Ben M'Barek Imane, Jauvion Grégoire, Vitrou Juliette, Holmström Emilia, Koskas Martin, Ceccaldi Pierre-François
Department of Gynecology Obstetrics, Assistance Publique des Hôpitaux de Paris -Beaujon, Clichy, France.
Health Simulation Department, iLumens, Université Paris Cité, Paris, France.
Front Pediatr. 2023 Jun 15;11:1190441. doi: 10.3389/fped.2023.1190441. eCollection 2023.
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 and delivery in order to detect fetal hypoxia and intervene before permanent damage to the fetus. We present DeepCTG® 1.0, a model able to predict fetal acidosis from the cardiotocography signals.
DeepCTG® 1.0 is based on a logistic regression model fed with four features extracted from the last available 30 min 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. We have also evaluated the impact of two key factors on the performance of the model: the inclusion of cesareans in the datasets and the length of the cardiotocography segment used to compute the features fed to the model.
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 min segments).
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.
胎心宫缩图监测包括胎儿心率和子宫活动，在临床实践中被广泛用于评估分娩期间胎儿的健康状况，以检测胎儿缺氧并在胎儿受到永久性损伤之前进行干预。我们展示了DeepCTG® 1.0，这是一种能够根据胎心宫缩图信号预测胎儿酸中毒的模型。
DeepCTG® 1.0基于一个逻辑回归模型，该模型输入从胎心宫缩图信号的最后30分钟可用段中提取的四个特征：胎儿心率基线的最小值和最大值，以及加速和减速所覆盖的面积。这四个特征是从一组更大的25个特征中挑选出来的。该模型在三个数据集上进行了训练和评估：开放的CTU-UHB数据集、SPaM数据集以及在法国克利希的博若莱医院建立的一个数据集。其性能与其他已发表的模型以及九位对CTU-UHB病例进行注释的产科医生的表现进行了比较。我们还评估了两个关键因素对模型性能的影响：数据集中剖宫产病例的纳入情况以及用于计算输入模型特征的胎心宫缩图段的长度。
该模型在CTU-UHB和博若莱数据集上的AUC为0.74，在SPaM数据集上的AUC在0.77至0.87之间。在相同灵敏度（45%）下，它的假阳性率（12%对25%）比九位产科医生中最常见的注释要低得多。仅在剖宫产病例中，模型的性能略低（AUC = 0.74对0.76），并且用较短的胎心宫缩图段输入模型会导致其性能显著下降（10分钟段的AUC = 0.68）。
尽管DeepCTG® 1.0相对简单，但性能良好：与临床实践相比非常出色，并且基于类似方法比其他已发表的模型表现略好。它具有可解释的重要特征，因为其基于的四个特征是从业者所熟知和理解的。通过整合母婴临床因素、使用更先进的机器学习或深度学习方法以及基于包含更多病理病例和覆盖更多产科中心的更大数据集对模型进行更稳健的评估，可以进一步改进该模型。
