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Long-Term Outcomes of Biodegradable Versus Second-Generation Durable Polymer Drug-Eluting Stent Implantations for Myocardial Infarction Effect of Aspirin on All-Cause Mortality in the Healthy Elderly Heart Failure With Preserved, Borderline, and Reduced Ejection Fraction: 5-Year Outcomes Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT Successful catheter ablation of electrical storm after myocardial infarction ST-Segment Elevation Myocardial Infarction Patients in the Coronary Care Unit Is it Time to Break Old Habits? 2015 ESC Guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation: Task Force for the Management of Acute Coronary Syndromes in Patients Presenting without Persistent ST-Segment Elevation of the European Society of Cardiology (ESC) Mortality 10 Years After Percutaneous or Surgical Revascularization in Patients With Total Coronary Artery Occlusions The spectrum of chronic coronary syndromes: genetics, imaging, and management after PCI and CABG Randomized Comparison of Ridaforolimus-Eluting and Zotarolimus-Eluting Coronary Stents 2-Year Clinical Outcomes: From the BIONICS and NIREUS Trials
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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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