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Prognostic impact of atrial fibrillation in cardiogenic shock complicating acute myocardial infarction: a substudy of the IABP-SHOCK II trial Relationship Between Infarct Size and Outcomes Following Primary PCI: Patient-Level Analysis From 10 Randomized Trials Intravascular ultrasound-guided percutaneous coronary intervention in left main coronary bifurcation lesions: a review Predicting lifetime risk for developing atherosclerotic cardiovascular disease in Chinese population: the China-PAR project Prognostic impact of non-culprit chronic total occlusions in infarct-related cardiogenic shock: results of the randomised IABP-SHOCK II trial Risk of Early Adverse Events After Clopidogrel Discontinuation in Patients Undergoing Short-Term Dual Antiplatelet Therapy: An Individual Participant Data Analysis Risk Stratification for Patients in Cardiogenic Shock After Acute Myocardial Infarction Non-eligibility for reperfusion therapy in patients presenting with ST-segment elevation myocardial infarction: Contemporary insights from the National Cardiovascular Data Registry (NCDR) Intracoronary Optical Coherence Tomography 2018: Current Status and Future Directions Predicting the 10-Year Risks of Atherosclerotic Cardiovascular Disease in Chinese Population: The China-PAR Project (Prediction for ASCVD Risk in China)
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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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