Real-time Classification of Fetal Status Based on Deep Learning and Cardiotocography Data.
This study uses convolutional neural networks (CNNs) and cardiotocography data for the real-time classification of fetal status in the mobile application of a pregnant woman and the computer server of a data expert at the same time (The sensor is connected with the smartphone, which is linked with t...
| Publicado en: | Journal of Medical Systems Vol. 47; no. 1; pp. 1 - 8 |
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| Autores principales: | , , , , , , , |
| Formato: | pictorial research tables/charts tracings Journal Article |
| Publicado: |
Springer Nature
8/3/2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=170717185&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 170717185 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 8/3/2023 vid: 47 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 170717185 170717185 170717185 10.1007/s10916-023-01960-1 170717185 ppf: 1 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Real-time Classification of Fetal Status Based on Deep Learning and Cardiotocography Data. aug: au: Lee, Kwang-Sig Choi, Eun Saem Nam, Young Jin Liu, Nae Won Yang, Yong Seok Kim, Ho Yeon Ahn, Ki Hoon Hong, Soon Cheol affil: AI Center, Korea University College of Medicine, Seoul, Korea sug: subj: Deep Learning Cardiotocography Fetal Well-Being Evaluation Human Retrospective Design Funding Source Fetal Monitoring Mobile Applications Descriptive Statistics Neural Networks (Computer) ab: This study uses convolutional neural networks (CNNs) and cardiotocography data for the real-time classification of fetal status in the mobile application of a pregnant woman and the computer server of a data expert at the same time (The sensor is connected with the smartphone, which is linked with the web server for the woman and the computer server for the expert). Data came from 5249 (or 4833) cardiotocography traces in Anam Hospital for the mobile application (or the computer server). 150 data cases of 5-minute duration were extracted from each trace with 141,001 final cases for the mobile application and for the computer server alike. The dependent variable was fetal status with two categories (Normal, Abnormal) for the mobile application and three categories (Normal, Middle, Abnormal) for the computer server. The fetal heart rate served as a predictor for the mobile application and the computer server, while uterus contraction for the computer server only. The 1-dimension (or 2-dimension) Resnet CNN was trained for the mobile application (or the computer server) during 800 epochs. The sensitivity, specificity and their harmonic mean of the 1-dimension CNN for the mobile application were 94.9%, 91.2% and 93.0%, respectively. The corresponding statistics of the 2-dimension CNN for the computer server were 98.0%, 99.5% and 98.7%. The average inference time per 1000 images was 6.51 micro-seconds. Deep learning provides an efficient model for the real-time classification of fetal status in the mobile application and the computer server at the same time. pubtype: Academic Journal doctype: pictorial research tables/charts tracings Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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