Fetal origins of adult disease: transforming prenatal care by integrating Barker's Hypothesis with AI-driven 4D ultrasound.
Authors
Affiliations (8)
Affiliations (8)
- Department of Obstetrics and Gynecology, Maternal-Fetal Medicine Division, Women Health Center, Ekahospital, Tangerang, Banten, Indonesia.
- Department of Obstetrics and Gynecology, Faculty of Medicine, Maternal-Fetal Medicine Division, Sebelas Maret University, Dr. Moewardi General Hospital, Solo, Indonesia.
- Department of Obstetrics and Gynecology, Faculty of Medicine, Maternal-Fetal Medicine Division, Airlangga University, Dr. Soetomo General Hospital, Surabaya, Indonesia.
- Department of Obstetrics and Gynecology, Faculty of Medicine, Maternal-Fetal Medicine Division, Padjadjaran University, Hasan Sadikin General Hospital, Bandung, Indonesia.
- Department of Obstetrics and Gynecology, Faculty of Medicine, Maternal-Fetal Medicine Division, Diponegoro University, Dr. Kariadi General Hospital, Semarang, Indonesia.
- Department of Obstetrics and Gynecology, Faculty of Medicine, Maternal-Fetal Medicine Division, Sebelas Maret University, Sebelas Maret University Hospital, Solo, Indonesia.
- Department of Neonatology and Rare Diseases, Medical University of Warsaw, Warsaw, Poland.
- Department of Obstetrics and Gynecology, Medical School University of Zagreb, Zagreb, Croatia.
Abstract
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. 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. 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. 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.