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Reduced Leaflet Motion after Transcatheter Aortic-Valve Replacement 1-Year Outcomes After Edge-to-Edge Valve Repair for Symptomatic Tricuspid Regurgitation: Results From the TriValve Registry Single Versus Dual Antiplatelet Therapy Following TAVR: A Systematic Review and Meta-Analysis of Randomized Controlled Trials Comparison of plaque characteristics in narrowings with ST-elevation myocardial infarction (STEMI), non-STEMI/unstable angina pectoris and stable coronary artery disease (from the ADAPT-DES IVUS Substudy) Reduced Apolipoprotein M and Adverse Outcomes Across the Spectrum of Human Heart Failure Role of intravascular ultrasound in patients with acute myocardial infarction undergoing percutaneous coronary intervention Clinical impact of conduction disturbances in transcatheter aortic valve replacement recipients: a systematic review and meta-analysis Usefulness of intravascular ultrasound to predict outcomes in short-length lesions treated with drug-eluting stents High-Risk Coronary Plaque Regression After Intensive Lifestyle Intervention in Nonbstructive Coronary Disease: A Randomized Study Percutaneous Left Atrial Appendage Closure for Stroke Prophylaxis in Patients With Atrial Fibrillation: 2.3-Year Follow-up of the PROTECT AF (Watchman Left Atrial Appendage System for Embolic Protection in Patients With Atrial Fibrillation) Trial

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.