Automated Analysis of Doppler Echocardiographic Videos as a Screening Tool for Valvular Heart Diseases

Chung, 2008, Results of the Predictors of Response to CRT (PROSPECT) trial, Circulation, 117, 2608, 10.1161/CIRCULATIONAHA.107.743120 Narula, 2016, Machine-learning algorithms to automate morphological and functional assessments in 2D echocardiography, J Am Coll Cardiol, 68, 2287, 10.1016/j.jacc.2016.08.062 Zhang, 2018, Fully automated echocardiogram interpretation in clinical practice, Circulation, 138, 1623, 10.1161/CIRCULATIONAHA.118.034338 Ghorbani, 2020, Deep learning interpretation of echocardiograms, NPJ Digit Med, 3, 10, 10.1038/s41746-019-0216-8 Beecy, 2020, Development of novel machine learning model for right ventricular quantification on echocardiography---a multimodality validation study, Echocardiography, 37, 688, 10.1111/echo.14674 Sengupta, 2020, Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation (PRIME): a checklist: reviewed by the American College of Cardiology Healthcare Innovation Council, J Am Coll Cardiol Img, 13, 2017, 10.1016/j.jcmg.2020.07.015 Nishimura, 2014, 2014 AHA/ACC guideline for the management of patients with valvular heart disease: executive summary: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines, J Am Coll Cardiol, 63, 2438, 10.1016/j.jacc.2014.02.537 Zoghbi, 2017, Recommendations for noninvasive evaluation of native valvular regurgitation: a report from the American Society of Echocardiography developed in collaboration with the Society for Cardiovascular Magnetic Resonance, J Am Soc Echocardiogr, 30, 303, 10.1016/j.echo.2017.01.007 Huang, 2020, Automated recognition of regional wall motion abnormalities through deep neural network interpretation of transthoracic echocardiography, Circulation, 142, 1510, 10.1161/CIRCULATIONAHA.120.047530 Knackstedt, 2015, Fully automated versus standard tracking of left ventricular ejection fraction and longitudinal strain: the FAST-EFs multicenter study, J Am Coll Cardiol, 66, 1456, 10.1016/j.jacc.2015.07.052 Ouyang, 2020, Video-based AI for beat-to-beat assessment of cardiac function, Nature, 580, 252, 10.1038/s41586-020-2145-8 Moghaddasi, 2016, Automatic assessment of mitral regurgitation severity based on extensive textural features on 2D echocardiography videos, Comput Biol Med, 73, 47, 10.1016/j.compbiomed.2016.03.026 Gaillard, 2010, Optimization of Doppler echocardiographic velocity measurements using an automatic contour detection method, Ultrasound Med Biol, 36, 1513, 10.1016/j.ultrasmedbio.2010.05.021 Kiruthika, 2006, Automated assessment of aortic regurgitation using 2D Doppler echocardiogram, Proceedings of the 2006 IEEE International Workshop on Imagining Systems and Techniques (IST 2006). Minori, Italy. IEEE;, 95, 10.1109/IST.2006.1650783 Kusunose, 2020, Clinically feasible and accurate view classification of echocardiographic images using deep learning, Biomolecules, 10, 665, 10.3390/biom10050665 Ostvik, 2019, Real-time standard view classification in transthoracic echocardiography using convolutional neural networks, Ultrasound Med Biol, 45, 374, 10.1016/j.ultrasmedbio.2018.07.024 Madani, 2018, Fast and accurate view classification of echocardiograms using deep learning, NPJ Digit Med, 10.1038/s41746-017-0013-1