Key result
AI in cardiac surgery improves preoperative risk assessment and predicts mortality, complications, and length of stay.
Why the study?
Artificial intelligence has emerged as a tool to potentially increase efficiency and efficacy of cardiovascular care and improve clinical outcomes, prompting an overview of its applications in cardiac surgery.
Do artificial intelligence applications improve risk prediction, clinician decision-making, and patient outcomes in cardiac surgery?
Systematic Review
Do artificial intelligence applications improve risk prediction, clinician decision-making, and patient outcomes in cardiac surgery?
Artificial intelligence and machine learning models show promise in improving preoperative risk assessment and predicting postoperative complications in cardiac surgery, though further validation is needed before routine clinical integration.
AI shows promise for risk prediction in cardiac surgery but should not yet change practice; leaves open need for prospective validation.
BACKGROUND Artificial intelligence has emerged as a tool to potentially increase efficiency and efficacy of cardiovascular care and improve clinical outcomes. This study aims to provide an overview of applications of artificial intelligence in cardiac surgery. METHODS A systematic literature search on artificial intelligence applications in cardiac surgery from inception to February 2024 was conducted. Articles were then filtered based on the inclusion and exclusion criteria and risk of bias was assessed. Key findings were then summarized RESULTS A total of 81 studies were found that reported on artificial intelligence applications in cardiac surgery. There is a rapid rise in studies since 2020. The most popular machine learning technique was Random Forest (n=48), followed by Support Vector Machine (n=33), Logistic Regression (n=32), and Extreme Gradient Boosting (n=31). Most of the studies were on adult patients, conducted in China, and involved procedures such as valvular surgery (24.7%), heart transplant (9.4%), coronary revascularization (11.8%), congenital heart disease surgery (3.5%), and aortic dissection repair (2.4%). Regarding evaluation outcomes, 35 studies examined the performance, 26 studies examined clinician outcomes, and 20 studies examined patient outcomes. CONCLUSION Artificial intelligence was mainly used to predict complications following cardiac surgeries and improve clinicians’ decision-making by providing better preoperative risk assessment, stratification, and prognostication. While the application of artificial intelligence in cardiac surgery has greatly progressed in the last decade, further studies need to be done to verify accuracy and ensure safety before use in clinical practice.
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Sulague et al. (2023) conducted a systematic review in Cardiac surgery. Artificial intelligence applications was evaluated on Performance of AI applications, clinician outcomes, and patient outcomes. Artificial intelligence applications in cardiac surgery demonstrated utility in predicting mortality, postoperative length of stay, and complications, while improving preoperative risk assessment.
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