TY - JOUR
T1 - The Application of Artificial Intelligence and Machine Learning in Left Ventricular Assist Device Implantation
T2 - A Systematic Review
AU - Hussein, Usama
AU - Chou, Wing Kiu
AU - Balasubramanian, Abhinav
AU - Rahmatova, Jamolbi
AU - Wilkinson, Lydia
AU - Arjomandi Rad, Arian
AU - Dimarakis, Ioannis
AU - Kourliouros, Antonios
PY - 2025/6/1
Y1 - 2025/6/1
N2 - 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.
AB - 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.
KW - artificial intelligence
KW - LVAD
KW - machine learning
KW - mechanical circulatory support
KW - FAILURE
KW - MODEL
U2 - 10.1111/aor.15025
DO - 10.1111/aor.15025
M3 - (Systematic) Review article
SN - 0160-564X
JO - Artificial Organs
JF - Artificial Organs
ER -