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Quantitative angiography methods for bifurcation lesions: a consensus statement update from the European Bifurcation Club Management of Acute Myocardial Infarction During the COVID-19 Pandemic High-Risk Coronary Atherosclerosis: Is It the Plaque Burden, the Calcium, the Lipid, or Something Else? China PEACE risk estimation tool for in-hospital death from acute myocardial infarction: an early risk classification tree for decisions about fibrinolytic therapy From Nonclinical Research to Clinical Trials and Patient-registries: Challenges and Opportunities in Biomedical Research IVUS Guidance Is Associated With Better Outcome in Patients Undergoing Unprotected Left Main Coronary Artery Stenting Compared With Angiography Guidance Alone How Will the Transition to hs-cTn Affect the Diagnosis of Type 1 and 2 MI? Prognostic Effect and Longitudinal Hemodynamic Assessment of Borderline Pulmonary Hypertension Comparative analysis of recurrent events after presentation with an index myocardial infarction or ischaemic stroke Wearable Cardioverter-Defibrillator Therapy for the Prevention of Sudden Cardiac Death A Systematic Review and Meta-Analysis
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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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