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. 2024 Jun 28;11(7):658.
doi: 10.3390/bioengineering11070658.

The Approach to Sensing the True Fetal Heart Rate for CTG Monitoring: An Evaluation of Effectiveness of Deep Learning with Doppler Ultrasound Signals

Yuta Hirono  1   2 , Ikumi Sato  1   3 , Chiharu Kai  1   4 , Akifumi Yoshida  4 , Naoki Kodama  4 , Fumikage Uchida  2 , Satoshi Kasai  4
Affiliations

The Approach to Sensing the True Fetal Heart Rate for CTG Monitoring: An Evaluation of Effectiveness of Deep Learning with Doppler Ultrasound Signals

Yuta Hirono et al. Bioengineering (Basel). .

Abstract

Cardiotocography (CTG) is widely used to assess fetal well-being. CTG is typically obtained using ultrasound and autocorrelation methods, which extract periodicity from the signal to calculate the heart rate. However, during labor, maternal vessel pulsations can be measured, resulting in the output of the maternal heart rate (MHR). Since the autocorrelation output is displayed as fetal heart rate (FHR), there is a risk that obstetricians may mistakenly evaluate the fetal condition based on MHR, potentially overlooking the necessity for medical intervention. This study proposes a method that utilizes Doppler ultrasound (DUS) signals and artificial intelligence (AI) to determine whether the heart rate obtained by autocorrelation is of fetal origin. We developed a system to simultaneously record DUS signals and CTG and obtained data from 425 cases. The midwife annotated the DUS signals by auditory differentiation, providing data for AI, which included 30,160 data points from the fetal heart and 2160 data points from the maternal vessel. Comparing the classification accuracy of the AI model and a simple mathematical method, the AI model achieved the best performance, with an area under the curve (AUC) of 0.98. Integrating this system into fetal monitoring could provide a new indicator for evaluating CTG quality.

Keywords: AI; Doppler ultrasound; fetal heart rate; maternal heart rate.

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Conflict of interest statement

This work was supported by TOITU Co., Ltd. Authors Y.H. and F.U. are employed by TOITU Co., Ltd., and receive a salary. I.S. has a consulting agreement with TOITU Co., Ltd. S.K. has received research funding from TOITU Co., Ltd.

Figures

Figure 1
Figure 1
Flowchart of labeling data.
Figure 2
Figure 2
AI-model architecture for 1D-CNN.
Figure 3
Figure 3
Comparison of AI model and threshold-based ROC curves.
Figure 4
Figure 4
Color bar of AI output and FHR/MHR displayed in parallel. An illustration of simultaneous monitoring values and AI determination results, with FHR denoted by a blue line and MHR by an orange line. At the top of the graph, a color bar indicates AI output results: fetal source displayed in blue and vessel source in red.
Figure 5
Figure 5
Examples of DUS signals are classified by the midwife. (a) Example of “From Fetal heart” (b) Example of “From vessel”. The signal lengths of (a,b) are both 1 s. Both examples record 2 beats.
See this image and copyright information in PMC

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