Cargando…
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...
Autores principales: | , , , , , , , , , |
---|---|
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 |
_version_ | 1784649813887811584 |
---|---|
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. |
format | Online Article Text |
id | pubmed-8836991 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT wegnerfelixk accuracyofdeeplearningechocardiographicviewclassificationinpatientswithcongenitalorstructuralheartdiseaseimportanceofspecificdatasets AT beneschvidalmarial accuracyofdeeplearningechocardiographicviewclassificationinpatientswithcongenitalorstructuralheartdiseaseimportanceofspecificdatasets AT niehuesphilipp accuracyofdeeplearningechocardiographicviewclassificationinpatientswithcongenitalorstructuralheartdiseaseimportanceofspecificdatasets AT willykevin accuracyofdeeplearningechocardiographicviewclassificationinpatientswithcongenitalorstructuralheartdiseaseimportanceofspecificdatasets AT radkerobertm accuracyofdeeplearningechocardiographicviewclassificationinpatientswithcongenitalorstructuralheartdiseaseimportanceofspecificdatasets AT garthephilippd accuracyofdeeplearningechocardiographicviewclassificationinpatientswithcongenitalorstructuralheartdiseaseimportanceofspecificdatasets AT eckardtlars accuracyofdeeplearningechocardiographicviewclassificationinpatientswithcongenitalorstructuralheartdiseaseimportanceofspecificdatasets AT baumgartnerhelmut accuracyofdeeplearningechocardiographicviewclassificationinpatientswithcongenitalorstructuralheartdiseaseimportanceofspecificdatasets AT dillergerhardpaul accuracyofdeeplearningechocardiographicviewclassificationinpatientswithcongenitalorstructuralheartdiseaseimportanceofspecificdatasets AT orwatstefan accuracyofdeeplearningechocardiographicviewclassificationinpatientswithcongenitalorstructuralheartdiseaseimportanceofspecificdatasets |