## 1 Introduction
All over the world, and despite the modern development and availability of medical resources, it is worth noting that maternal mortality is still rife and was estimated to be at 287,000 cases in 2020. Maternal health outcomes are influenced not only through biomedical factors but also social, environmental, and systemic determinants; therefore, holistic, multi-faceted, and advanced approaches are required for managing the condition beyond clinical medicine [6]. Globally, the community healthcare system considers maternal health as one of the critical success factors for social and economic growth. However, complications during pregnancy remain the major cause of hospitalization and mortality among pregnant women, particularly in countries with low to middle incomes (LMICs). In 2017, approximately 295,000 women died following childbirth according to a WHO report. Statistics indicate that 94% of deaths belong to LMIC countries [7]. The adoption of machine learning and deep learning in the management of maternal healthcare leads to the early prediction of potential high-risk complications, such as preterm labor. In [8], the ability of ML algorithms to analyze the patient records and data from wearable sensors to predict early labor signs accurately has been demonstrated. However, there are currently systems used to monitor pregnancy that need several devices to be connected to a base unit to monitor the number of objectives, including blood oxygenation, temperature, blood pressure, heart rate, fetal heart rate, and uterine contractions [9]. Another of the main application areas of maternal care includes the use of methods and tools to detect fetal and maternal diseases. Due to the innovative utility of deep learning, especially convolutional neural networks (CNNs), ultrasonic visualization and examination have improved to make precise diagnoses of fetal structural abnormalities and placental pathologies. Recall that CNNs have obtained accuracy rates of 92% in the task of defect detection in ultrasound images [10]. A precise image database has been used to a great extent by conducting rigorous research to create procedures that can identify disorders such as spina bifida and cardiac defects. In the same manner, wearable sensor technologies that integrate deep learning extract the vital signs of the mother, such as blood pressure and heart rate, to monitor the clinical well-being of the mother [11]. Owing to technological improvements in fetal MRI in recent years, the capabilities of describing the microstructures and patterns of fetal brain development in utero with higher spatial resolution have become more advanced. However, the manual segmentation process of fetal brain structure configuration is time-consuming and prone to variability. This has made it possible to segment fetal brain images through deep learning techniques such as CNNs and the U-Net family in dealing with aspects such as motion artifacts, low tissue contrast, and limited annotated samples [12]. A supplementary aspect of maternal care is the mental health of the woman during her pregnancy. Naturally, recent works have been devoted to the integration of machine learning approaches with methods of prenatal mental health screening that can contribute to the early stages of clinical practice. For instance, [PERSON] et al. investigated an interpretable model of using first-trimester structured patient-reported data for early-onset high-risk moderate to severe depression during pregnancy. By incorporating such psychosocial factors as food insecurity, the model performed well in terms of predictive accuracy and remained easy to implement, thus underlining the role of mobile-app-based tools in early mental health screening [13]. The study conducted by [PERSON] et al. [14] is one of the few that applied the approach of machine learning for early pregnancy loss (EPL) prediction across women who had recurrent pregnancy loss (RPL) based on preconception data only. [PERSON] et al.