This study reviews the current status and future prospective of knowledge on the use of artificial intelligence for the prediction of spontaneous preterm labor and birth (“preterm birth” hereafter). The summary of review suggests that different machine learning approaches would be optimal for different types of data regarding the prediction of preterm birth: the artificial neural network, logistic regression and/or the random forest for numeric data; the support vector machine for electrohysterogram data; the recurrent neural network for text data; and the convolutional neural network for image data. The ranges of performance measures were 0.79–0.94 for accuracy, 0.22–0.97 for sensitivity, 0.86–1.00 for specificity, and 0.54–0.83 for the area under the receiver operating characteristic curve. The following maternal variables were reported to be major determinants of preterm birth: delivery and pregestational body mass index, age, parity, predelivery systolic and diastolic blood pressure, twins, below high school graduation, infant sex, prior preterm birth, progesterone medication history, upper gastrointestinal tract symptom, gastroesophageal reflux disease, Helicobacter pylori, urban region, calcium channel blocker medication history, gestational diabetes mellitus, prior cone biopsy, cervical length, myomas and adenomyosis, insurance, marriage, religion, systemic lupus erythematosus, hydroxychloroquine sulfate, and increased cerebrospinal fluid and reduced cortical folding due to impaired brain growth.

Lee KS, et al. J Korean Med Sci. 2019 Apr;34(16):e128. https://doi.org/10.3346/jkms.2019.34.e128

This study reviews recent advances on the application of artificial intelligence for the early diagnosis of various maternal-fetal conditions such as preterm birth and abnormal fetal growth. It is found in this study that various machine learning methods have been successfully employed for different kinds of data capture with regard to early diagnosis of maternal-fetal conditions. With the more popular use of artificial intelligence, ethical issues should also be considered accordingly.

Lee KS, et al. J Korean Med Sci. 2020 Feb;35(16):e105. https://doi.org/10.3346/jkms.2020.35.e105

This study investigated whether anterior placenta previa in the second trimester is associated with neonatal respiratory distress syndrome (RDS). The neonates delivered by 2067 women between 2007 and 2015 were evaluated for the presence of RDS through birth records. The location of the placenta and the presence of placenta previa during the second and third trimesters were assessed and recorded. Demographic, prenatal, and perinatal records were reviewed. Anterior placenta previa in the second and third trimesters was correlated with RDS. Infants with lower gestational ages and birth weights had higher rates of RDS. Multivariate logistic regression analysis identified a significant association between anterior placenta previa in the second trimester and neonatal RDS. Anterior placenta previa in the second trimester is associated with neonatal RDS. Obstetricians should be aware that anterior placenta previa detected during the second trimester, irrespective of whether the placenta will migrate in the third trimester, may be an independent risk factor for neonatal RDS.

Background: This study introduced machine learning approaches to predict newborn’s body mass index (BMI) based on ultrasound measures and maternal/delivery information. Methods: Data came from 3159 obstetric patients and their newborns enrolled in a multi-center retrospective study. Variable importance, the effect of a variable on model performance, was used for identifying major predictors of newborn’s BMI among ultrasound measures and maternal/delivery information. The ultrasound measures included biparietal diameter (BPD), abdominal circumference (AC) and estimated fetal weight (EFW) taken three times during the week 21 - week 35 of gestational age and once in the week 36 or later. Results: Based on variable importance from the random forest, major predictors of newborn’s BMI were the first AC and EFW in the week 36 or later, gestational age at delivery, the first AC during the week 21 - the week 35, maternal BMI at delivery, maternal weight at delivery and the first BPD in the week 36 or later. For predicting newborn’s BMI, linear regression (2.0744) and the random forest (2.1610) were better than artificial neural networks with one, two and three hidden layers (150.7100, 154.7198 and 152.5843, respectively) in the mean squared error. Conclusions: This is the first machine-learning study with 64 clinical and sonographic markers for the prediction of newborns’ BMI. The week 36 or later is the most effective period for taking the ultrasound measures and AC and EFW are the best predictors of newborn’s BMI alongside gestational age at delivery and maternal BMI at delivery.