Artificial intelligence guidance of advanced heart failure therapies: A systematic scoping review

作者：Mohammad A. Al‐Ani, Chen Bai, Amal Hashky, Alex Michael Parker, Juan R. Vilaro, Juan M. Aranda, Benjamin Shickel, Parisa Rashidi, Azra Bihorac, Mustafa M. Ahmed, Mamoun Mardini · 发表于：Frontiers in Cardiovascular Medicine · 年份：2023 · DOI：10.3389/fcvm.2023.1127716 · 被引用次数：16 · 研究领域：Mechanical Circulatory Support Devices、Transplantation: Methods and Outcomes、Artificial Intelligence in Healthcare and Education

Introduction: Artificial intelligence can recognize complex patterns in large datasets. It is a promising technology to advance heart failure practice, as many decisions rely on expert opinions in the absence of high-quality data-driven evidence. Methods: We searched Embase, Web of Science, and PubMed databases for articles containing "artificial intelligence," "machine learning," or "deep learning" and any of the phrases "heart transplantation," "ventricular assist device," or "cardiogenic shock" from inception until August 2022. We only included original research addressing post heart transplantation (HTx) or mechanical circulatory support (MCS) clinical care. Review and data extraction were performed in accordance with PRISMA-Scr guidelines. Results: = 3, respectively). One study addressed temporary mechanical circulatory support. Most studies advocated for rapid integration of AI into clinical practice, acknowledging potential improvements in management guidance and reliability of outcomes prediction. There was a notable paucity of external data validation and integration of multiple data modalities. Conclusion: Our review showed mounting innovation in AI application in management of MCS and HTx, with the largest evidence showing improved mortality outcome prediction.