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Physicians and Machine-Learning Algorithm Performance in Predicting Left-Ventricular Systolic Dysfunction from a Standard 12-Lead-Electrocardiogram
Early detection of left ventricular systolic dysfunction (LVSD) may prompt early care and improve outcomes for asymptomatic patients. Standard 12-lead ECG may be used to predict LVSD. We aimed to compare the performance of Machine Learning Algorithms (MLA) and physicians in predicting LVSD from a st...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9699306/ https://www.ncbi.nlm.nih.gov/pubmed/36431244 http://dx.doi.org/10.3390/jcm11226767 |
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author | Golany, Tomer Radinsky, Kira Kofman, Natalia Litovchik, Ilya Young, Revital Monayer, Antoinette Love, Itamar Tziporin, Faina Minha, Ido Yehuda, Yakir Ziv-Baran, Tomer Fuchs, Shmuel Minha, Sa’ar |
author_facet | Golany, Tomer Radinsky, Kira Kofman, Natalia Litovchik, Ilya Young, Revital Monayer, Antoinette Love, Itamar Tziporin, Faina Minha, Ido Yehuda, Yakir Ziv-Baran, Tomer Fuchs, Shmuel Minha, Sa’ar |
author_sort | Golany, Tomer |
collection | PubMed |
description | Early detection of left ventricular systolic dysfunction (LVSD) may prompt early care and improve outcomes for asymptomatic patients. Standard 12-lead ECG may be used to predict LVSD. We aimed to compare the performance of Machine Learning Algorithms (MLA) and physicians in predicting LVSD from a standard 12-lead ECG. By utilizing a dataset of 13,820 pairs of ECGs and echocardiography, a deep residual convolutional neural network was trained for predicting LVSD (ejection fraction (EF) < 50%) from ECG. The ECGs of the test set (n = 850) were assessed for LVSD by the MLA and six physicians. The performance was compared using sensitivity, specificity, and C-statistics. The interobserver agreement between the physicians for the prediction of LVSD was moderate (κ = 0.50), with average sensitivity and specificity of 70%. The C-statistic of the MLA was 0.85. Repeating this analysis with LVSD defined as EF < 35% resulted in an improvement in physicians’ average sensitivity to 84% but their specificity decreased to 57%. The MLA C-statistic was 0.88 with this threshold. We conclude that although MLA outperformed physicians in predicting LVSD from standard ECG, prior to robust implementation of MLA in ECG machines, physicians should be encouraged to use this approach as a simple and readily available aid for LVSD screening. |
format | Online Article Text |
id | pubmed-9699306 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96993062022-11-26 Physicians and Machine-Learning Algorithm Performance in Predicting Left-Ventricular Systolic Dysfunction from a Standard 12-Lead-Electrocardiogram Golany, Tomer Radinsky, Kira Kofman, Natalia Litovchik, Ilya Young, Revital Monayer, Antoinette Love, Itamar Tziporin, Faina Minha, Ido Yehuda, Yakir Ziv-Baran, Tomer Fuchs, Shmuel Minha, Sa’ar J Clin Med Article Early detection of left ventricular systolic dysfunction (LVSD) may prompt early care and improve outcomes for asymptomatic patients. Standard 12-lead ECG may be used to predict LVSD. We aimed to compare the performance of Machine Learning Algorithms (MLA) and physicians in predicting LVSD from a standard 12-lead ECG. By utilizing a dataset of 13,820 pairs of ECGs and echocardiography, a deep residual convolutional neural network was trained for predicting LVSD (ejection fraction (EF) < 50%) from ECG. The ECGs of the test set (n = 850) were assessed for LVSD by the MLA and six physicians. The performance was compared using sensitivity, specificity, and C-statistics. The interobserver agreement between the physicians for the prediction of LVSD was moderate (κ = 0.50), with average sensitivity and specificity of 70%. The C-statistic of the MLA was 0.85. Repeating this analysis with LVSD defined as EF < 35% resulted in an improvement in physicians’ average sensitivity to 84% but their specificity decreased to 57%. The MLA C-statistic was 0.88 with this threshold. We conclude that although MLA outperformed physicians in predicting LVSD from standard ECG, prior to robust implementation of MLA in ECG machines, physicians should be encouraged to use this approach as a simple and readily available aid for LVSD screening. MDPI 2022-11-15 /pmc/articles/PMC9699306/ /pubmed/36431244 http://dx.doi.org/10.3390/jcm11226767 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 Golany, Tomer Radinsky, Kira Kofman, Natalia Litovchik, Ilya Young, Revital Monayer, Antoinette Love, Itamar Tziporin, Faina Minha, Ido Yehuda, Yakir Ziv-Baran, Tomer Fuchs, Shmuel Minha, Sa’ar Physicians and Machine-Learning Algorithm Performance in Predicting Left-Ventricular Systolic Dysfunction from a Standard 12-Lead-Electrocardiogram |
title | Physicians and Machine-Learning Algorithm Performance in Predicting Left-Ventricular Systolic Dysfunction from a Standard 12-Lead-Electrocardiogram |
title_full | Physicians and Machine-Learning Algorithm Performance in Predicting Left-Ventricular Systolic Dysfunction from a Standard 12-Lead-Electrocardiogram |
title_fullStr | Physicians and Machine-Learning Algorithm Performance in Predicting Left-Ventricular Systolic Dysfunction from a Standard 12-Lead-Electrocardiogram |
title_full_unstemmed | Physicians and Machine-Learning Algorithm Performance in Predicting Left-Ventricular Systolic Dysfunction from a Standard 12-Lead-Electrocardiogram |
title_short | Physicians and Machine-Learning Algorithm Performance in Predicting Left-Ventricular Systolic Dysfunction from a Standard 12-Lead-Electrocardiogram |
title_sort | physicians and machine-learning algorithm performance in predicting left-ventricular systolic dysfunction from a standard 12-lead-electrocardiogram |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9699306/ https://www.ncbi.nlm.nih.gov/pubmed/36431244 http://dx.doi.org/10.3390/jcm11226767 |
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