Applications of artificial intelligence/machine learning approaches in cardiovascular medicine: a systematic review with recommendations. Issue 3 (8th June 2021)

• Record Type: Journal Article
• Authors: Friedrich, Sarah; Groß, Stefan; König, Inke R; Engelhardt, Sandy; Bahls, Martin; Heinz, Judith; Huber, Cynthia; Kaderali, Lars; Kelm, Marcus; Leha, Andreas; Rühl, Jasmin; Schaller, Jens; Scherer, Clemens; Vollmer, Marcus; Seidler, Tim; Friede, Tim
• Is Part Of: European heart journal. Volume 2:Issue 3(2021)
• Page Start: 424
• Page End: 436
• Publication Date: 2021-06-08
• Subjects: Artificial intelligence -- Machine learning -- Cardiology -- Cardiovascular disease; Medical informatics -- Periodicals; Medical technology -- Periodicals; Cardiovascular system -- Periodicals; 616.100284
• DOI: 10.1093/ehjdh/ztab054
• Languages: English
• ISSNs: 2634-3916

Abstract

Abstract: Aims: Artificial intelligence (AI) and machine learning (ML) promise vast advances in medicine. The current state of AI/ML applications in cardiovascular medicine is largely unknown. This systematic review aims to close this gap and provides recommendations for future applications. Methods and results: Pubmed and EMBASE were searched for applied publications using AI/ML approaches in cardiovascular medicine without limitations regarding study design or study population. The PRISMA statement was followed in this review. A total of 215 studies were identified and included in the final analysis. The majority (87%) of methods applied belong to the context of supervised learning. Within this group, tree-based methods were most commonly used, followed by network and regression analyses as well as boosting approaches. Concerning the areas of application, the most common disease context was coronary artery disease followed by heart failure and heart rhythm disorders. Often, different input types such as electronic health records and images were combined in one AI/ML application. Only a minority of publications investigated reproducibility and generalizability or provided a clinical trial registration. Conclusions: A major finding is that methodology may overlap even with similar data. Since we observed marked variation in quality, reporting of the evaluation and transparency of data and methods urgently need to be improved. Graphical Abstract: