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Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph
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Deep neural network-based classification of cardiotocograms outperformed conventional algorithms.

Jun Ogasawara1, Satoru Ikenoue2, Hiroko Yamamoto3

  • 1Department of Pharmacology, School of Medicine, Keio University, Tokyo, 160-8582, Japan.

Scientific Reports
|June 29, 2021
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Summary

A new deep neural network, CTG-net, offers an objective method for evaluating fetal heart rate (FHR) and uterine contraction (UC) patterns. This automated system aids in detecting compromised fetal status, reducing potential hypoxic injuries.

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Area of Science:

  • Obstetrics and Gynecology
  • Medical Imaging and Signal Processing
  • Artificial Intelligence in Medicine

Background:

  • Cardiotocography (CTG) is crucial for assessing fetal well-being, monitoring fetal heart rates (FHR) and uterine contractions (UC).
  • Manual interpretation of CTG traces by obstetricians can be subjective, leading to diagnostic variability and potentially inappropriate interventions.
  • There is a need for quantitative, unbiased algorithms to improve the accuracy of CTG evaluation.

Purpose of the Study:

  • To develop and evaluate a novel deep neural network, CTG-net, for the automated detection of compromised fetal status using CTG data.
  • To quantitatively assess fetal well-being by analyzing temporal patterns and interrelationships between FHR and UC signals.
  • To compare the performance of CTG-net against conventional algorithms and other deep learning models.

Main Methods:

  • A deep neural network (CTG-net) comprising three convolutional layers was designed to extract features from CTG signals.
  • CTG data was used to classify fetuses into abnormal (umbilical artery pH < 7.20 or Apgar score < 7 at 1 min) and normal groups.
  • Performance was evaluated using the F1 score and the area under the receiver operating characteristic curve (AUC), with comparisons to support vector machine (SVM), k-means clustering, and long short-term memory (LSTM) models.

Main Results:

  • CTG-net achieved an AUC of 0.73 ± 0.04, demonstrating significantly superior performance compared to the LSTM model.
  • The F1 score was utilized to assess classification accuracy, indicating the model's effectiveness in distinguishing between normal and abnormal fetal states.
  • The proposed CTG-net system showed potential for accurate and objective CTG interpretation.

Conclusions:

  • CTG-net provides a quantitative and automated approach to fetal status assessment, addressing the subjectivity of manual CTG interpretation.
  • Early and accurate identification of compromised fetuses through CTG-net can facilitate timely interventions.
  • The implementation of such AI-driven systems may lead to a reduction in adverse outcomes, such as hypoxic injury.