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Multimodal deep learning enhances diagnostic precision in left ventricular hypertrophy( )

AIMS: Determining the aetiology of left ventricular hypertrophy (LVH) can be challenging due to the similarity in clinical presentation and cardiac morphological features of diverse causes of disease. In particular, distinguishing individuals with hypertrophic cardiomyopathy (HCM) from the much larg...

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Autores principales: Soto, Jessica Torres, Weston Hughes, J, Sanchez, Pablo Amador, Perez, Marco, Ouyang, David, Ashley, Euan A
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9707995/
https://www.ncbi.nlm.nih.gov/pubmed/36712167
http://dx.doi.org/10.1093/ehjdh/ztac033
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author Soto, Jessica Torres
Weston Hughes, J
Sanchez, Pablo Amador
Perez, Marco
Ouyang, David
Ashley, Euan A
author_facet Soto, Jessica Torres
Weston Hughes, J
Sanchez, Pablo Amador
Perez, Marco
Ouyang, David
Ashley, Euan A
author_sort Soto, Jessica Torres
collection PubMed
description AIMS: Determining the aetiology of left ventricular hypertrophy (LVH) can be challenging due to the similarity in clinical presentation and cardiac morphological features of diverse causes of disease. In particular, distinguishing individuals with hypertrophic cardiomyopathy (HCM) from the much larger set of individuals with manifest or occult hypertension (HTN) is of major importance for family screening and the prevention of sudden death. We hypothesized that an artificial intelligence method based joint interpretation of 12-lead electrocardiograms and echocardiogram videos could augment physician interpretation. METHODS AND RESULTS: We chose not to train on proximate data labels such as physician over-reads of ECGs or echocardiograms but instead took advantage of electronic health record derived clinical blood pressure measurements and diagnostic consensus (often including molecular testing) among physicians in an HCM centre of excellence. Using more than 18 000 combined instances of electrocardiograms and echocardiograms from 2728 patients, we developed LVH-fusion. On held-out test data, LVH-fusion achieved an F1-score of 0.71 in predicting HCM, and 0.96 in predicting HTN. In head-to-head comparison with human readers LVH-fusion had higher sensitivity and specificity rates than its human counterparts. Finally, we use explainability techniques to investigate local and global features that positively and negatively impact LVH-fusion prediction estimates providing confirmation from unsupervised analysis the diagnostic power of lateral T-wave inversion on the ECG and proximal septal hypertrophy on the echocardiogram for HCM. CONCLUSION: These results show that deep learning can provide effective physician augmentation in the face of a common diagnostic dilemma with far reaching implications for the prevention of sudden cardiac death.
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spelling pubmed-97079952023-01-27 Multimodal deep learning enhances diagnostic precision in left ventricular hypertrophy( ) Soto, Jessica Torres Weston Hughes, J Sanchez, Pablo Amador Perez, Marco Ouyang, David Ashley, Euan A Eur Heart J Digit Health Original Article AIMS: Determining the aetiology of left ventricular hypertrophy (LVH) can be challenging due to the similarity in clinical presentation and cardiac morphological features of diverse causes of disease. In particular, distinguishing individuals with hypertrophic cardiomyopathy (HCM) from the much larger set of individuals with manifest or occult hypertension (HTN) is of major importance for family screening and the prevention of sudden death. We hypothesized that an artificial intelligence method based joint interpretation of 12-lead electrocardiograms and echocardiogram videos could augment physician interpretation. METHODS AND RESULTS: We chose not to train on proximate data labels such as physician over-reads of ECGs or echocardiograms but instead took advantage of electronic health record derived clinical blood pressure measurements and diagnostic consensus (often including molecular testing) among physicians in an HCM centre of excellence. Using more than 18 000 combined instances of electrocardiograms and echocardiograms from 2728 patients, we developed LVH-fusion. On held-out test data, LVH-fusion achieved an F1-score of 0.71 in predicting HCM, and 0.96 in predicting HTN. In head-to-head comparison with human readers LVH-fusion had higher sensitivity and specificity rates than its human counterparts. Finally, we use explainability techniques to investigate local and global features that positively and negatively impact LVH-fusion prediction estimates providing confirmation from unsupervised analysis the diagnostic power of lateral T-wave inversion on the ECG and proximal septal hypertrophy on the echocardiogram for HCM. CONCLUSION: These results show that deep learning can provide effective physician augmentation in the face of a common diagnostic dilemma with far reaching implications for the prevention of sudden cardiac death. Oxford University Press 2022-05-23 /pmc/articles/PMC9707995/ /pubmed/36712167 http://dx.doi.org/10.1093/ehjdh/ztac033 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of the European Society of Cardiology. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Original Article
Soto, Jessica Torres
Weston Hughes, J
Sanchez, Pablo Amador
Perez, Marco
Ouyang, David
Ashley, Euan A
Multimodal deep learning enhances diagnostic precision in left ventricular hypertrophy( )
title Multimodal deep learning enhances diagnostic precision in left ventricular hypertrophy( )
title_full Multimodal deep learning enhances diagnostic precision in left ventricular hypertrophy( )
title_fullStr Multimodal deep learning enhances diagnostic precision in left ventricular hypertrophy( )
title_full_unstemmed Multimodal deep learning enhances diagnostic precision in left ventricular hypertrophy( )
title_short Multimodal deep learning enhances diagnostic precision in left ventricular hypertrophy( )
title_sort multimodal deep learning enhances diagnostic precision in left ventricular hypertrophy( )
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9707995/
https://www.ncbi.nlm.nih.gov/pubmed/36712167
http://dx.doi.org/10.1093/ehjdh/ztac033
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