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Accuracy of Deep Learning Echocardiographic View Classification in Patients with Congenital or Structural Heart Disease: Importance of Specific Datasets

Introduction: Automated echocardiography image interpretation has the potential to transform clinical practice. However, neural networks developed in general cohorts may underperform in the setting of altered cardiac anatomy. Methods: Consecutive echocardiographic studies of patients with congenital...

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Autores principales: Wegner, Felix K., Benesch Vidal, Maria L., Niehues, Philipp, Willy, Kevin, Radke, Robert M., Garthe, Philipp D., Eckardt, Lars, Baumgartner, Helmut, Diller, Gerhard-Paul, Orwat, Stefan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8836991/
https://www.ncbi.nlm.nih.gov/pubmed/35160148
http://dx.doi.org/10.3390/jcm11030690
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author Wegner, Felix K.
Benesch Vidal, Maria L.
Niehues, Philipp
Willy, Kevin
Radke, Robert M.
Garthe, Philipp D.
Eckardt, Lars
Baumgartner, Helmut
Diller, Gerhard-Paul
Orwat, Stefan
author_facet Wegner, Felix K.
Benesch Vidal, Maria L.
Niehues, Philipp
Willy, Kevin
Radke, Robert M.
Garthe, Philipp D.
Eckardt, Lars
Baumgartner, Helmut
Diller, Gerhard-Paul
Orwat, Stefan
author_sort Wegner, Felix K.
collection PubMed
description Introduction: Automated echocardiography image interpretation has the potential to transform clinical practice. However, neural networks developed in general cohorts may underperform in the setting of altered cardiac anatomy. Methods: Consecutive echocardiographic studies of patients with congenital or structural heart disease (C/SHD) were used to validate an existing convolutional neural network trained on 14,035 echocardiograms for automated view classification. In addition, a new convolutional neural network for view classification was trained and tested specifically in patients with C/SHD. Results: Overall, 9793 imaging files from 262 patients with C/SHD (mean age 49 years, 60% male) and 62 normal controls (mean age 45 years, 50.0% male) were included. Congenital diagnoses included among others, tetralogy of Fallot (30), Ebstein anomaly (18) and transposition of the great arteries (TGA, 48). Assessing correct view classification based on 284,250 individual frames revealed that the non-congenital model had an overall accuracy of 48.3% for correct view classification in patients with C/SHD compared to 66.7% in patients without cardiac disease. Our newly trained convolutional network for echocardiographic view detection based on over 139,910 frames and tested on 35,614 frames from C/SHD patients achieved an accuracy of 76.1% in detecting the correct echocardiographic view. Conclusions: The current study is the first to validate view classification by neural networks in C/SHD patients. While generic models have acceptable accuracy in general cardiology patients, the quality of image classification is only modest in patients with C/SHD. In contrast, our model trained in C/SHD achieved a considerably increased accuracy in this particular cohort.
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spelling pubmed-88369912022-02-12 Accuracy of Deep Learning Echocardiographic View Classification in Patients with Congenital or Structural Heart Disease: Importance of Specific Datasets Wegner, Felix K. Benesch Vidal, Maria L. Niehues, Philipp Willy, Kevin Radke, Robert M. Garthe, Philipp D. Eckardt, Lars Baumgartner, Helmut Diller, Gerhard-Paul Orwat, Stefan J Clin Med Article Introduction: Automated echocardiography image interpretation has the potential to transform clinical practice. However, neural networks developed in general cohorts may underperform in the setting of altered cardiac anatomy. Methods: Consecutive echocardiographic studies of patients with congenital or structural heart disease (C/SHD) were used to validate an existing convolutional neural network trained on 14,035 echocardiograms for automated view classification. In addition, a new convolutional neural network for view classification was trained and tested specifically in patients with C/SHD. Results: Overall, 9793 imaging files from 262 patients with C/SHD (mean age 49 years, 60% male) and 62 normal controls (mean age 45 years, 50.0% male) were included. Congenital diagnoses included among others, tetralogy of Fallot (30), Ebstein anomaly (18) and transposition of the great arteries (TGA, 48). Assessing correct view classification based on 284,250 individual frames revealed that the non-congenital model had an overall accuracy of 48.3% for correct view classification in patients with C/SHD compared to 66.7% in patients without cardiac disease. Our newly trained convolutional network for echocardiographic view detection based on over 139,910 frames and tested on 35,614 frames from C/SHD patients achieved an accuracy of 76.1% in detecting the correct echocardiographic view. Conclusions: The current study is the first to validate view classification by neural networks in C/SHD patients. While generic models have acceptable accuracy in general cardiology patients, the quality of image classification is only modest in patients with C/SHD. In contrast, our model trained in C/SHD achieved a considerably increased accuracy in this particular cohort. MDPI 2022-01-28 /pmc/articles/PMC8836991/ /pubmed/35160148 http://dx.doi.org/10.3390/jcm11030690 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wegner, Felix K.
Benesch Vidal, Maria L.
Niehues, Philipp
Willy, Kevin
Radke, Robert M.
Garthe, Philipp D.
Eckardt, Lars
Baumgartner, Helmut
Diller, Gerhard-Paul
Orwat, Stefan
Accuracy of Deep Learning Echocardiographic View Classification in Patients with Congenital or Structural Heart Disease: Importance of Specific Datasets
title Accuracy of Deep Learning Echocardiographic View Classification in Patients with Congenital or Structural Heart Disease: Importance of Specific Datasets
title_full Accuracy of Deep Learning Echocardiographic View Classification in Patients with Congenital or Structural Heart Disease: Importance of Specific Datasets
title_fullStr Accuracy of Deep Learning Echocardiographic View Classification in Patients with Congenital or Structural Heart Disease: Importance of Specific Datasets
title_full_unstemmed Accuracy of Deep Learning Echocardiographic View Classification in Patients with Congenital or Structural Heart Disease: Importance of Specific Datasets
title_short Accuracy of Deep Learning Echocardiographic View Classification in Patients with Congenital or Structural Heart Disease: Importance of Specific Datasets
title_sort accuracy of deep learning echocardiographic view classification in patients with congenital or structural heart disease: importance of specific datasets
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8836991/
https://www.ncbi.nlm.nih.gov/pubmed/35160148
http://dx.doi.org/10.3390/jcm11030690
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