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Novel Transcatheter Mitral Valve Prosthesis for Patients With Severe Mitral Annular Calcification The Tricuspid Annular Plane Systolic Excursion to Systolic Pulmonary Artery Pressure Index: Association With All-Cause Mortality in Patients With Moderate or Severe Tricuspid Regurgitation Outcomes of TTVI in Patients With Pacemaker or Defibrillator Leads: Data From the TriValve Registry Rivaroxaban Is Associated With Higher Rates of Gastrointestinal Bleeding Than Other Direct Oral Anticoagulants: A Nationwide Propensity Score–Weighted Study Prevalence of potential drug-drug interactions in cancer patients treated with oral anticancer drugs Percutaneous Closure of the Left Atrial Appendage Versus Warfarin Therapy for Prevention of Stroke in Patients With Atrial Fibrillation: A Randomised Non-Inferiority Trial Drug-Coated Balloon Angioplasty Versus Drug-Eluting Stent Implantation in Patients With Coronary Stent Restenosis Mathematical modelling of endovascular drug delivery: balloons versus stents Cardio-Oncology Services: rationale, organization, and implementation: A report from the ESC Cardio-Oncology council The Art of SAPIEN 3 Transcatheter Mitral Valve Replacement in Valve-in-Ring and Valve-in-Mitral-Annular-Calcification Procedures
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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.

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