A novel artificial intelligence approach for the automatic differentiation of fetal occiput anterior and non-occiput anterior positions during labor

Objectives: The aim of this study is to develop a Machine Learning (ML) algorithm for an automatic classification of fetal occiput position at transperineal ultrasound (TPU) during the second stage of labor. Methods: Prospective cohort study including singleton term pregnancies (> 37 weeks of gestation) in the second stage of labor, with the fetus in cephalic presentation. Transabdominal ultrasound was preliminarily performed to assess the actual fetal occiput position, which was labeled as occiput anterior (OA) or non-occiput anterior (non-OA). Subsequently, for each case, one sonographic image of the fetal head was acquired on the axial plane using TPU and archived on a cloud for remote analysis. Using the transabdominal sonographic diagnosis as the gold standard, a ML algorithm based on a pattern recognition feed-forward neural network was trained on the transperineal images to discriminate between OA and non-OA cases. In the training phase the model tuned its parameters in order to approximate correctly the training data - i.e., the training dataset - in order to correctly assess the fetal head position, by exploiting geometric, morphological and intensity-based features of the images. In the testing phase, the diagnostic performance of the algorithm was evaluated on unlabeled data, which represented the testing dataset. On this group the ability of the ML algorithm to differentiate the OA from the non-OA fetal positions was assessed in terms of diagnostic accuracy. The F1 -score and Precision-Recall Area Under the Curve (PR-AUC) were also calculated to assess the algorithm's performance. The Cohen's kappa (k) was finally added to evaluate the agreement between the algorithm and the gold standard. Results: Over a period of 24 months, 1219 women in the second stage of labor were enrolled. They were classified as OA (n=801 or 65.7%) or non-OA (n=418 or 34.3%) on the basis of transabdominal ultrasound. From both the sub-groups (OA and non-OA), 70% of the patients were randomly assigned to the training dataset (824 patients) while the remaining 30% (395 patients) were used as testing dataset. On the latter group the ML based algorithm yielded a correct classification of the fetal occiput position in 90.6% of cases (357 out of 395), including 224 out of 246 OA (91.0%) and of 133 out of 149 non-OA images (89.3%). Moreover, for the evaluation the algorithm's performance we found a F1 -score=88.7% and PR-AUC=85.4%. The algorithm showed a balanced performance in the recognition of both anterior and non-anterior occiput positions. Eventually, the robustness of the proposed algorithm was confirmed by a high agreement with the gold standard method (k = 0.81; p < 0.0001). Conclusion: A ML-based algorithm for the automatic assessment of the fetal head position at TPU has been developed and can accurately differentiate between OA and non-OA positions. This algorithm has the potential to support not only obstetricians, but also midwives and accoucheurs in the clinical use of TPU. This article is protected by copyright. All rights reserved.