing VA-ECMO Using Artificial Intelligence Based Approaches

Gülşah Çelik Korhan

doi:10.5281/zenodo.15775992

Authors

Gülşah Çelik Korhan Harran Üniversitesi https://orcid.org/0009-0001-2949-6287

DOI:

https://doi.org/10.5281/zenodo.15775992

Keywords:

Veno-arterial ECMO, Cardiogenic shock, Artificial intelligence, Mortality risk prediction, Weaning

Abstract

Veno-arterial extracorporeal membrane oxygenation (VA-ECMO) is widely used as a temporary life support strategy to maintain organ perfusion in patients experiencing severe cardiac dysfunction. However, the discontinuation of this support known as the weaning process plays a critical role in patient outcomes, with its timing and success prediction representing a complex challenge in clinical decision-making. The limited predictive power of conventional methods has led clinicians to seek more advanced analytical techniques. At this point, artificial intelligence (AI) algorithms offer a novel perspective due to their ability to analyze multivariate clinical data, their learning capabilities, and high predictive performance. In particular, machine learning-based models have shown promise in enabling the development of early warning systems with high accuracy in patient monitoring. Recent studies suggest that AI facilitates data driven and objective insights into the weaning process in VA-ECMO patients.

The aim of this review is to examine current AI-based approaches developed to predict weaning success in patients receiving VA-ECMO support, to evaluate their applicability in clinical decision support systems, and to provide a comprehensive overview of the literature in this field.

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