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Preterm EEG: A Multimodal Neurophysiological Protocol
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A Neural Network-based Approach to Prediction of Preterm Birth using Non-invasive Tests.

Masoumeh Mirzamoradi1, Hamid Mokhtari Torshizi2, Masoumeh Abaspour1

  • 1Department of Perinatology, Mahdieh Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Journal of Biomedical Physics & Engineering
|October 11, 2024
PubMed
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An artificial neural network (ANN) effectively predicts preterm birth (PB) risk using questionnaire data. This early identification aids timely interventions, reducing infant complications and mortality.

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Maternal-Fetal Medicine

Background:

  • Preterm birth (PB) is a leading cause of neonatal mortality and infant health complications.
  • Reliable and accurate prediction methods for preterm labor are currently lacking.
  • Early prediction is crucial for timely medical intervention to improve infant outcomes.

Purpose of the Study:

  • To propose an artificial neural network (ANN)-based approach for the early prediction of preterm birth (PB).
  • To enable earlier physician intervention, thereby reducing infant morbidity and mortality.
  • To develop a non-invasive prediction model using readily available risk factors.

Main Methods:

  • A historical cohort study involving 300 pregnant women (150 with PB, 150 normal controls).
Keywords:
Artificial Neural NetworkMachine LearningPregnancyPremature BirthPreterm DeliveryPreterm Labor

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  • A feed-forward artificial neural network (ANN) with 7 hidden neurons was utilized.
  • Thirteen PB risk factors, collected via questionnaire, served as ANN inputs for training, validation, and testing.

    • Main Results:

    • The ANN model achieved an overall accuracy of 79.03% in classifying normal and preterm birth cases.
    • Sensitivity was 73.45% and specificity was 84.62% for predicting preterm birth.
    • The prediction model effectively utilized risk factors obtainable through a simple questionnaire, avoiding lab tests.

    Conclusions:

    • The proposed ANN approach demonstrates efficacy in the early identification of high-risk pregnancies for preterm delivery.
    • Early identification facilitates timely clinical interventions during pregnancy, improving maternal and infant health outcomes.
    • This AI-driven method offers a practical tool for proactive management of preterm birth risk.