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

TL;DRAbstract

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 ot

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