An Artificial Intelligence Approach to Fetal Health Risk Prediction

Abstract

The use of artificial intelligence (AI) in obstetrics has the potential to improve the prediction and monitoring of fetal health, which could help reduce maternal and infant mortality rates. This study uses IBM Watson to predict fetal health by analyzing data from cardiotocography (a recording of the fetal heart rate based on ultrasound). The data contains information on fetal movement, accelerations, and the mother’s uterine contractions. Fetal movement and uterine contractions are two important indicators of fetal well-being. However, monitoring these factors in a traditional manner can be subjective and may not provide a complete picture of fetal health. By using AI to analyze data from these sources, we aim to identify patterns and make more accurate predictions about the health of the fetus. We conducted a prospective observational study to evaluate the effectiveness of using IBM Watson to predict fetal health. The data for this study was borrowed from the University of Porto, which consists of 2126 pregnant women whose fetal movement and uterine contractions have been monitored throughout their pregnancy using a combination of ultrasound and tocodynamometry. IBM Watson was then used to analyze this data and make models to give predictions about fetal health. The primary outcome measure of this study was the maternal and infant mortality rates. This study also demonstrated the effectiveness of using IBM Watson to develop models using multiple algorithms in an optimized way to obtain high prediction accuracies.

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