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Bypass Surgery or Stenting for Left Main Coronary Artery Disease in Patients With Diabetes Intravascular ultrasound-guided implantation of drug-eluting stents to improve outcome: a meta-analysis In vivo intravascular ultrasound-derived thin-cap fibroatheroma detection using ultrasound radiofrequency data analysis Heart Failure With Preserved Ejection Fraction in the Young Non-obstructive High-Risk Plaques Increase the Risk of Future Culprit Lesions Comparable to Obstructive Plaques Without High-Risk Features: The ICONIC Study Treatment strategies for coronary in-stent restenosis: systematic review and hierarchical Bayesian network meta-analysis of 24 randomised trials and 4880 patients The pyruvate-lactate axis modulates cardiac hypertrophy and heart failure Left Main Revascularization With PCI or CABG in Patients With Chronic Kidney Disease: EXCEL Trial Patient Selection and Clinical Outcomes in the STOPDAPT-2 Trial: An All-Comer Single-Center Registry During the Enrollment Period of the STOPDAPT-2 Randomized Controlled Trial Use of Intravascular Ultrasound Imaging in Percutaneous Coronary Intervention to Treat Left Main Coronary Artery Disease
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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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