{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T01:32:05Z","timestamp":1792805525014,"version":"4.5.0"},"reference-count":40,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,20]],"date-time":"2022-05-20T00:00:00Z","timestamp":1653004800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["81922033"],"award-info":[{"award-number":["81922033"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"crossref"}]},{"name":"National Natural Science Foundation of China","award":["82171964"],"award-info":[{"award-number":["82171964"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"crossref"}]},{"name":"National Natural Science Foundation of China","award":["2021CFA046"],"award-info":[{"award-number":["2021CFA046"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"crossref"}]},{"name":"Key Research and Development Program of Hubei","award":["81922033"],"award-info":[{"award-number":["81922033"]}]},{"name":"Key Research and Development Program of Hubei","award":["82171964"],"award-info":[{"award-number":["82171964"]}]},{"name":"Key Research and Development Program of Hubei","award":["2021CFA046"],"award-info":[{"award-number":["2021CFA046"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>The accurate assessment of left ventricular systolic function is crucial in the diagnosis and treatment of cardiovascular diseases. Left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS) are the most critical indexes of cardiac systolic function. Echocardiography has become the mainstay of cardiac imaging for measuring LVEF and GLS because it is non-invasive, radiation-free, and allows for bedside operation and real-time processing. However, the human assessment of cardiac function depends on the sonographer\u2019s experience, and despite their years of training, inter-observer variability exists. In addition, GLS requires post-processing, which is time consuming and shows variability across different devices. Researchers have turned to artificial intelligence (AI) to address these challenges. The powerful learning capabilities of AI enable feature extraction, which helps to achieve accurate identification of cardiac structures and reliable estimation of the ventricular volume and myocardial motion. Hence, the automatic output of systolic function indexes can be achieved based on echocardiographic images. This review attempts to thoroughly explain the latest progress of AI in assessing left ventricular systolic function and differential diagnosis of heart diseases by echocardiography and discusses the challenges and promises of this new field.<\/jats:p>","DOI":"10.3390\/jcm11102893","type":"journal-article","created":{"date-parts":[[2022,5,20]],"date-time":"2022-05-20T13:56:12Z","timestamp":1653054972000},"page":"2893","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Artificial Intelligence-Enhanced Echocardiography for Systolic Function Assessment"],"prefix":"10.3390","volume":"11","author":[{"given":"Zisang","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China"},{"name":"Clinical Research Center for Medical Imaging in Hubei Province, Wuhan 430022, China"},{"name":"Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ye","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China"},{"name":"Clinical Research Center for Medical Imaging in Hubei Province, Wuhan 430022, China"},{"name":"Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manwei","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China"},{"name":"Clinical Research Center for Medical Imaging in Hubei Province, Wuhan 430022, China"},{"name":"Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziming","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China"},{"name":"Clinical Research Center for Medical Imaging in Hubei Province, Wuhan 430022, China"},{"name":"Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China"},{"name":"Clinical Research Center for Medical Imaging in Hubei Province, Wuhan 430022, China"},{"name":"Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Yang","sequence":"additional","affiliation":[{"name":"Media and Communication Lab (MC Lab), Electronics and Information Engineering Department, Huazhong University of Science and Technology, Wuhan 430022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingxing","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China"},{"name":"Clinical Research Center for Medical Imaging in Hubei Province, Wuhan 430022, China"},{"name":"Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1620-1512","authenticated-orcid":false,"given":"Li","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China"},{"name":"Clinical Research Center for Medical Imaging in Hubei Province, Wuhan 430022, China"},{"name":"Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3599","DOI":"10.1093\/eurheartj\/ehab368","article-title":"2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure","volume":"42","author":"McDonagh","year":"2021","journal-title":"Eur. Heart J."},{"key":"ref_2","first-page":"e895","article-title":"2022 AHA\/ACC\/HFSA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology\/American Heart Association Joint Committee on Clinical Practice Guidelines","volume":"145","author":"Heidenreich","year":"2022","journal-title":"Circulation"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1093\/ehjci\/jev014","article-title":"Recommendations for cardiac chamber quantification by echocardiography in adults: An update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging","volume":"16","author":"Lang","year":"2015","journal-title":"Eur. Heart J. Cardiovasc. Imaging"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1636","DOI":"10.1002\/ejhf.1326","article-title":"Markers of left ventricular systolic dysfunction when left ventricular ejection fraction is normal","volume":"20","author":"Nauta","year":"2018","journal-title":"Eur. J. Heart Fail."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.jcmg.2017.11.017","article-title":"Assessment of Left Ventricular Function by Echocardiography: The Case for Routinely Adding Global Longitudinal Strain to Ejection Fraction","volume":"11","author":"Potter","year":"2018","journal-title":"JACC Cardiovasc. Imaging"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"S48","DOI":"10.1038\/d41586-019-03845-1","article-title":"How Artificial Intelligence Will Change Medicine","volume":"576","author":"Wallis","year":"2019","journal-title":"Nature"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1549","DOI":"10.1016\/j.jcmg.2019.06.009","article-title":"State-of-the-Art Deep Learning in Cardiovascular Image Analysis","volume":"12","author":"Litjens","year":"2019","journal-title":"JACC Cardiovasc. Imaging"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1061","DOI":"10.1016\/j.echo.2020.04.025","article-title":"Artificial Intelligence and Echocardiography: A Primer for Cardiac Sonographers","volume":"33","author":"Davis","year":"2020","journal-title":"J. Am. Soc. Echocardiogr."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2058","DOI":"10.1093\/eurheartj\/ehz056","article-title":"Deep learning for cardiovascular medicine: A practical primer","volume":"40","author":"Krittanawong","year":"2019","journal-title":"Eur. Heart J."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.1016\/j.jcmg.2018.11.038","article-title":"Automated Quantification in Echocardiography","volume":"12","author":"Nolan","year":"2019","journal-title":"JACC Cardiovasc. Imaging"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.media.2016.10.007","article-title":"Automatic apical view classification of echocardiograms using a discriminative learning dictionary","volume":"36","author":"Khamis","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1038\/s41746-018-0065-x","article-title":"Deep echocardiography: Data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease","volume":"1","author":"Madani","year":"2018","journal-title":"NPJ Digit. Med."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1038\/s41746-017-0013-1","article-title":"Fast and accurate view classification of echocardiograms using deep learning","volume":"1","author":"Madani","year":"2018","journal-title":"NPJ Digit. Med."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1001\/jamacardio.2021.0185","article-title":"Utility of a Deep-Learning Algorithm to Guide Novices to Acquire Echocardiograms for Limited Diagnostic Use","volume":"6","author":"Narang","year":"2021","journal-title":"JAMA Cardiol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2198","DOI":"10.1109\/TMI.2019.2900516","article-title":"Deep Learning for Segmentation Using an Open Large-Scale Dataset in 2D Echocardiography","volume":"38","author":"Leclerc","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Smistad, E., Salte, I.M., Ostvik, A., Leclerc, S., Bernard, O., and Lovstakken, L. (2019, January 6\u20139). Segmentation of apical long axis, four-and two-chamber views using deep neural networks. Proceedings of the IEEE International Ultrasonics Symposium, Glasgow, UK.","DOI":"10.1109\/ULTSYM.2019.8926017"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2519","DOI":"10.1109\/TUFFC.2020.3003403","article-title":"LU-Net: A Multistage Attention Network to Improve the Robustness of Segmentation of Left Ventricular Structures in 2-D Echocardiography","volume":"67","author":"Leclerc","year":"2020","journal-title":"IEEE Trans. Ultrason. Ferroelectr. Freq. Control."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1038\/s41586-020-2145-8","article-title":"Video-based AI for beat-to-beat assessment of cardiac function","volume":"580","author":"Ouyang","year":"2020","journal-title":"Nature"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"e009303","DOI":"10.1161\/CIRCIMAGING.119.009303","article-title":"Automated Echocardiographic Quantification of Left Ventricular Ejection Fraction without Volume Measurements Using a Machine Learning Algorithm Mimicking a Human Expert","volume":"12","author":"Asch","year":"2019","journal-title":"Circ. Cardiovasc. Imaging"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1623","DOI":"10.1161\/CIRCULATIONAHA.118.034338","article-title":"Fully Automated Echocardiogram Interpretation in Clinical Practice","volume":"138","author":"Zhang","year":"2018","journal-title":"Circulation"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1918","DOI":"10.1016\/j.jcmg.2021.04.018","article-title":"Artificial Intelligence for Automatic Measurement of Left Ventricular Strain in Echocardiography","volume":"14","author":"Salte","year":"2021","journal-title":"JACC Cardiovasc. Imaging"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.jacc.2020.11.030","article-title":"Machine Learning and the Future of Cardiovascular Care: JACC State-of-the-Art Review","volume":"77","author":"Quer","year":"2021","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1119","DOI":"10.1016\/j.jcmg.2019.12.018","article-title":"Interpatient Similarities in Cardiac Function: A Platform for Personalized Cardiovascular Medicine","volume":"13","author":"Tokodi","year":"2020","journal-title":"JACC Cardiovasc. Imaging"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2287","DOI":"10.1016\/j.jacc.2016.08.062","article-title":"Machine-Learning Algorithms to Automate Morphological and Functional Assessments in 2D Echocardiography","volume":"68","author":"Narula","year":"2016","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e004330","DOI":"10.1161\/CIRCIMAGING.115.004330","article-title":"Cognitive Machine-Learning Algorithm for Cardiac Imaging: A Pilot Study for Differentiating Constrictive Pericarditis From Restrictive Cardiomyopathy","volume":"9","author":"Sengupta","year":"2016","journal-title":"Circ. Cardiovasc. Imaging"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zhu, H., Chen, Y., Yang, C., Cheng, H., Li, Y., Zhong, W., and Wang, F. (2021). Ensemble machine learning approach for screening of coronary heart disease based on echocardiography and risk factors. BMC Med. Inform. Decis. Mak., 21.","DOI":"10.1186\/s12911-021-01535-5"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wei, H., Cao, H., Cao, Y., Zhou, Y., Xue, W., Ni, D., and Li, S. (2020, January 29). Temporal-consistent Segmentation of Echocardiography with Co-learning from Appearance and Shape. Proceedings of the Medical Image Computing and Computer Assisted Intervention, Lima, Peru.","DOI":"10.1007\/978-3-030-59713-9_60"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Reynaud, H., Vlontzos, A., Hou, B., Beqiri, A., Leeson, P., and Kainz, B. (2021). Ultrasound Video Transformers for Cardiac Ejection Fraction Estimation. arXiv.","DOI":"10.1007\/978-3-030-87231-1_48"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"e46","DOI":"10.1016\/S2589-7500(21)00235-1","article-title":"Automated interpretation of systolic and diastolic function on the echocardiogram: A multicohort study","volume":"4","author":"Tromp","year":"2022","journal-title":"Lancet. Digit. Health"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1593","DOI":"10.1056\/NEJMra1916062","article-title":"Point-of-Care Ultrasonography","volume":"385","author":"Mayo","year":"2021","journal-title":"N. Engl. J. Med."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"e012293","DOI":"10.1161\/CIRCIMAGING.120.012293","article-title":"Deep Learning-Based Automated Echocardiographic Quantification of Left Ventricular Ejection Fraction: A Point-of-Care Solution","volume":"14","author":"Asch","year":"2021","journal-title":"Circ. Cardiovasc. Imaging"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1615","DOI":"10.1002\/ejhf.1330","article-title":"Innovative imaging methods in heart failure: A shifting paradigm in cardiac assessment. Position statement on behalf of the Heart Failure Association of the European Society of Cardiology","volume":"20","author":"Plymen","year":"2018","journal-title":"Eur. J. Heart Fail."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.echo.2020.09.011","article-title":"Feasibility, Reproducibility, and Clinical Implications of the Novel Fully Automated Assessment for Global Longitudinal Strain","volume":"34","author":"Kawakami","year":"2021","journal-title":"J. Am. Soc. Echocardiogr. Off. Publ. Am. Soc. Echocardiogr."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1929","DOI":"10.1016\/j.jcmg.2021.05.012","article-title":"Let AI Take the Strain","volume":"14","author":"Leeson","year":"2021","journal-title":"JACC. Cardiovasc. Imaging"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Evain, E., Sun, Y., Faraz, K., Garcia, D., Saloux, E., Gerber, B.L., De Craene, M., and Bernard, O. (2022). Motion estimation by deep learning in 2D echocardiography: Synthetic dataset and validation. IEEE Trans. Med. Imaging.","DOI":"10.1109\/TMI.2022.3151606"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1170","DOI":"10.1016\/j.echo.2021.06.014","article-title":"Automated Pattern Recognition in Whole-Cardiac Cycle Echocardiographic Data: Capturing Functional Phenotypes with Machine Learning","volume":"34","author":"Loncaric","year":"2021","journal-title":"J. Am. Soc. Echocardiogr. Off. Publ. Am. Soc. Echocardiogr."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2424","DOI":"10.1016\/j.ultrasmedbio.2020.03.002","article-title":"Strain Curve Classification Using Supervised Machine Learning Algorithm with Physiologic Constraints","volume":"46","author":"Yahav","year":"2020","journal-title":"Ultrasound Med. Biol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"19450","DOI":"10.1038\/s41598-021-98773-4","article-title":"Cardiac involvement in hospitalized patients with COVID-19 and its incremental value in outcomes prediction","volume":"11","author":"Pournazari","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"604","DOI":"10.1016\/j.echo.2018.12.002","article-title":"Contribution of Cardiovascular Reserve to Prognostic Categories of Heart Failure With Preserved Ejection Fraction: A Classification Based on Machine Learning","volume":"32","author":"Marwick","year":"2019","journal-title":"J. Am. Soc. Echocardiogr. Off. Publ. Am. Soc. Echocardiogr."}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/11\/10\/2893\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:15:21Z","timestamp":1760138121000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/11\/10\/2893"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,20]]},"references-count":40,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["jcm11102893"],"URL":"https:\/\/doi.org\/10.3390\/jcm11102893","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,20]]}}}