Comparison and verification of detection accuracy for late deceleration with and without uterine contractions signals using convolutional neural networks.

Abstract

Introduction: Cardiotocography (CTG) is used to monitor and evaluate fetal health by recording the fetal heart rate (FHR) and uterine contractions (UC) over time. Among these, the detection of late deceleration (LD), the early marker of fetal mild hypoxemia, is important, and the temporal relationship between FHR and UC is an essential factor in deciphering it. However, there is a problem with UC signals generally tending to have poor signal quality due to defects in installation or obesity in pregnant women. Since obstetricians evaluate potential LD signals only from the FHR signal when the UC signal quality is poor, we hypothesized that LD could be detected by capturing the morphological features of the FHR signal using Artificial Intelligence (AI). Therefore, this study compares models using FHR only (FHR-only model) and FHR with UC (FHR + UC model) constructed using a Convolutional Neural Network (CNN) to examine whether LD could be detected using only the FHR signal.

Methods: The data used to construct the CNN model were obtained from the publicly available CTU-UHB database. We used 86 cases with LDs and 440 cases without LDs from the database, confirmed by expert obstetricians.

Results: The results showed high accuracy with an area under the curve (AUC) of 0.896 for the FHR-only model and 0.928 for the FHR + UC model. Furthermore, in a validation using 23 cases in which obstetricians judged that the UC signals were poor and the FHR signal had an LD-like morphology, the FHR-only model achieved an AUC of 0.867.

Conclusion: This indicates that using only the FHR signal as input to the CNN could detect LDs and potential LDs with high accuracy. These results are expected to improve fetal outcomes by promptly alerting obstetric healthcare providers to signs of nonreassuring fetal status, even when the UC signal quality is poor, and encouraging them to monitor closely and prepare for emergency delivery.