Preterm Labor:
Innovative research by Singh et al., studied the combination of AI and amniotic fluid (AF) proteomics and metabolomics, in conjunction or independently with imaging, demographic and clinical factors, to predict perinatal outcome in asymptomatic women with short cervix length [14]. The type of AI they used is called deep learning (DL). This subtype of AI can operate with a larger amount of data because it has a greater number of neural networks, which makes it ideal for biological system studies involving multiomics. Currently, the short cervical length is the strongest risk factor for prematurity, however, many women with this condition carry their pregnancy to term. Many centers now incorporate amniocentesis in these women to evaluate additional factors that might put them at risks, such as inflammation and infectious processes. The AF of the subjects was additionally studied for omics, such as metabolomics, to shed light on potential new biomarkers that might be involved in preterm birth. This can improve the accuracy and predictive value of women at risk of poor outcomes, and it can help physicians stratify those patients at risk of preterm birth better than the current risk factors such as short cervical length and prior preterm birth delivery [15]. In this way, physicians can use this tool to guide their management, such as observation alone, or suggesting cervical cerclage and or antenatal steroids if deemed necessary. A shortcoming of the study was the small size of the group. The study concluded that DL was a superior tool when it came to the prediction of perinatal outcome in asymptomatic women with short cervix length and that further studies are needed to analyze AF omics and its relationship to premature shortening of the cervix to help guide management in these patients.
A study done by Idowu et al, emphasizes the importance of using AI technology to decrease expenses generated by inaccurate detection of preterm labor leading to unnecessary hospitalizations and procedures, and at the meantime, expedite treatment in those who are in true labor to prevent hazardous consequences for the baby and the mother [16]. In this study, they used electro-hysterography signals and used three distinct machine learning algorithms to classify these signals to help them identify true labor and accurately diagnose preterm labor. They concluded that the Random Forest algorithm performed the most efficiently of the three machines tested, as it was able to handle a larger amount of data, is relatively accurate, and has a robust learning capacity which resulted in an accuracy of 97 percent in predicting preterm labor.