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Automatic diagnosis...
Automatic diagnosis of the 12-lead ECG using a deep neural network
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- Ribeiro, Antônio H. (författare)
- Uppsala universitet,Avdelningen för systemteknik,Artificiell intelligens,Univ Fed Minas Gerais, Belo Horizonte, MG, Brazil
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- Ribeiro, Manoel Horta (författare)
- Univ Fed Minas Gerais, Belo Horizonte, MG, Brazil.
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- Paixao, Gabriela M. M. (författare)
- Univ Fed Minas Gerais, Belo Horizonte, MG, Brazil.;Univ Fed Minas Gerais, Hosp Clin, Telehlth Ctr, Belo Horizonte, MG, Brazil.
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- Oliveira, Derick M. (författare)
- Univ Fed Minas Gerais, Belo Horizonte, MG, Brazil.
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- Gomes, Paulo R. (författare)
- Univ Fed Minas Gerais, Belo Horizonte, MG, Brazil.;Univ Fed Minas Gerais, Hosp Clin, Telehlth Ctr, Belo Horizonte, MG, Brazil.
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- Canazart, Jessica A. (författare)
- Univ Fed Minas Gerais, Belo Horizonte, MG, Brazil.;Univ Fed Minas Gerais, Hosp Clin, Telehlth Ctr, Belo Horizonte, MG, Brazil.
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- Ferreira, Milton P. S. (författare)
- Univ Fed Minas Gerais, Belo Horizonte, MG, Brazil.
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- Andersson, Carl (författare)
- Uppsala universitet,Avdelningen för systemteknik,Artificiell intelligens
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- Macfarlane, Peter W. (författare)
- Univ Glasgow, Glasgow, Lanark, Scotland.
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- Wagner, Meira, Jr. (författare)
- Univ Fed Minas Gerais, Belo Horizonte, MG, Brazil.
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- Schön, Thomas B., Professor, 1977- (författare)
- Uppsala universitet,Artificiell intelligens,Avdelningen för systemteknik
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- Ribeiro, Antonio Luiz P. (författare)
- Univ Fed Minas Gerais, Belo Horizonte, MG, Brazil.;Univ Fed Minas Gerais, Hosp Clin, Telehlth Ctr, Belo Horizonte, MG, Brazil.
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(creator_code:org_t)
- 2020-04-09
- 2020
- Engelska.
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Ingår i: Nature Communications. - : NATURE PUBLISHING GROUP. - 2041-1723. ; 11:1
- Relaterad länk:
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https://doi.org/10.1...
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https://uu.diva-port... (primary) (Raw object)
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https://www.nature.c...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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. The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. In that context, the authors present a Deep Neural Network (DNN) that recognizes different abnormalities in ECG recordings which matches or outperform cardiology and emergency resident medical doctors.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Kardiologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cardiac and Cardiovascular Systems (hsv//eng)
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- ref (ämneskategori)
- art (ämneskategori)
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Till lärosätets databas
- Av författaren/redakt...
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Ribeiro, Antônio ...
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Ribeiro, Manoel ...
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Paixao, Gabriela ...
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Oliveira, Derick ...
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Gomes, Paulo R.
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Canazart, Jessic ...
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visa fler...
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Ferreira, Milton ...
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Andersson, Carl
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Macfarlane, Pete ...
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Wagner, Meira, J ...
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Schön, Thomas B. ...
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Ribeiro, Antonio ...
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- Om ämnet
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- MEDICIN OCH HÄLSOVETENSKAP
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MEDICIN OCH HÄLS ...
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och Klinisk medicin
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och Kardiologi
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Nature Communica ...
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Uppsala universitet