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Optimal threshold of postintervention minimum stent area to predict in-stent restenosis in small coronary arteries: An optical coherence tomography analysis Left main coronary artery compression in pulmonary hypertension Rotational Atherectomy in acute STEMI with heavily calcified culprit lesion is a rule breaking solution Sildenafil added to pirfenidone in patients with advanced idiopathic pulmonary fibrosis and risk of pulmonary hypertension: A Phase IIb, randomised, double-blind, placebo-controlled study - Rationale and study design Developing a Mobile Application for Global Cardiovascular Education Long-term clinical outcome after fractional flow reserve-guided treatment in patients with angiographically equivocal left main coronary artery stenosis Updated clinical classification of pulmonary hypertension Coronary CT Angiographic and Flow Reserve-Guided Management of Patients With Stable Ischemic Heart Disease Treatment of calcified coronary lesions with Palmaz-Schatz stents. An intravascular ultrasound study Comparative efficacy of two paclitaxel-coated balloons with different excipient coatings in patients with coronary in-stent restenosis: A pooled analysis of the Intracoronary Stenting and Angiographic Results: Optimizing Treatment of Drug Eluting Stent In-Stent Restenosis 3 and 4 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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