Machine Learning Model for Classifying the Results of Fetal Cardiotocography Conducted in High-Risk Pregnancies

Abstract

Purpose Fetal well-being is usually assessed via fetal heart rate (FHR) monitoring during the antepartum period. However, the interpretation of FHR is a complex and subjective process with low reliability. This study developed a machine learning model that can classify fetal cardiotocography results as normal or abnormal. Materials and Methods In total, 17492 fetal cardiotocography results were obtained from Ajou University Hospital and 100 fetal cardiotocography results from Czech Technical University and University Hospital in Brno. Board-certified physicians then reviewed the fetal cardiotocography results and labeled 1456 of them as gold-standard; these results were used to train and validate the model. The remaining results were used to validate the clinical effectiveness of the model with the actual outcome. Results In a test dataset, our model achieved an area under the receiver operating characteristic curve (AUROC) of 0.89 and area under the precision-recall curve (AUPRC) of 0.73 in an internal validation dataset. An average AUROC of 0.73 and average AUPRC of 0.40 were achieved in the external validation dataset. Fetus abnormality score, as calculated from the continuous fetal cardiotocography results, was significantly associated with actual clinical outcomes [intrauterine growth restriction: odds ratio, 3.626 ( p =0.031); Apgar score 1 min: odds ratio, 9.523 ( p <0.001), Apgar score 5 min: odds ratio, 11.49 ( p =0.001), and fetal distress: odds ratio, 23.09 ( p <0.001)]. Conclusion The machine learning model developed in this study showed precision in classifying FHR signals. This suggests that the model can be applied to medical devices as a screening tool for monitoring fetal status.

Machine learning and disease prediction in obstetrics

“…Here 23 attributes were dynamically programmed and fed through the algorithm with accuracy (99.7%). In the last year, many promising studies have been published within this area of research ( Fei et al, 2022 ; Muhammad Hussain et al, 2022 ; Mehbodniya et al, 2022 ; Park et al, 2022 ; Cheng et al, 2022 ).…”

Machine Learning Prediction of Fetal Health Status from Cardiotocography Examination in Developing Healthcare Contexts

“…17,592 fetal CTG records were obtained from three teaching hospitals, which were divided into training and validation sets. The model achieved an average area under the receiver operating characteristic curve (AUROC) of 0.73 and an area under the precision-recall curve (AUPRC) of 0.40 in the external validation dataset [20] . It's noteworthy that the study did not carry out a feature selection process to identify relevant features for fetal health status model building and prediction.…”

Development and Evaluation of Deep Learning Models for Cardiotocography Interpretation

“…Current deep learning methods for CTG interpretation, which use the physiological time series data as input, rely on proxy labels for fetal well-being recorded immediately after delivery: the umbilical artery blood pH and the 1-minute Apgar score [17][18][19][20][21][22][23]. Umbilical cord blood pH at the time of birth, often used in high-resource medical facilities, is presently the only objective quantification for the potential occurrence of fetal hypoxia during labor.…”