Echocardiography
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What's New
Last Posted: Oct 16, 2020
- Steps to use artificial intelligence in echocardiography.
Kusunose Kenya et al. Journal of echocardiography 2020 Oct - Machine Learning in Electrocardiography and Echocardiography: Technological Advances in Clinical Cardiology.
Chang Amanda et al. Current cardiology reports 2020 Oct 22(12) 161 - A Comparison of Three-Dimensional Speckle Tracking Echocardiography Parameters in Predicting Left Ventricular Remodeling.
Zhong Junda et al. Journal of healthcare engineering 2020 20208847144 - Spectrum of transthyretin gene mutations and clinical characteristics of Polish patients with cardiac transthyretin amyloidosis.
Gawor Monika et al. Cardiology journal 2020 Aug - Machine learning for the diagnosis of pulmonary hypertension.
Zhu Fubao et al. Kardiologiia 2020 Jul 60(6) 953 - Influence of hereditary haemochromatosis on left ventricular wall thickness: does iron overload exacerbate cardiac hypertrophy?
Rozwadowska K et al. Folia morphologica 2019 78(4) 746-753 - Artificial Intelligence in Cardiovascular Imaging.
Lim Lisa J et al. Methodist DeBakey cardiovascular journal 16(2) 138-145 - Biventricular imaging markers to predict outcomes in non-compaction cardiomyopathy: a machine learning study.
Rocon Camila et al. ESC heart failure 2020 Jun - Applying machine learning to detect early stages of cardiac remodelling and dysfunction.
Sabovcik František et al. European heart journal cardiovascular Imaging 2020 Jun - Artificial Intelligence for Dynamic Echocardiographic Tricuspid Valve Analysis: A New Tool in Echocardiography.
Fatima Huma et al. Journal of cardiothoracic and vascular anesthesia 2020 May - Left Ventricular Strain and Progression of Hypertrophy in Danon Disease Cardiomyopathy: Insights from a Global Registry.
Ma G S et al. The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation 2020 Apr 39(4S) S154 - Minimal Patient Clinical Variables to Accurately Predict Stress Echocardiography Outcome: Validation Study Using Machine Learning Techniques.
Bennasar Mohamed et al. JMIR cardio 2020 May 4(1) e16975 - Development of novel machine learning model for right ventricular quantification on echocardiography-A multimodality validation study.
Beecy Ashley N et al. Echocardiography (Mount Kisco, N.Y.) 2020 May - Early Detection of Heart Failure With Reduced Ejection Fraction Using Perioperative Data Among Noncardiac Surgical Patients: A Machine-Learning Approach.
Mathis Michael R et al. Anesthesia and analgesia 2020 May 130(5) 1188-1200 - Improving ultrasound video classification: an evaluation of novel deep learning methods in echocardiography.
Howard James P et al. Journal of medical artificial intelligence 2020 Mar 3
About HLBS-PopOmics
HLBS-PopOmics is an online, continuously updated, searchable database of published scientific literature, CDC and NIH resources, and other materials that address the translation of genomic discoveries into improved health care and prevention related to Heart and Vascular Diseases(H), Lung Diseases(L), Blood Diseases(B), and Sleep Disorders(S)...more
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Site Citation:
Mensah GA, Yu W, Barfield WL, Clyne M, Engelgau MM, Khoury MJ. HLBS-PopOmics: an online knowledge base to accelerate dissemination and implementation of research advances in population genomics to reduce the burden of heart, lung, blood, and sleep disorders. Genet Med. 2018 Sep 10. doi: 10.1038/s41436-018-0118-1
Disclaimer: Articles listed in the Public Health Knowledge Base are selected by the CDC Office of Public Health Genomics to provide current awareness of the literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the update, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.
- Page last reviewed:Oct 1, 2020
- Page last updated:Dec 28, 2020
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