SynthesisArtificial organs2025
The Application of Artificial Intelligence and Machine Learning in Left Ventricular Assist Device Implantation: A Systematic Review.
Synthesis in Artificial organs, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
8 authors.
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Abstract
backgroundThis systematic review evaluates the current evidence pertaining to the application of artificial intelligence (AI) and machine learning (ML) in left ventricular assist device (LVAD) implantation. Specifically, the potential of AI/ML in risk stratification, predicting complications, and improving patient outcomes is explored, whereas also identifying key challenges and elucidating avenues of future research.
methodsA comprehensive search was conducted across EMBASE, MEDLINE, Cochrane, PubMed, and Google Scholar databases to identify studies on AI/ML in LVAD implantation up to March 2024. Articles were selected if they utilized AI/ML techniques in LVAD settings and met predefined criteria. A total of 17 studies were included after a rigorous screening and appraisal process.
resultsThe included studies highlighted the use of ML in five main areas: (1) mortality prediction, where ML models demonstrated higher accuracy compared to traditional models; (2) adverse event prediction, including aortic regurgitation and suction events; (3) myocardial recovery, with ML models outperforming traditional stratification methods; (4) deciphering thrombosis risk, with ML identifying key predictors such as younger age and higher BMI; and (5) right ventricular failure prognostication, within which ML models leveraged hemodynamic and imaging data for superior prediction accuracy. Despite such prevalent advances, challenges including data heterogeneity, lack of causality, and limited generalizability persist.
conclusionAI and ML possess transformative potential in optimizing LVAD management, offering both advanced prediction of commonly encountered risk occurrence and personalized care respectively. However, identified issues in AI/ML application, including data interpretability, dataset diversity, and integration into clinical workflows, must be addressed in order to enhance their broader adoption and impact.
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