Routine Echocardiography and Artificial Intelligence Solutions
Introduction: Echocardiography is widely used because of its portability, high temporal resolution, absence of radiation, and due to the low-costs. Over the past years, echocardiography has been recommended by the European Society of Cardiology in most cardiac diseases for both diagnostic and progno...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-02-01
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| Series: | Frontiers in Cardiovascular Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fcvm.2021.648877/full |
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| author | Mark J. Schuuring Ivana Išgum Ivana Išgum Ivana Išgum Bernard Cosyns Steven A. J. Chamuleau Steven A. J. Chamuleau Berto J. Bouma |
| author_facet | Mark J. Schuuring Ivana Išgum Ivana Išgum Ivana Išgum Bernard Cosyns Steven A. J. Chamuleau Steven A. J. Chamuleau Berto J. Bouma |
| author_sort | Mark J. Schuuring |
| collection | DOAJ |
| description | Introduction: Echocardiography is widely used because of its portability, high temporal resolution, absence of radiation, and due to the low-costs. Over the past years, echocardiography has been recommended by the European Society of Cardiology in most cardiac diseases for both diagnostic and prognostic purposes. These recommendations have led to an increase in number of performed studies each requiring diligent processing and reviewing. The standard work pattern of image analysis including quantification and reporting has become highly resource intensive and time consuming. Existence of a large number of datasets with digital echocardiography images and recent advent of AI technology have created an environment in which artificial intelligence (AI) solutions can be developed successfully to automate current manual workflow.Methods and Results: We report on published AI solutions for echocardiography analysis on methods' performance, characteristics of the used data and imaged population. Contemporary AI applications are available for automation and advent in the image acquisition, analysis, reporting and education. AI solutions have been developed for both diagnostic and predictive tasks in echocardiography. Left ventricular function assessment and quantification have been most often performed. Performance of automated image view classification, image quality enhancement, cardiac function assessment, disease classification, and cardiac event prediction was overall good but most studies lack external evaluation.Conclusion: Contemporary AI solutions for image acquisition, analysis, reporting and education are developed for relevant tasks with promising performance. In the future major benefit of AI in echocardiography is expected from improvements in automated analysis and interpretation to reduce workload and improve clinical outcome. Some of the challenges have yet to be overcome, however, none of them are insurmountable. |
| first_indexed | 2024-12-23T23:30:48Z |
| format | Article |
| id | doaj.art-152a8d93b28243dcaec014ffc970e046 |
| institution | Directory Open Access Journal |
| issn | 2297-055X |
| language | English |
| last_indexed | 2024-12-23T23:30:48Z |
| publishDate | 2021-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cardiovascular Medicine |
| spelling | doaj.art-152a8d93b28243dcaec014ffc970e0462022-12-21T17:26:03ZengFrontiers Media S.A.Frontiers in Cardiovascular Medicine2297-055X2021-02-01810.3389/fcvm.2021.648877648877Routine Echocardiography and Artificial Intelligence SolutionsMark J. Schuuring0Ivana Išgum1Ivana Išgum2Ivana Išgum3Bernard Cosyns4Steven A. J. Chamuleau5Steven A. J. Chamuleau6Berto J. Bouma7Amsterdam University Medical Centers -Location Academic Medical Center, Department of Cardiology, University of Amsterdam, Amsterdam, NetherlandsAmsterdam University Medical Centers -Location Academic Medical Center, Department of Biomedical Engineering and Physics, University of Amsterdam, Amsterdam, NetherlandsAmsterdam University Medical Centers -Location Academic Medical Center, Department of Radiology and Nuclear Medicine, University of Amsterdam, Amsterdam, NetherlandsAmsterdam Cardiovascular Sciences, Amsterdam University Medical Centers -Location Academic Medical Center, University of Amsterdam, Amsterdam, NetherlandsDepartment of Cardiology, University Hospital Brussel, Brussels, BelgiumAmsterdam University Medical Centers -Location Academic Medical Center, Department of Cardiology, University of Amsterdam, Amsterdam, NetherlandsAmsterdam Cardiovascular Sciences, Amsterdam University Medical Centers -Location Academic Medical Center, University of Amsterdam, Amsterdam, NetherlandsAmsterdam University Medical Centers -Location Academic Medical Center, Department of Cardiology, University of Amsterdam, Amsterdam, NetherlandsIntroduction: Echocardiography is widely used because of its portability, high temporal resolution, absence of radiation, and due to the low-costs. Over the past years, echocardiography has been recommended by the European Society of Cardiology in most cardiac diseases for both diagnostic and prognostic purposes. These recommendations have led to an increase in number of performed studies each requiring diligent processing and reviewing. The standard work pattern of image analysis including quantification and reporting has become highly resource intensive and time consuming. Existence of a large number of datasets with digital echocardiography images and recent advent of AI technology have created an environment in which artificial intelligence (AI) solutions can be developed successfully to automate current manual workflow.Methods and Results: We report on published AI solutions for echocardiography analysis on methods' performance, characteristics of the used data and imaged population. Contemporary AI applications are available for automation and advent in the image acquisition, analysis, reporting and education. AI solutions have been developed for both diagnostic and predictive tasks in echocardiography. Left ventricular function assessment and quantification have been most often performed. Performance of automated image view classification, image quality enhancement, cardiac function assessment, disease classification, and cardiac event prediction was overall good but most studies lack external evaluation.Conclusion: Contemporary AI solutions for image acquisition, analysis, reporting and education are developed for relevant tasks with promising performance. In the future major benefit of AI in echocardiography is expected from improvements in automated analysis and interpretation to reduce workload and improve clinical outcome. Some of the challenges have yet to be overcome, however, none of them are insurmountable.https://www.frontiersin.org/articles/10.3389/fcvm.2021.648877/fullechocardiographycardiac imagingartificial intelligenceimage analysisdiagnosisprediction |
| spellingShingle | Mark J. Schuuring Ivana Išgum Ivana Išgum Ivana Išgum Bernard Cosyns Steven A. J. Chamuleau Steven A. J. Chamuleau Berto J. Bouma Routine Echocardiography and Artificial Intelligence Solutions Frontiers in Cardiovascular Medicine echocardiography cardiac imaging artificial intelligence image analysis diagnosis prediction |
| title | Routine Echocardiography and Artificial Intelligence Solutions |
| title_full | Routine Echocardiography and Artificial Intelligence Solutions |
| title_fullStr | Routine Echocardiography and Artificial Intelligence Solutions |
| title_full_unstemmed | Routine Echocardiography and Artificial Intelligence Solutions |
| title_short | Routine Echocardiography and Artificial Intelligence Solutions |
| title_sort | routine echocardiography and artificial intelligence solutions |
| topic | echocardiography cardiac imaging artificial intelligence image analysis diagnosis prediction |
| url | https://www.frontiersin.org/articles/10.3389/fcvm.2021.648877/full |
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