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A Meta-Analysis of Contemporary Lesion Modification Strategies During Percutaneous Coronary Intervention in 244,795 Patients From 22 Studies Drug-Coated Balloon Treatment for Femoropopliteal Artery Disease: The IN.PACT Global Study De Novo In-Stent Restenosis Imaging Cohort Coronary Artery Calcium Is Associated with Left Ventricular Diastolic Function Independent of Myocardial Ischemia Can the Vanishing Stent Reappear? Fix the Technique, or Fix the Device? Aggressive lipid-lowering therapy after percutaneous coronary intervention – for whom and how? Rotational Atherectomy Followed by Drug-Coated Balloon Dilation for Left Main In-Stent Restenosis in the Setting of Acute Coronary Syndrome Complicated with Right Coronary Chronic Total Occlusion SGLT2 Inhibitors in Patients With Heart Failure With Reduced Ejection Fraction: A Meta-Analysis of the EMPEROR-Reduced and DAPA-HF Trials Disrupting Fellow Education Through Group Texting: WhatsApp in Fellow Education? AIM2-driven inflammasome activation in heart failure Coronary Angiography after Cardiac Arrest — The Right Timing or the Right Patients?

Review ArticleVolume 12, Issue 14, July 2019

JOURNAL:JACC Cardiovasc Interv. Article Link

Impact of Artificial Intelligence on Interventional Cardiology: From Decision-Making Aid to Advanced Interventional Procedure Assistance

P Sardar, JD Abbott, A Kundu et al. Keywords: artificial intelligence; interventional cardiology

ABSTRACT


Access to big data analyzed by supercomputers using advanced mathematical algorithms (i.e., deep machine learning) has allowed for enhancement of cognitive output (i.e., visual imaging interpretation) to previously unseen levels and promises to fundamentally change the practice of medicine. This field, known as “artificial intelligence” (AI), is making significant progress in areas such as automated clinical decision making, medical imaging analysis, and interventional procedures, and has the potential to dramatically influence the practice of interventional cardiology. The unique nature of interventional cardiology makes it an ideal target for the development of AI-based technologies designed to improve real-time clinical decision making, streamline workflow in the catheterization laboratory, and standardize catheter-based procedures through advanced robotics. This review provides an introduction to AI by highlighting its scope, potential applications, and limitations in interventional cardiology.