DeepCTG® 1.0: an interpretable model to detect fetal hypoxia from cardiotocography data during labor and delivery - Ben M’Barek, Imane; Jauvion, Grégoire; Vitrou, Juliette; Holmström, Emilia; Koskas, Martin; Ceccaldi, Pierre-François (Frontiers in Pediatrics, 2023)
Introduction Cardiotocography, which consists in monitoring the fetal heart rate as well as uterine activity, is widely used in clinical practice to assess fetal wellbeing during labor. We present DeepCTG® 1.0, a model able to predict fetal acidosis from the cardiotocography signals. Materials and methods DeepCTG® 1.0 is based on a logistic regression model fed with four features extracted from the last available 30-minutes segment of cardiotocography signals: the minimum and maximum values of the fetal heart rate baseline, and the area covered by accelerations and decelerations. Those four features have been selected among a larger set of 25 features. The model has been trained and evaluated on three datasets: the open CTU-UHB dataset, the SPaM dataset and a dataset built in hospital Beaujon (Clichy, France). Its performance has been compared with other published models and with nine obstetricians who have annotated the CTU-UHB cases. Results The AUC of the model is 0.74 on the CTU-UHB and Beaujon datasets, and between 0.77 and 0.87 on the SPaM dataset. It achieves a much lower false positive rate (12% vs 25%) than the most frequent annotation among the nine obstetricians for the same sensitivity (45%). The performance of the model is slightly lower on the cesarean cases only (AUC=0.74 vs 0.76) and feeding the model with shorter CTG segments leads to a significant decrease in its performance (AUC=0.68 with 10-minutes segments). Discussion Although being relatively simple, DeepCTG® 1.0 reaches a good performance: it compares very favorably to clinical practice and performs slightly better than other published models based on similar approaches. It has the important characteristic of being interpretable, as the four features it is based on are known and understood by practitioners. The model could be improved further by integrating maternofetal clinical factors, using more advanced machine learning or deep learning approaches and having a more robust evaluation of the model based on a larger dataset with more pathological cases and covering more maternity centers.