Literature DB >> 31257314

Utilization of Artificial Intelligence in Echocardiography.

Kenya Kusunose1, Akihiro Haga2, Takashi Abe3, Masataka Sata1.   

Abstract

Echocardiography has a central role in the diagnosis and management of cardiovascular disease. Precise and reliable echocardiographic assessment is required for clinical decision-making. Even if the development of new technologies (3-dimentional echocardiography, speckle-tracking, semi-automated analysis, etc.), the final decision on analysis is strongly dependent on operator experience. Diagnostic errors are a major unresolved problem. Moreover, not only can cardiologists differ from one another in image interpretation, but also the same observer may come to different findings when a reading is repeated. Daily high workloads in clinical practice may lead to this error, and all cardiologists require precise perception in this field. Artificial intelligence (AI) has the potential to improve analysis and interpretation of medical images to a new stage compared with previous algorithms. From our comprehensive review, we believe AI has the potential to improve accuracy of diagnosis, clinical management, and patient care. Although there are several concerns about the required large dataset and "black box" algorithm, AI can provide satisfactory results in this field. In the future, it will be necessary for cardiologists to adapt their daily practice to incorporate AI in this new stage of echocardiography.

Entities:  

Keywords:  Artificial intelligence; Automated diagnosis; Deep learning; Echocardiography; Machine learning

Year:  2019        PMID: 31257314     DOI: 10.1253/circj.CJ-19-0420

Source DB:  PubMed          Journal:  Circ J        ISSN: 1346-9843            Impact factor:   2.993


  13 in total

Review 1.  The roles of global longitudinal strain imaging in contemporary clinical cardiology.

Authors:  Toshimitsu Kato; Tomonari Harada; Kazuki Kagami; Masaru Obokata
Journal:  J Med Ultrason (2001)       Date:  2022-01-28       Impact factor: 1.314

2.  Deep Learning for Detection of Exercise-Induced Pulmonary Hypertension Using Chest X-Ray Images.

Authors:  Kenya Kusunose; Yukina Hirata; Natsumi Yamaguchi; Yoshitaka Kosaka; Takumasa Tsuji; Jun'ichi Kotoku; Masataka Sata
Journal:  Front Cardiovasc Med       Date:  2022-06-15

3.  Artificial Intelligence in Cardiovascular Medicine: Historical Overview, Current Status, and Future Directions.

Authors:  Zvonimir Krajcer
Journal:  Tex Heart Inst J       Date:  2022-03-01

4.  Utility of a Deep-Learning Algorithm to Guide Novices to Acquire Echocardiograms for Limited Diagnostic Use.

Authors:  Akhil Narang; Richard Bae; Ha Hong; Yngvil Thomas; Samuel Surette; Charles Cadieu; Ali Chaudhry; Randolph P Martin; Patrick M McCarthy; David S Rubenson; Steven Goldstein; Stephen H Little; Roberto M Lang; Neil J Weissman; James D Thomas
Journal:  JAMA Cardiol       Date:  2021-06-01       Impact factor: 14.676

Review 5.  Steps to use artificial intelligence in echocardiography.

Authors:  Kenya Kusunose
Journal:  J Echocardiogr       Date:  2020-10-12

Review 6.  Machine Learning and Precision Medicine in Emergency Medicine: The Basics.

Authors:  Sangil Lee; Samuel H Lam; Thiago Augusto Hernandes Rocha; Ross J Fleischman; Catherine A Staton; Richard Taylor; Alexander T Limkakeng
Journal:  Cureus       Date:  2021-09-01

7.  Revealing Unforeseen Diagnostic Image Features With Deep Learning by Detecting Cardiovascular Diseases From Apical 4-Chamber Ultrasounds.

Authors:  Li-Hsin Cheng; Pablo B J Bosch; Rutger F H Hofman; Timo B Brakenhoff; Eline F Bruggemans; Rob J van der Geest; Eduard R Holman
Journal:  J Am Heart Assoc       Date:  2022-08-05       Impact factor: 6.106

8.  Deep learning to predict elevated pulmonary artery pressure in patients with suspected pulmonary hypertension using standard chest X ray.

Authors:  Kenya Kusunose; Yukina Hirata; Takumasa Tsuji; Jun'ichi Kotoku; Masataka Sata
Journal:  Sci Rep       Date:  2020-11-17       Impact factor: 4.379

Review 9.  Practical guidance for echocardiography for cancer therapeutics-related cardiac dysfunction.

Authors:  Tetsuari Onishi; Yuko Fukuda; Sakiko Miyazaki; Hirotsugu Yamada; Hidekazu Tanaka; Jiro Sakamoto; Masao Daimon; Chisato Izumi; Akiko Nonaka; Satoshi Nakatani; Makoto Akaishi
Journal:  J Echocardiogr       Date:  2020-11-07

10.  Automated Recognition of Ultrasound Cardiac Views Based on Deep Learning with Graph Constraint.

Authors:  Yanhua Gao; Yuan Zhu; Bo Liu; Yue Hu; Gang Yu; Youmin Guo
Journal:  Diagnostics (Basel)       Date:  2021-06-29
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