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Optimal Fluoroscopic Projections of Coronary Ostia and Bifurcations Defined by Computed Tomographic Coronary Angiography Comparison of intravascular ultrasound-guided with angiography-guided double kissing crush stenting for patients with complex coronary bifurcation lesions: rationale and design of a prospective, randomized and multicenter DKCRUSH VIII trial Long-term outcomes after treatment of bare-metal stent restenosis with paclitaxel-coated balloon catheters or everolimus-eluting stents: 3-year follow-up of the TIS clinical study Atrial Fibrillation: JACC Council Perspectives Optical coherence tomography-guided percutaneous coronary intervention in ST-segmentelevation myocardial infarction: a prospective propensity-matched cohort of the thrombectomy versus percutaneous coronary intervention alone trial The Natural History of Nonculprit Lesions in STEMI: An FFR Substudy of the Compare-Acute Trial Sex Differences in Instantaneous Wave-Free Ratio or Fractional Flow Reserve–Guided Revascularization Strategy Optical Coherence Tomography to Optimize Results of Percutaneous Coronary Intervention in Patients with Non-ST-Elevation Acute Coronary Syndrome: Results of the Multicenter, Randomized DOCTORS Study (Does Optical Coherence Tomography Optimize Results of Stenting) Physiology-Based Revascularization: A New Approach to Plan and Optimize Percutaneous Coronary Intervention: State-of-the-Art Review Randomized study on simple versus complex stenting of coronary artery bifurcation lesions: the Nordic bifurcation study
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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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