Cargando…

Automatic diagnosis of the 12-lead ECG using a deep neural network

The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. Deep Neural Networks (DNNs) are models composed of stacked transformations that learn tasks by examples. This technology has recently achieved striking success in a variety of t...

Descripción completa

Detalles Bibliográficos
Autores principales: Ribeiro, Antônio H., Ribeiro, Manoel Horta, Paixão, Gabriela M. M., Oliveira, Derick M., Gomes, Paulo R., Canazart, Jéssica A., Ferreira, Milton P. S., Andersson, Carl R., Macfarlane, Peter W., Wagner Jr., Meira, Schön, Thomas B., Ribeiro, Antonio Luiz P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7145824/
https://www.ncbi.nlm.nih.gov/pubmed/32273514
http://dx.doi.org/10.1038/s41467-020-15432-4
_version_ 1783520062332731392
author Ribeiro, Antônio H.
Ribeiro, Manoel Horta
Paixão, Gabriela M. M.
Oliveira, Derick M.
Gomes, Paulo R.
Canazart, Jéssica A.
Ferreira, Milton P. S.
Andersson, Carl R.
Macfarlane, Peter W.
Wagner Jr., Meira
Schön, Thomas B.
Ribeiro, Antonio Luiz P.
author_facet Ribeiro, Antônio H.
Ribeiro, Manoel Horta
Paixão, Gabriela M. M.
Oliveira, Derick M.
Gomes, Paulo R.
Canazart, Jéssica A.
Ferreira, Milton P. S.
Andersson, Carl R.
Macfarlane, Peter W.
Wagner Jr., Meira
Schön, Thomas B.
Ribeiro, Antonio Luiz P.
author_sort Ribeiro, Antônio H.
collection PubMed
description The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. Deep Neural Networks (DNNs) are models composed of stacked transformations that learn tasks by examples. This technology has recently achieved striking success in a variety of task and there are great expectations on how it might improve clinical practice. Here we present a DNN model trained in a dataset with more than 2 million labeled exams analyzed by the Telehealth Network of Minas Gerais and collected under the scope of the CODE (Clinical Outcomes in Digital Electrocardiology) study. The DNN outperform cardiology resident medical doctors in recognizing 6 types of abnormalities in 12-lead ECG recordings, with F1 scores above 80% and specificity over 99%. These results indicate ECG analysis based on DNNs, previously studied in a single-lead setup, generalizes well to 12-lead exams, taking the technology closer to the standard clinical practice.
format Online
Article
Text
id pubmed-7145824
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-71458242020-04-13 Automatic diagnosis of the 12-lead ECG using a deep neural network Ribeiro, Antônio H. Ribeiro, Manoel Horta Paixão, Gabriela M. M. Oliveira, Derick M. Gomes, Paulo R. Canazart, Jéssica A. Ferreira, Milton P. S. Andersson, Carl R. Macfarlane, Peter W. Wagner Jr., Meira Schön, Thomas B. Ribeiro, Antonio Luiz P. Nat Commun Article The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. Deep Neural Networks (DNNs) are models composed of stacked transformations that learn tasks by examples. This technology has recently achieved striking success in a variety of task and there are great expectations on how it might improve clinical practice. Here we present a DNN model trained in a dataset with more than 2 million labeled exams analyzed by the Telehealth Network of Minas Gerais and collected under the scope of the CODE (Clinical Outcomes in Digital Electrocardiology) study. The DNN outperform cardiology resident medical doctors in recognizing 6 types of abnormalities in 12-lead ECG recordings, with F1 scores above 80% and specificity over 99%. These results indicate ECG analysis based on DNNs, previously studied in a single-lead setup, generalizes well to 12-lead exams, taking the technology closer to the standard clinical practice. Nature Publishing Group UK 2020-04-09 /pmc/articles/PMC7145824/ /pubmed/32273514 http://dx.doi.org/10.1038/s41467-020-15432-4 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Ribeiro, Antônio H.
Ribeiro, Manoel Horta
Paixão, Gabriela M. M.
Oliveira, Derick M.
Gomes, Paulo R.
Canazart, Jéssica A.
Ferreira, Milton P. S.
Andersson, Carl R.
Macfarlane, Peter W.
Wagner Jr., Meira
Schön, Thomas B.
Ribeiro, Antonio Luiz P.
Automatic diagnosis of the 12-lead ECG using a deep neural network
title Automatic diagnosis of the 12-lead ECG using a deep neural network
title_full Automatic diagnosis of the 12-lead ECG using a deep neural network
title_fullStr Automatic diagnosis of the 12-lead ECG using a deep neural network
title_full_unstemmed Automatic diagnosis of the 12-lead ECG using a deep neural network
title_short Automatic diagnosis of the 12-lead ECG using a deep neural network
title_sort automatic diagnosis of the 12-lead ecg using a deep neural network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7145824/
https://www.ncbi.nlm.nih.gov/pubmed/32273514
http://dx.doi.org/10.1038/s41467-020-15432-4
work_keys_str_mv AT ribeiroantonioh automaticdiagnosisofthe12leadecgusingadeepneuralnetwork
AT ribeiromanoelhorta automaticdiagnosisofthe12leadecgusingadeepneuralnetwork
AT paixaogabrielamm automaticdiagnosisofthe12leadecgusingadeepneuralnetwork
AT oliveiraderickm automaticdiagnosisofthe12leadecgusingadeepneuralnetwork
AT gomespaulor automaticdiagnosisofthe12leadecgusingadeepneuralnetwork
AT canazartjessicaa automaticdiagnosisofthe12leadecgusingadeepneuralnetwork
AT ferreiramiltonps automaticdiagnosisofthe12leadecgusingadeepneuralnetwork
AT anderssoncarlr automaticdiagnosisofthe12leadecgusingadeepneuralnetwork
AT macfarlanepeterw automaticdiagnosisofthe12leadecgusingadeepneuralnetwork
AT wagnerjrmeira automaticdiagnosisofthe12leadecgusingadeepneuralnetwork
AT schonthomasb automaticdiagnosisofthe12leadecgusingadeepneuralnetwork
AT ribeiroantonioluizp automaticdiagnosisofthe12leadecgusingadeepneuralnetwork