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Fractional flow reserve in clinical practice: from wire-based invasive measurement to image-based computation Therapeutic efficacy of paclitaxel-coated balloon for de novo coronary lesions with diameters larger than 2.8 mm One Versus 2-stent Strategy for the Treatment of Bifurcation Lesions in the Context of a Coronary Chronic Total Occlusion: A Multicenter Registry Angiography Alone Versus Angiography Plus Optical Coherence Tomography to Guide Percutaneous Coronary Intervention: Outcomes From the Pan-London PCI Cohort Fractional flow reserve derived from computed tomography coronary angiography in the assessment and management of stable chest pain: the FORECAST randomized trial Comparison of Coronary Computed Tomography Angiography, Fractional Flow Reserve, and Perfusion Imaging for Ischemia Diagnosis Coronary fractional flow reserve in bifurcation stenoses: what have we learned? Pulmonary Artery Denervation: A New, Long-Awaited Interventional Treatment for Combined Pre- and Post-Capillary Pulmonary Hypertension? Myocardial Blood Flow and Coronary Flow Reserve During 3 Years Following Bioresorbable Vascular Scaffold Versus Metallic Drug-Eluting Stent Implantation: The VANISH Trial Prospective, large-scale multicenter trial for the use of drug-coated balloons in coronary lesions: The DCB-only All-Comers Registry
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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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