Comprehensive assessment of uterine contractility using a large database of dynamic T2∗ studies.

Introduction: Uterine contractile activity, essential for vaginal birth, begins early in gestation, but quantitative assessment remains limited. T2∗ relaxometry detects placental oxygenation changes during contractions, offering insights into placental function. This study uses a large dynamic T2∗ MRI database and an AI-driven pipeline to assess contractile activity, quantify prevalence, and explore links with clinical and scanning variables. By leveraging real-time deep learning, it aims to enhance understanding of subclinical contractions and their impact on placental function and antenatal care.

Methods: A total of 821 dynamic fetal MRI scans were analyzed from pregnancies between 15 and 41 weeks of gestation, including both uncomplicated pregnancies and those affected by placental insufficiency. An automated pipeline incorporating deep-learning-based placental and fetal brain segmentation, as well as dynamic signal analysis, was used to evaluate uterine contractility.

Results: Contractile activity was detected in 19 % of cases, showing no significant correlation with field strength, maternal position during the scan, parity, maternal age, or body mass index. However, activity increased with advancing gestational age, peaking notably in the week before delivery. The observed variations in T2∗ values between contractile states underscore the importance of accounting for dynamic uterine activity in placental MRI analysis.

Discussion: Dynamic T2∗ MRI assessment of uterine contractility provides novel insights into subclinical uterine contractions and establishes a foundation for real-time detection in the future. This approach could significantly enhance our in vivo understanding of placental function during contractions, particularly in pregnancies affected by placental disease, ultimately improving prenatal monitoring and clinical management.