Deep neural network-based classification of cardiotocograms outperformed conventional algorithms

Abstract Cardiotocography records fetal heart rates and their temporal relationship to uterine contractions. To identify high risk fetuses, obstetricians inspect cardiotocograms (CTGs) by eye. Therefore, CTG traces are often interpreted differently among obstetricians, resulting in inappropriate int...

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Autores principales:	Jun Ogasawara, Satoru Ikenoue, Hiroko Yamamoto, Motoshige Sato, Yoshifumi Kasuga, Yasue Mitsukura, Yuji Ikegaya, Masato Yasui, Mamoru Tanaka, Daigo Ochiai
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3ed9c7208b7242f5b732d84df5130a7c
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spelling	oai:doaj.org-article:3ed9c7208b7242f5b732d84df5130a7c2021-12-02T16:10:36ZDeep neural network-based classification of cardiotocograms outperformed conventional algorithms10.1038/s41598-021-92805-92045-2322https://doaj.org/article/3ed9c7208b7242f5b732d84df5130a7c2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92805-9https://doaj.org/toc/2045-2322Abstract Cardiotocography records fetal heart rates and their temporal relationship to uterine contractions. To identify high risk fetuses, obstetricians inspect cardiotocograms (CTGs) by eye. Therefore, CTG traces are often interpreted differently among obstetricians, resulting in inappropriate interventions. However, few studies have focused on quantitative and nonbiased algorithms for CTG evaluation. In this study, we propose a newly constructed deep neural network model (CTG-net) to detect compromised fetal status. CTG-net consists of three convolutional layers that extract temporal patterns and interrelationships between fetal heart rate and uterine contraction signals. We aimed to classify the abnormal group (umbilical artery pH < 7.20 or Apgar score at 1 min < 7) and the normal group from CTG data. We evaluated the performance of the CTG-net with the F1 score and compared it with conventional algorithms, namely, support vector machine and k-means clustering, and another deep neural network model, long short-term memory. CTG-net showed the area under the receiver operating characteristic curve of 0.73 ± 0.04, which was significantly higher than that of long short-term memory. CTG-net, a quantitative and automated diagnostic aid system, enables early intervention for putatively abnormal fetuses, resulting in a reduction in the number of cases of hypoxic injury.Jun OgasawaraSatoru IkenoueHiroko YamamotoMotoshige SatoYoshifumi KasugaYasue MitsukuraYuji IkegayaMasato YasuiMamoru TanakaDaigo OchiaiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jun Ogasawara Satoru Ikenoue Hiroko Yamamoto Motoshige Sato Yoshifumi Kasuga Yasue Mitsukura Yuji Ikegaya Masato Yasui Mamoru Tanaka Daigo Ochiai Deep neural network-based classification of cardiotocograms outperformed conventional algorithms
description	Abstract Cardiotocography records fetal heart rates and their temporal relationship to uterine contractions. To identify high risk fetuses, obstetricians inspect cardiotocograms (CTGs) by eye. Therefore, CTG traces are often interpreted differently among obstetricians, resulting in inappropriate interventions. However, few studies have focused on quantitative and nonbiased algorithms for CTG evaluation. In this study, we propose a newly constructed deep neural network model (CTG-net) to detect compromised fetal status. CTG-net consists of three convolutional layers that extract temporal patterns and interrelationships between fetal heart rate and uterine contraction signals. We aimed to classify the abnormal group (umbilical artery pH < 7.20 or Apgar score at 1 min < 7) and the normal group from CTG data. We evaluated the performance of the CTG-net with the F1 score and compared it with conventional algorithms, namely, support vector machine and k-means clustering, and another deep neural network model, long short-term memory. CTG-net showed the area under the receiver operating characteristic curve of 0.73 ± 0.04, which was significantly higher than that of long short-term memory. CTG-net, a quantitative and automated diagnostic aid system, enables early intervention for putatively abnormal fetuses, resulting in a reduction in the number of cases of hypoxic injury.
format	article
author	Jun Ogasawara Satoru Ikenoue Hiroko Yamamoto Motoshige Sato Yoshifumi Kasuga Yasue Mitsukura Yuji Ikegaya Masato Yasui Mamoru Tanaka Daigo Ochiai
author_facet	Jun Ogasawara Satoru Ikenoue Hiroko Yamamoto Motoshige Sato Yoshifumi Kasuga Yasue Mitsukura Yuji Ikegaya Masato Yasui Mamoru Tanaka Daigo Ochiai
author_sort	Jun Ogasawara
title	Deep neural network-based classification of cardiotocograms outperformed conventional algorithms
title_short	Deep neural network-based classification of cardiotocograms outperformed conventional algorithms
title_full	Deep neural network-based classification of cardiotocograms outperformed conventional algorithms
title_fullStr	Deep neural network-based classification of cardiotocograms outperformed conventional algorithms
title_full_unstemmed	Deep neural network-based classification of cardiotocograms outperformed conventional algorithms
title_sort	deep neural network-based classification of cardiotocograms outperformed conventional algorithms
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/3ed9c7208b7242f5b732d84df5130a7c
work_keys_str_mv	AT junogasawara deepneuralnetworkbasedclassificationofcardiotocogramsoutperformedconventionalalgorithms AT satoruikenoue deepneuralnetworkbasedclassificationofcardiotocogramsoutperformedconventionalalgorithms AT hirokoyamamoto deepneuralnetworkbasedclassificationofcardiotocogramsoutperformedconventionalalgorithms AT motoshigesato deepneuralnetworkbasedclassificationofcardiotocogramsoutperformedconventionalalgorithms AT yoshifumikasuga deepneuralnetworkbasedclassificationofcardiotocogramsoutperformedconventionalalgorithms AT yasuemitsukura deepneuralnetworkbasedclassificationofcardiotocogramsoutperformedconventionalalgorithms AT yujiikegaya deepneuralnetworkbasedclassificationofcardiotocogramsoutperformedconventionalalgorithms AT masatoyasui deepneuralnetworkbasedclassificationofcardiotocogramsoutperformedconventionalalgorithms AT mamorutanaka deepneuralnetworkbasedclassificationofcardiotocogramsoutperformedconventionalalgorithms AT daigoochiai deepneuralnetworkbasedclassificationofcardiotocogramsoutperformedconventionalalgorithms
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Deep neural network-based classification of cardiotocograms outperformed conventional algorithms

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