Artificial Intelligence in Cardiac Surgery: A Systematic Review

Abstract

BACKGROUND Artificial intelligence has emerged as a tool to potentially increase efficiency and efficacy of healthcare and improve clinical outcomes. The growing body of knowledge of artificial intelligence applications in cardiac surgery necessitates evaluation of past studies to gain insights to the future direction of artificial intelligence applications in cardiac surgery. This study aims to provide a systematic review of the applications of artificial intelligence in cardiac surgery. METHODS A systematic literature search on artificial intelligence applications in cardiac surgery from 2000 to 2022 was conducted in the following databases: PubMed, Embase, Europe PMC, Epistemonikos, CINAHL, Cochrane Central, Google Scholar, Web of Science, Scopus, Cambridge Core, clinicaltrials.gov, and science. Studies on the implementation of artificial intelligence applications in cardiac surgery and the provision of decision support by the application through simulating clinical decision-making processes of healthcare providers were included. Studies not in English, published only as abstracts, review papers, meta-analyses, clinical trials that were still in progress, and published study protocols were excluded. This study was registered on Prospero (CRD42022377530). RESULTS A total of 42 studies were found that reported on artificial intelligence applications in cardiac surgery, all of which are cohort studies. Nine (21.43%) of the studies measured different parameters regarding cardiac surgeries in general. Meanwhile, 6 (14.29%) studies focused on Heart Transplantation (HT), 4 (9.52%) on Transcatheter Aortic Valve Replacement (TAVR), 3 (7.14%) anchored on Aortic Stenosis, and another 3 (7.14%) on Perioperative Complications. Three topics had 2 (4.76%) studies dedicated to them, namely Coronary Artery Bypass Graft (CABG), Postoperative Atrial Fibrillation (POAF), and Acute Kidney Injury (AKI). The remaining eleven studies have their own unique disease topics, procedures or surgeries in focus (n=11, 1 (2.38%), namely Postoperative Major Bleeding, Early Coronary Revascularization, Heart Valve Surgery, Isolated Mitral Valve Replacement (IMVR), Surgical Aortic Valve Replacement (SAVR), Open-Chest Surgery, Infective endocarditis, Post-Operative Deterioration, Red Blood Cell Transfusion, AKI - related Hippocampal Damage, and Open-Heart Surgery. Regarding evaluation outcomes, 26 studies examined the performance, 32 studies examined clinician outcomes, and 2 studies examined patient outcomes. Of the 42 studies, only 13 were conducted in Lower- and Middle-Income Countries. CONCLUSION Artificial intelligence was used to predict mortality, postoperative length of stay, and complications following cardiac surgeries. It can also improve clinicians’ medical decisions by providing better preoperative risk assessment, stratification, and prognostication. While the application of artificial intelligence in cardiac surgery has greatly progressed in the last two decades, more highly powered studies need to be done to assess challenges and to ensure accuracy and safety for use in clinical practice.