Prediction of IUGR condition at birth by means of CTG recordings and a ResNet model.

Journal: Computers in biology and medicine
PMID: 40184939

Abstract

OBJECTIVE: Sub-optimal uterine-placental perfusion and fetal nutrition can lead to intrauterine growth restriction (IUGR), also called fetal growth restriction (FGR). Antenatal cardiotocography (CTG) can aid in the early detection of IUGR. Reliably diagnosing IUGR before delivery remains challenging, and deep learning (DL) techniques offer potential solutions. This paper describes the development of a DL approach to predict an IUGR condition at birth by using CTG signals collected during antenatal monitoring.

Authors

  • Edoardo Spairani
    Department of Electrical, Computer and Biomedical Engineering, Università di Pavia, 27100, Pavia, Italy.
  • Giulio Steyde
    Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133, Milano, Italy. Electronic address: giulio.steyde@polimi.it.
  • Federica Spuri Forotti
    Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133, Milano, Italy.
  • Giovanni Magenes
    Dipartimento di Ingegneria Industriale e dell'Informazione, University of Pavia, 27100 Pavia, Italy. giovanni.magenes@unipv.it.
  • Maria G Signorini
    Department of Electronics, Information and Bioengineering (DEIB), Politecnico Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy. Electronic address: mariagabriella.signorini@polimi.it.

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