Background— Fetal growth restriction (FGR) affects 5% to 10% of newborns and is associated with increased cardiovascular mortality in adulthood. The most commonly accepted hypothesis is that fetal metabolic programming leads secondarily to diseases associated with cardiovascular disease, such as obesity, diabetes mellitus, and hypertension. Our main objective was to evaluate the alternative hypothesis that FGR induces primary cardiac changes that persist into childhood. Methods and Results— Within a cohort of fetuses with growth restriction identified in fetal life and followed up into childhood, we randomly selected 80 subjects with FGR and compared them with 120 normally grown fetuses, matched for gender, birth date, and gestational age at birth. Cardiovascular assessment was performed in childhood (mean age of 5 years). Compared with control subjects, children with FGR had a different cardiac shape, with increased transversal diameters and more globular cardiac ventricles. Although left ejection fraction was similar among the study groups, stroke volume was reduced significantly, which was compensated for by an increased heart rate to maintain output in severe FGR. This was associated with subclinical longitudinal systolic dysfunction (decreased myocardial peak velocities) and diastolic changes (increased E/E′ ratio and E deceleration time). Children with FGR also had higher blood pressure and increased intima-media thickness. For all parameters evaluated, there was a linear increase with the severity of growth restriction. Conclusions— These findings suggest that FGR induces primary cardiac and vascular changes that could explain the increased predisposition to cardiovascular disease in adult life. If these results are confirmed, the impact of strategies with beneficial effects on cardiac remodeling should be explored in children with FGR.

The goal of this study was to evaluate the maturity of current Deep Learning classification techniques for their application in a real maternal-fetal clinical environment. A large dataset of routinely acquired maternal-fetal screening ultrasound images (which will be made publicly available) was collected from two different hospitals by several operators and ultrasound machines. All images were manually labeled by an expert maternal fetal clinician. Images were divided into 6 classes: four of the most widely used fetal anatomical planes (Abdomen, Brain, Femur and Thorax), the mother’s cervix (widely used for prematurity screening) and a general category to include any other less common image plane. Fetal brain images were further categorized into the 3 most common fetal brain planes (Trans-thalamic, Trans-cerebellum, Trans-ventricular) to judge fine grain categorization performance. The final dataset is comprised of over 12,400 images from 1,792 patients, making it the largest ultrasound dataset to date. We then evaluated a wide variety of state-of-the-art deep Convolutional Neural Networks on this dataset and analyzed results in depth, comparing the computational models to research technicians, which are the ones currently performing the task daily. Results indicate for the first time that computational models have similar performance compared to humans when classifying common planes in human fetal examination. However, the dataset leaves the door open on future research to further improve results, especially on fine-grained plane categorization.

Objective: To assess the neurodevelopmental outcome at 2 years of age of children who had been small‐for‐gestational‐age (SGA) term babies with cerebral blood flow redistribution.Methods: Perinatal outcome was assessed in a cohort of 97 term singleton appropriate‐for‐gestational‐age and 125 term singleton SGA fetuses with normal umbilical artery Doppler, stratified according to the presence of cerebral blood flow redistribution. Neurodevelopmental outcome was assessed prospectively at 2 years of age by means of the 24‐month Age & Stage Questionnaire (ASQ).Results: Of the 125 SGA fetuses, 25 had redistribution of the cerebral blood flow, and 100 did not. There were no significant differences in perinatal outcome between these two SGA groups. At 2 years of age, children who had been SGA fetuses with middle cerebral artery (MCA) pulsatility index (PI) < 5^th centile had a higher incidence of suboptimal neurodevelopmental outcome compared with those with normal MCA‐PI (52% vs. 31%; P = 0.049) and a lower mean centile in communication (53.1 vs. 67.4; P = 0.006) and problem‐solving (39.7 vs. 47.4; P = 0.04) areas.Conclusion: SGA fetuses with cerebral blood flow redistribution have a higher risk of subtle neurodevelopmental deficits at 2 years of age. This challenges the concept that fetal cerebral redistribution is an entirely protective mechanism and suggests MCA‐PI as a risk stratifying factor for adverse neurodevelopmental outcome. Copyright © 2008 ISUOG. Published by John Wiley & Sons, Ltd.