DeepCTG® 2.0: Development and validation of a deep learning model to detect neonatal acidemia from cardiotocography during labor

Abstract

Cardiotocography (CTG) is the main tool available to detect neonatal acidemia during delivery. Presently, obstetricians and midwives primarily rely on visual interpretation, leading to a significant intra-observer variability. In this paper, we build and evaluate a convolutional neural network to detect neonatal acidemia from the CTG signals during delivery on a multicenter database with 27662 cases in five centers, including 3457 and 464 cases of moderate and severe neonatal acidemia respectively (defined by a fetal pH at birth between 7.05 and 7.20, and lower than 7.05 respectively). To use all the available records, the convolutional layers are pretrained on a task which consists in predicting several features known to be associated with neonatal acidemia from the raw CTG signals. In a cross-center evaluation, the AUC varies from 0.74 to 0.83 between the centers for the detection of severe acidemia, showing the ability of deep learning models to generalize from one dataset to the other and paving the way for more accurate models trained on larger databases. The model can still be significantly improved, by adding clinical variables to account for risk factors of acidemia that may not appear in the CTG signals. Further research will also be led to integrate the model in a tool that could assist humans in the interpretation of CTG.

Keywords: Cardiotocography; Computerized cardiotocography; Convolutional neural network; Deep learning; Fetal heart rate; Neonatal acidemia; Neonatal morbidity.