Can Machine Learning Help Simplify the Measurement of Diastolic Function in Echocardiography?
- 14 July 2021
- journal article
- editorial
- Published by Elsevier BV in JACC: Cardiovascular Imaging
- Vol. 14 (11), 2105-2106
- https://doi.org/10.1016/j.jcmg.2021.06.007
Abstract
No abstract availableThis publication has 12 references indexed in Scilit:
- Machine Learning and the Future of Cardiovascular CareJournal of Invasive Cardiology, 2021
- 2021 ACC/AHA Key Data Elements and Definitions for Heart FailureJournal of Invasive Cardiology, 2020
- Best practices for authors of healthcare-related artificial intelligence manuscriptsnpj Digital Medicine, 2020
- Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklistNature Medicine, 2020
- Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation (PRIME): A ChecklistJACC: Cardiovascular Imaging, 2020
- Video-based AI for beat-to-beat assessment of cardiac functionNature, 2020
- Fully Automated Echocardiogram Interpretation in Clinical PracticeJournal of the American College of Cardiology, 2018
- Recommendations for the Evaluation of Left Ventricular Diastolic Function by Echocardiography: An Update from the American Society of Echocardiography and the European Association of Cardiovascular ImagingJournal of the American Society of Echocardiography, 2016
- U-Net: Convolutional Networks for Biomedical Image SegmentationPublished by Springer Science and Business Media LLC ,2015
- 2013 ACCF/AHA Guideline for the Management of Heart FailureJournal of Invasive Cardiology, 2013