Introduction: The fetal origins of adult disease, widely known as Barker’s Hypothesis, suggest that adverse fetal environments significantly impact the risk of developing chronic diseases, such as diabetes and cardiovascular conditions, in adulthood. Recent advancements in 4D ultrasound (4D US) and artificial intelligence (AI) technologies offer a promising avenue for improving prenatal diagnostics and validating this hypothesis. These innovations provide detailed insights into fetal behavior and neurodevelopment, linking early developmental markers to long-term health outcomes. Content: This study synthesizes contemporary developments in AI-enhanced 4D US, focusing on their roles in detecting fetal anomalies, assessing neurodevelopmental markers, and evaluating congenital heart defects. The integration of AI with 4D US allows for real-time, high-resolution visualization of fetal anatomy and behavior, surpassing the diagnostic precision of traditional methods. Despite these advancements, challenges such as algorithmic bias, data diversity, and real-world validation persist and require further exploration. Summary: Findings demonstrate that AI-driven 4D US improves diagnostic sensitivity and accuracy, enabling earlier detection of fetal abnormalities and optimization of clinical workflows. By providing a more comprehensive understanding of fetal programming, these technologies substantiate the links between early-life conditions and adult health outcomes, as proposed by Barker’s Hypothesis. Outlook: The integration of AI and 4D US has the potential to revolutionize prenatal care, paving the way for personalized maternal-fetal healthcare. Future research should focus on addressing current limitations, including ethical concerns and accessibility challenges, to promote equitable implementation. Such advancements could significantly reduce the global burden of chronic diseases and foster healthier generations.

Artificial intelligence (AI) has emerged as a transformative technology in the field of healthcare, offering significant advancements in various medical disciplines, including obstetrics. The integration of artificial intelligence into 3D/4D ultrasound analysis of fetal facial profiles presents numerous benefits. By leveraging machine learning and deep learning algorithms, AI can assist in the accurate and efficient interpretation of complex 3D/4D ultrasound data, enabling healthcare providers to make more informed decisions and deliver better prenatal care. One such innovation that has significantly improved the analysis of fetal facial profiles is the integration of AI in 3D/4D ultrasound imaging. In conclusion, the integration of artificial intelligence in the analysis of 3D/4D ultrasound data for fetal facial profiles offers numerous benefits, including improved accuracy, consistency, and efficiency in prenatal diagnosis and care.

Aim: To investigate the influence of antenatal magnesium sulfate (MgSO₄) application on cord blood brain-derived neurotrophic factor (BDNF) levels – the first-line neuroprotection for preventing cerebral palsy in prematurely born infants. Subjects and methods: A randomized controlled trial was conducted by observing 72 pregnant women who were divided into three groups: group I (preterm pregnancy with MgSO₄), group II (preterm pregnancy without MgSO₄), and group III (full-term pregnancy as control group). Groups I and II were selected by block permutation randomization on subjects. Inclusion criteria consisted of preterm pregnancy at 34 weeks of gestation or less who were in labor or having planned terminations and receiving antenatal corticosteroids. Exclusion criteria consisted of previous complications caused by MgSO₄, previous history of antenatal MgSO₄ application in the current pregnancy infant was born before 4 h administration of MgSO₄ or unborn more than 72 h after maximum course of antenatal MgSO₄ of 24 h, prolonged antenatal MgSO₄ treatment (>24 h), refusal to participate, and emergent adverse events during the study. Group I was given intravenous MgSO₄; initial dose was 4 g, which was maintained at 1 g/h up to maximum of 24 h. Meanwhile, groups II and III were not given any special treatment. BDNF was examined by ELISA by taking 5 mL cord blood sample shortly after birth. The result was statistically measured by ANOVA. Results: The cord blood BDNF levels in premature infants with antenatal MgSO₄ was significantly higher than in premature infants without antenatal MgSO₄ (11,568 vs. 5027 pg/mL, P=0.000). Moreover, the result was statistically comparable to full-term infants (11,568 vs. 13,300 pg/mL, P=0.085). Conclusion: The application of antenatal MgSO₄ in preterm delivery increased cord blood BDNF levels, which could have a potential role on fetal neuroprotection. Further investigation is needed.

The advent of non-invasive prenatal testing (NIPT) utilizing cell-free fetal DNA (cfDNA) has transformed the landscape of early chromosomal anomaly detection. When paired with high-resolution ultrasound imaging, it establishes a robust framework for prenatal diagnostics. This study explores the efficacy of merging NIPT findings with detailed ultrasound markers to enhance the identification of both chromosomal and structural fetal abnormalities. Data from 190 cases demonstrated a cfDNA efficacy rate of 91.58 % (cfDNA ≥4 %) and a detection rate of 4.74 % for aneuploidies. The investigation delves into key findings for trisomies, monosomies, and physical malformations, backed by state-of-the-art diagnostic benchmarks. Markers such as nuchal translucency (NT), craniofacial characteristics, and cardiac irregularities were analyzed alongside genetic results. This integrative strategy significantly refines diagnostic precision, paving the way for personalized prenatal care and management.

The development of facial musculature and expressions in the human fetus represents a critical intersection of developmental biology, neurology, and evolutionary anthropology, offering insights into early neurological and social development. Fetal facial expressions, shaped by Cranial Nerve VII, reflect evolutionary adaptations for nonverbal communication and exhibit minimal asymmetry in universal expressions. Advancements in 4D ultrasound imaging and artificial intelligence (AI) have introduced innovative methods for analyzing these movements, revealing their potential as diagnostic tools for neurodevelopmental disorders like Bell’s Palsy and Ramsay Hunt Syndrome before birth. These technologies promise early interventions that could significantly improve neonatal outcomes. By integrating imaging, AI, and longitudinal studies, researchers propose a multidisciplinary approach to establish diagnostic criteria for fetal facial movements. However, translating these advancements into clinical practice requires addressing ethical and practical challenges, refining imaging and AI methodologies, and fostering interdisciplinary collaboration. The review highlights the universality of fetal expressions while emphasizing the importance of distinguishing typical variability from pathological markers. In conclusion, these findings suggest transformative potential for maternal-fetal medicine, paving the way for proactive strategies to manage neurodevelopmental risks. Focused research is essential to fully harness these innovations and establish a new frontier in perinatal science.

Introduction: Maternal nutrition during pregnancy can influence fetal development through epigenetic modifications, affecting gene expression without altering DNA sequence. Nutriepigenomics – the study of nutrient-driven epigenetic regulation – provides critical insights into how prenatal nutritional exposures can shape immediate and lifelong health outcomes. Content: This narrative review synthesizes evidence from human cohort studies and experimental animal models on how macro- and micronutrients, including folate, vitamin B12, choline, vitamin D, omega-3 fatty acids, and bioactive compounds such as polyphenols and resveratrol, modify key epigenetic processes. These include DNA methylation, histone modifications, and non-coding RNA regulation, particularly within the placenta and developing fetal tissues. Summary: Maternal diet-induced epigenetic changes influence fetal metabolic programming, neurodevelopment, immune maturation, and organogenesis, with impacts detectable at birth and persisting into adulthood. Evidence indicates associations with altered birthweight trajectories, increased risk of childhood obesity and immune dysregulation, and potential elevation in lifelong cardiometabolic and neuropsychiatric disease risk. Outlook: Integrating nutriepigenomic insights into perinatal care offers opportunities for early preventive strategies and personalized nutrition interventions. Translational application of epigenetic biomarkers, coupled with population-level nutritional policies, could reduce disease risk across generations and improve long-term population health outcomes.