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Randomized Comparison of FFR-Guided and Angiography-Guided Provisional Stenting of True Coronary Bifurcation Lesions: The DKCRUSH-VI Trial (Double Kissing Crush Versus Provisional Stenting Technique for Treatment of Coronary Bifurcation Lesions VI) Circulating Plasma microRNAs In Systemic Sclerosis-Associated Pulmonary Arterial Hypertension Percutaneous Coronary Intervention For Bifurcation Coronary Lesions.The 15th Consensus Document from the European Bifurcation Club Haemodynamic definitions and updated clinical classification of pulmonary hypertension Utilization and Outcomes of Measuring Fractional Flow Reserve in Patients With Stable Ischemic Heart Disease Neoatherosclerosis in Patients With Coronary Stent Thrombosis: Findings From Optical Coherence Tomography Imaging (A Report of the PRESTIGE Consortium) A new optical coherence tomography-based calcium scoring system to predict stent underexpansion Characteristics of stent thrombosis in bifurcation lesions analysed by optical coherence tomography The impact of downstream coronary stenoses on fractional flow reserve assessment of intermediate left main disease Impact of low tissue backscattering by optical coherence tomography on endothelial function after drug-eluting stent implantation
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Review Article2020 Jul 16;229:1-17.

JOURNAL：Am Heart J . Article Link

Clinical applications of machine learning in the diagnosis, classification, and prediction of heart failure

CR Olsen, RJ Mentz, KJ Anstrom et al. Keywords: machine learning; artificial intelligence;

ABSTRACT

Machine learning and artificial intelligence are generating significant attention in the scientific community and media. Such algorithms have great potential in medicine for personalizing and improving patient care, including in the diagnosis and management of heart failure. Many physicians are familiar with these terms and the excitement surrounding them, but many are unfamiliar with the basics of these algorithms and how they are applied to medicine. Within heart failure research, current applications of machine learning include creating new approaches to diagnosis, classifying patients into novel phenotypic groups, and improving prediction capabilities. In this paper, we provide an overview of machine learning targeted for the practicing clinician and evaluate current applications of machine learning in the diagnosis, classification, and prediction of heart failure.

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