Neonatal asphyxia prediction using features extracted from cardiotocography data by explainable artificial intelligence

Abstract

Background and objective

Developing Artificial Intelligence (AI)-assisted technology for cardiotocography (CTG) monitoring system is highly anticipated in the field of obstetrics. This study developed a neonatal asphyxia prediction model to assist obstetricians and practitioners in making early treatment decisions in clinical practice.

Methods

Using 32,711 CTG records, features based on fetal heart rate (FHR) were extracted following Japanese Society of Obstetrics and Gynecology (JSOG) guidelines. The machine learning algorithm LightGBM was adopted to construct a binary prediction model of normal and abnormal states for newborns after delivery. To address the data imbalance between normal and abnormal samples, multiple prediction models were constructed using the underbagging technique. Furthermore, features impacting neonatal asphyxia were analyzed using the SHapley Additive exPlanations (SHAP), an explainable artificial intelligence (XAI) technique.

Results

The best prediction model used the Apgar score as the outcome variable and 13 FHR-based features + maternal age as the feature set, with an area under the curve of 0.759. This performance is reliable because this study used 32,711 CTG records, whereas most prior studies used datasets with only a few hundred records. When risk factors were analyzed via SHAP, the top three features were mean FHR, frequency of acceleration, and frequency of marked variability. The relationship between many of the features and abnormal risk corresponded to the CTG interpretation of the JSOG guidelines.

Conclusions

This study demonstrated reliable prediction performance using a large dataset along with the rationale behind its prediction. These results will facilitate the use of AI-assisted technology in clinical practice. In the future, it is expected that XAI technology will be integrated into real-time CTG monitoring systems, and that the display of associated risk factors will occur simultaneously with risk alerts.