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01-12-2023 | Original Paper

Real-time Classification of Fetal Status Based on Deep Learning and Cardiotocography Data

Authors: Kwang-Sig Lee, Eun Saem Choi, Young Jin Nam, Nae Won Liu, Yong Seok Yang, Ho Yeon Kim, Ki Hoon Ahn, Soon Cheol Hong

Published in: Journal of Medical Systems | Issue 1/2023

Abstract

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.

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Title: Real-time Classification of Fetal Status Based on Deep Learning and Cardiotocography Data
Authors: Kwang-Sig Lee
Eun Saem Choi
Young Jin Nam
Nae Won Liu
Yong Seok Yang
Ho Yeon Kim
Ki Hoon Ahn
Soon Cheol Hong
Publication date: 01-12-2023
Publisher: Springer US
Published in: Journal of Medical Systems / Issue 1/2023
Print ISSN: 0148-5598
Electronic ISSN: 1573-689X
DOI: https://doi.org/10.1007/s10916-023-01960-1

At a glance: The STEP trials

Springer Medicine

Real-time Classification of Fetal Status Based on Deep Learning and Cardiotocography Data

Abstract

At a glance: The STEP trials

Springer Medicine

Abstract

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