Artificial Intelligence and Coronary Artery Bypass Grafting: Current Status and Future Perspectives

Abstract

Artificial intelligence (AI) is revolutionizing the field of coronary artery bypass grafting (CABG) by enhancing various stages of the surgical process. Pre-operatively, AI, particularly through machine learning (ML) and language processing (LP), assists in consultations, medical diagnostics, and clinical predictions. ML models analyze patient data to predict outcomes and stratify risks, while LP automates the documentation of patient interactions, improving efficiency and reducing recall bias. Intra-operatively, computer vision (CV) plays a crucial role in improving surgical performance and team dynamics. CV can automate surgical checklists, assist surgeons by providing real-time feedback, and enhance procedural accuracy. It also aids in instrument tracking and situational awareness, contributing to better team coordination and reduced intraoperative errors. These applications are particularly beneficial for surgical trainees, offering guidance and improving their technique through real-time analysis. Post-operatively, AI continues to support patient care by predicting complications and optimizing recovery plans. ML models assess the risk of post-operative complications, such as major bleeding, myocardial infarction, and acute kidney injury, based on pre-operative characteristics. This enables personalized patient management and targeted interventions to mitigate risks. Additionally, CV can streamline post-operative processes by monitoring patient turnover and improving operating room efficiency. Despite its potential, the integration of AI in CABG faces challenges, including model overfitting, lack of transparency, and high implementation costs. Ethical considerations, such as patient privacy and data security, must be addressed to ensure responsible AI use. Future research should focus on validating AI applications in real-world settings and exploring their impact on minimally invasive techniques and overall surgical outcomes.

Ähnliche Arbeiten