RTSeg-net: A lightweight network for real-time segmentation of fetal head and pubic symphysis from intrapartum ultrasound images.

The segmentation of the fetal head (FH) and pubic symphysis (PS) from intrapartum ultrasound images plays a pivotal role in monitoring labor progression and informing crucial clinical decisions. Achieving real-time segmentation with high accuracy on systems with limited hardware capabilities presents significant challenges. To address these challenges, we propose the real-time segmentation network (RTSeg-Net), a groundbreaking lightweight deep learning model that incorporates innovative distribution shifting convolutional blocks, tokenized multilayer perceptron blocks, and efficient feature fusion blocks. Designed for optimal computational efficiency, RTSeg-Net minimizes resource demand while significantly enhancing segmentation performance. Our comprehensive evaluation on two distinct intrapartum ultrasound image datasets reveals that RTSeg-Net achieves segmentation accuracy on par with more complex state-of-the-art networks, utilizing merely 1.86 M parameters-just 6 % of their hyperparameters-and operating seven times faster, achieving a remarkable rate of 31.13 frames per second on a Jetson Nano, a device known for its limited computing capacity. These achievements underscore RTSeg-Net's potential to provide accurate, real-time segmentation on low-power devices, broadening the scope for its application across various stages of labor. By facilitating real-time, accurate ultrasound image analysis on portable, low-cost devices, RTSeg-Net promises to revolutionize intrapartum monitoring, making sophisticated diagnostic tools accessible to a wider range of healthcare settings.

PSFHS: Intrapartum ultrasound image dataset for AI-based segmentation of pubic symphysis and fetal head.

During the process of labor, the intrapartum transperineal ultrasound examination serves as a valuable tool, allowing direct observation of the relative positional relationship between the pubic symphysis and fetal head (PSFH). Accurate assessment of fetal head descent and the prediction of the most suitable mode of delivery heavily rely on this relationship. However, achieving an objective and quantitative interpretation of the ultrasound images necessitates precise PSFH segmentation (PSFHS), a task that is both time-consuming and demanding. Integrating the potential of artificial intelligence (AI) in the field of medical ultrasound image segmentation, the development and evaluation of AI-based models rely significantly on access to comprehensive and meticulously annotated datasets. Unfortunately, publicly accessible datasets tailored for PSFHS are notably scarce. Bridging this critical gap, we introduce a PSFHS dataset comprising 1358 images, meticulously annotated at the pixel level. The annotation process adhered to standardized protocols and involved collaboration among medical experts. Remarkably, this dataset stands as the most expansive and comprehensive resource for PSFHS to date.

Sexual dimorphism of the human fetal pelvis exists at the onset of primary ossification.

Human adolescent and adult skeletons exhibit sexual dimorphism in the pelvis. However, the degree of sexual dimorphism of the human pelvis during prenatal development remains unclear. Here, we performed high-resolution magnetic resonance imaging-assisted pelvimetry on 72 human fetuses (males [M]: females [F], 34:38; 21 sites) with crown-rump lengths (CRL) of 50-225 mm (the onset of primary ossification). We used multiple regression analysis to examine sexual dimorphism with CRL as a covariate. Females exhibit significantly smaller pelvic inlet anteroposterior diameters (least squares mean, [F] 8.4 mm vs. [M] 8.8 mm, P = 0.036), larger subpubic angle ([F] 68.1° vs. [M] 64.0°, P = 0.034), and larger distance between the ischial spines relative to the transverse diameters of the greater pelvis than males. Furthermore, the sacral measurements indicate significant sex-CRL interactions. Our study suggests that sexual dimorphism of the human fetal pelvis is already apparent at the onset of primary ossification.

Fetal brain MRI atlases and datasets: A review.

Fetal brain development is a complex process involving different stages of growth and organization which are crucial for the development of brain circuits and neural connections. Fetal atlases and labeled datasets are promising tools to investigate prenatal brain development. They support the identification of atypical brain patterns, providing insights into potential early signs of clinical conditions. In a nutshell, prenatal brain imaging and post-processing via modern tools are a cutting-edge field that will significantly contribute to the advancement of our understanding of fetal development. In this work, we first provide terminological clarification for specific terms (i.e., "brain template" and "brain atlas"), highlighting potentially misleading interpretations related to inconsistent use of terms in the literature. We discuss the major structures and neurodevelopmental milestones characterizing fetal brain ontogenesis. Our main contribution is the systematic review of 18 prenatal brain atlases and 3 datasets. We also tangentially focus on clinical, research, and ethical implications of prenatal neuroimaging.

Novel hybrid rigid-deformable fetal modeling for simulating the vaginal delivery within the second stage of labor.

BACKGROUND AND OBJECTIVE: The fetal representation as a 3D articulated body plays an essential role to describe a realistic vaginal delivery simulation. However, the current computational solutions have been oversimplified. The objective of the present work was to develop and evaluate a novel hybrid rigid-deformable modeling approach for the fetal body and then simulate its interaction with surrounding fetal soft tissues and with other maternal pelvis soft tissues during the second stage of labor. METHODS: CT scan data was used for 3D fetal skeleton reconstruction. Then, a novel hybrid rigid-deformable model of the fetal body was developed. This model was integrated into a maternal 3D pelvis model to simulate the vaginal delivery. Soft tissue deformation was simulated using our novel HyperMSM formulation. Magnetic resonance imaging during the second stage of labor was used to impose the trajectory of the fetus during the delivery. RESULTS: Our hybrid rigid-deformable fetal model showed a potential capacity for simulating the movements of the fetus along with the deformation of the fetal soft tissues during the vaginal delivery. The deformation energy density observed in the simulation for the fetal head fell within the strain range of 3 % to 5 %, which is in good agreement with the literature data. CONCLUSIONS: This study developed, for the first time, a hybrid rigid-deformation modeling of the fetal body and then performed a vaginal delivery simulation using MRI-driven kinematic data. This opens new avenues for describing more realistic behavior of the fetal body kinematics and deformation during the second stage of labor. As perspectives, the integration of the full skeleton body, especially the upper and lower limbs will be investigated. Then, the completed model will be integrated into our developed next-generation childbirth training simulator for vaginal delivery simulation and associated complication scenarios.

On the use of contrastive learning for standard-plane classification in fetal ultrasound imaging.

BACKGROUND: To investigate the effectiveness of contrastive learning, in particular SimClr, in reducing the need for large annotated ultrasound (US) image datasets for fetal standard plane identification. METHODS: We explore SimClr advantage in the cases of both low and high inter-class variability, considering at the same time how classification performance varies according to different amounts of labels used. This evaluation is performed by exploiting contrastive learning through different training strategies. We apply both quantitative and qualitative analyses, using standard metrics (F1-score, sensitivity, and precision), Class Activation Mapping (CAM), and t-Distributed Stochastic Neighbor Embedding (t-SNE). RESULTS: When dealing with high inter-class variability classification tasks, contrastive learning does not bring a significant advantage; whereas it results to be relevant for low inter-class variability classification, specifically when initialized with ImageNet weights. CONCLUSIONS: Contrastive learning approaches are typically used when a large number of unlabeled data is available, which is not representative of US datasets. We proved that SimClr either as pre-training with backbone initialized via ImageNet weights or used in an end-to-end dual-task may impact positively the performance over standard transfer learning approaches, under a scenario in which the dataset is small and characterized by low inter-class variability.

Autonomous fetal morphology scan: deep learning + clustering merger - the second pair of eyes behind the doctor.

The main cause of fetal death, of infant morbidity or mortality during childhood years is attributed to congenital anomalies. They can be detected through a fetal morphology scan. An experienced sonographer (with more than 2000 performed scans) has the detection rate of congenital anomalies around 52%. The rates go down in the case of a junior sonographer, that has the detection rate of 32.5%. One viable solution to improve these performances is to use Artificial Intelligence. The first step in a fetal morphology scan is represented by the differentiation process between the view planes of the fetus, followed by a segmentation of the internal organs in each view plane. This study presents an Artificial Intelligence empowered decision support system that can label anatomical organs using a merger between deep learning and clustering techniques, followed by an organ segmentation with YOLO8. Our framework was tested on a fetal morphology image dataset that regards the fetal abdomen. The experimental results show that the system can correctly label the view plane and the corresponding organs on real-time ultrasound movies.Trial registrationThe study is registered under the name "Pattern recognition and Anomaly Detection in fetal morphology using Deep Learning and Statistical Learning (PARADISE)", project number 101PCE/2022, project code PN-III-P4-PCE-2021-0057. Trial registration: ClinicalTrials.gov, unique identifying number NCT05738954, date of registration 02.11.2023.

Ultrasound and magnetic resonance imaging features of fetal urogenital anomalies: A pictorial essay.

This pictorial essay focuses on ultrasound (US) and magnetic resonance imaging (MRI) features of fetal urogenital anomalies. Fetal urogenital malformations account for 30%-50% of all anomalies discovered during pregnancy or at birth. They are usually detected by fetal ultrasound exams. However, when ultrasound data on their characteristics is insufficient, MRI is the best option for detecting other associated anomalies. The prognosis highly depends on their type and whether they are associated with other fetal abnormalities.

Pulmonary vascular reactivity in growth restricted fetuses using computational modelling and machine learning analysis of fetal Doppler waveforms.

The aim of this study was to investigate the pulmonary vasculature in baseline conditions and after maternal hyperoxygenation in growth restricted fetuses (FGR). A prospective cohort study of singleton pregnancies including 97 FGR and 111 normally grown fetuses was carried out. Ultrasound Doppler of the pulmonary vessels was obtained at 24-37 weeks of gestation and data were acquired before and after oxygen administration. After, Machine Learning (ML) and a computational model were used on the Doppler waveforms to classify individuals and estimate pulmonary vascular resistance (PVR). Our results showed lower mean velocity time integral (VTI) in the main pulmonary and intrapulmonary arteries in baseline conditions in FGR individuals. Delta changes of the main pulmonary artery VTI and intrapulmonary artery pulsatility index before and after hyperoxygenation were significantly greater in FGR when compared with controls. Also, ML identified two clusters: A (including 66% controls and 34% FGR) with similar Doppler traces over time and B (including 33% controls and 67% FGR) with changes after hyperoxygenation. The computational model estimated the ratio of PVR before and after maternal hyperoxygenation which was closer to 1 in cluster A (cluster A 0.98 ± 0.33 vs cluster B 0.78 ± 0.28, p = 0.0156). Doppler ultrasound allows the detection of significant changes in pulmonary vasculature in most FGR at baseline, and distinct responses to hyperoxygenation. Future studies are warranted to assess its potential applicability in the clinical management of FGR.

Assessment of fetal radiation exposure in pregnant women undergoing computed tomography and rotational angiography examinations for pelvic trauma.

This study aimed to assess fetal radiation exposure in pregnant women undergoing computed tomography (CT) and rotational angiography (RA) examinations for the diagnosis of pelvic trauma. In addition, this study aimed to compare the dose distributions between the two examinations. Surface and average fetal doses were estimated during CT and RA examinations using a pregnant phantom model and real-time dosemeters. The pregnant model phantom was constructed using an anthropomorphic phantom, and a custom-made abdominal phantom was used to simulate pregnancy. The total average fetal dose received by pregnant women from both CT scans (plain, arterial and equilibrium phases) and a single RA examination was ~60 mGy. Because unnecessary repetition of radiographic examinations, such as CT or conventional 2D angiography can increase the radiation risk, the irradiation range should be limited, if necessary, to reduce overall radiation exposure.

Craniofacial phenotyping with fetal MRI: a feasibility study of 3D visualisation, segmentation, surface-rendered and physical models.

This study explores the potential of 3D Slice-to-Volume Registration (SVR) motion-corrected fetal MRI for craniofacial assessment, traditionally used only for fetal brain analysis. In addition, we present the first description of an automated pipeline based on 3D Attention UNet trained for 3D fetal MRI craniofacial segmentation, followed by surface refinement. Results of 3D printing of selected models are also presented.Qualitative analysis of multiplanar volumes, based on the SVR output and surface segmentations outputs, were assessed with computer and printed models, using standardised protocols that we developed for evaluating image quality and visibility of diagnostic craniofacial features. A test set of 25, postnatally confirmed, Trisomy 21 fetal cases (24-36 weeks gestational age), revealed that 3D reconstructed T2 SVR images provided 66-100% visibility of relevant craniofacial and head structures in the SVR output, and 20-100% and 60-90% anatomical visibility was seen for the baseline and refined 3D computer surface model outputs respectively. Furthermore, 12 of 25 cases, 48%, of refined surface models demonstrated good or excellent overall quality with a further 9 cases, 36%, demonstrating moderate quality to include facial, scalp and external ears. Additional 3D printing of 12 physical real-size models (20-36 weeks gestational age) revealed good/excellent overall quality in all cases and distinguishable features between healthy control cases and cases with confirmed anomalies, with only minor manual adjustments required before 3D printing.Despite varying image quality and data heterogeneity, 3D T2w SVR reconstructions and models provided sufficient resolution for the subjective characterisation of subtle craniofacial features. We also contributed a publicly accessible online 3D T2w MRI atlas of the fetal head, validated for accurate representation of normal fetal anatomy.Future research will focus on quantitative analysis, optimizing the pipeline, and exploring diagnostic, counselling, and educational applications in fetal craniofacial assessment.

Application of artificial neural networks to evaluate femur development in the human fetus.

The present article concentrates on an innovative analysis that was performed to assess the development of the femur in human fetuses using artificial intelligence. As a prerequisite, linear dimensions, cross-sectional surface areas and volumes of the femoral shaft primary ossification center in 47 human fetuses aged 17-30 weeks, originating from spontaneous miscarriages and preterm deliveries, were evaluated with the use of advanced imaging techniques such as computed tomography and digital image analysis. In order to ensure the data representativeness and to avoid introducing any hidden structures that may exist in the data, the entire dataset was randomized and separated into three subsets: training (50% of cases), testing (25% of cases), and validation (25% of cases). Based on the collected numerical data, an artificial neural network was devised, trained, and subject to testing in order to synchronously estimate five parameters of the femoral shaft primary ossification center, thus leveraging fundamental information such as gestational age and femur length. The findings reveal the formulated multi-layer perceptron model denoted as MLP 2-3-2-5 to exhibit robust predictive efficacy, as evidenced by the linear correlation coefficient between actual values and network outputs: R = 0.955 for the training dataset, R = 0.942 for validation, and R = 0.953 for the testing dataset. The authors have cogently demonstrated that the use of an artificial neural network to assess the growing femur in the human fetus may be a valuable tool in prenatal tests, enabling medical doctors to quickly and precisely assess the development of the fetal femur and detect potential anatomical abnormalities.

Chromosomal Microarray Analysis in Fetuses With Ultrasonographic Soft Markers: A Meta-Analysis of the Current Evidence.

BACKGROUND: Ultrasonographic soft markers are normal variants, rather than fetal abnormalities, and guidelines recommend a detailed survey of fetal anatomy to determine the necessity of antenatal karyotyping. Anecdotal reports have described cases with ultrasonographic soft markers in which chromosomal microarray analysis (CMA) revealed pathogenic copy number variants (CNVs) despite normal results on conventional karyotyping, but CMA for ultrasonographic soft markers remains a matter of debate. In this systematic review, we evaluated the clinical significance of CMA for pregnancies with isolated ultrasonographic soft markers and a normal fetal karyotype. METHODS: An electronic search was conducted by an experienced librarian through the MEDLINE, Embase, and Cochrane CENTRAL databases. We reviewed 3,338 articles (3,325 identified by database searching and 13 by a hand search) about isolated ultrasonographic soft markers, and seven ultrasonographic markers (choroid plexus cysts, echogenic bowel, echogenic intracardiac focus, hypoplastic nasal bone, short femur [SF], single umbilical artery, and urinary tract dilatation) were included for this study. RESULTS: Seven eligible articles were included in the final review. Pathogenic or likely pathogenic CNVs were found in fetuses with isolated ultrasonographic soft markers and a normal karyotype. The overall prevalence of pathogenic or likely pathogenic CNVs was 2.0% (41 of 2,048). The diagnostic yield of CMA was highest in fetuses with isolated SF (9 of 225, 3.9%). CONCLUSION: CMA could aid in risk assessment and pregnancy counseling in pregnancies where the fetus has isolated ultrasonographic soft markers along with a normal karyotype.

Automatic measurement of fetal anterior neck lower jaw angle in nuchal translucency scans.

This study aims at suggesting an end-to-end algorithm based on a U-net-optimized generative adversarial network to predict anterior neck lower jaw angles (ANLJA), which are employed to define fetal head posture (FHP) during nuchal translucency (NT) measurement. We prospectively collected 720 FHP images (half hyperextension and half normal posture) and regarded manual measurement as the gold standard. Seventy percent of the FHP images (half hyperextension and half normal posture) were used to fit models, and the rest to evaluate them in the hyperextension group, normal posture group (NPG), and total group. The root mean square error, explained variation, and mean absolute percentage error (MAPE) were utilized for the validity assessment; the two-sample t test, Mann-Whitney U test, Wilcoxon signed-rank test, Bland-Altman plot, and intraclass correlation coefficient (ICC) for the reliability evaluation. Our suggested algorithm outperformed all the competitors in all groups and indices regarding validity, except for the MAPE, where the Inception-v3 surpassed ours in the NPG. The two-sample t test and Mann-Whitney U test indicated no significant difference between the suggested method and the gold standard in group-level comparison. The Wilcoxon signed-rank test revealed significant differences between our new approach and the gold standard in personal-level comparison. All points in Bland-Altman plots fell between the upper and lower limits of agreement. The inter-ICCs of ultrasonographers, our proposed algorithm, and its opponents were graded good reliability, good or moderate reliability, and moderate or poor reliability, respectively. Our proposed approach surpasses the competition and is as reliable as manual measurement.

Automated body organ segmentation, volumetry and population-averaged atlas for 3D motion-corrected T2-weighted fetal body MRI.

Structural fetal body MRI provides true 3D information required for volumetry of fetal organs. However, current clinical and research practice primarily relies on manual slice-wise segmentation of raw T2-weighted stacks, which is time consuming, subject to inter- and intra-observer bias and affected by motion-corruption. Furthermore, there are no existing standard guidelines defining a universal approach to parcellation of fetal organs. This work produces the first parcellation protocol of the fetal body organs for motion-corrected 3D fetal body MRI. It includes 10 organ ROIs relevant to fetal quantitative volumetry studies. We also introduce the first population-averaged T2w MRI atlas of the fetal body. The protocol was used as a basis for training of a neural network for automated organ segmentation. It showed robust performance for different gestational ages. This solution minimises the need for manual editing and significantly reduces time. The general feasibility of the proposed pipeline was also assessed by analysis of organ growth charts created from automated parcellations of 91 normal control 3T MRI datasets that showed expected increase in volumetry during 22-38 weeks gestational age range.

A joint brain extraction and image quality assessment framework for fetal brain MRI slices.

Brain extraction and image quality assessment are two fundamental steps in fetal brain magnetic resonance imaging (MRI) 3D reconstruction and quantification. However, the randomness of fetal position and orientation, the variability of fetal brain morphology, maternal organs around the fetus, and the scarcity of data samples, all add excessive noise and impose a great challenge to automated brain extraction and quality assessment of fetal MRI slices. Conventionally, brain extraction and quality assessment are typically performed independently. However, both of them focus on the brain image representation, so they can be jointly optimized to ensure the network learns more effective features and avoid overfitting. To this end, we propose a novel two-stage dual-task deep learning framework with a brain localization stage and a dual-task stage for joint brain extraction and quality assessment of fetal MRI slices. Specifically, the dual-task module compactly contains a feature extraction module, a quality assessment head and a segmentation head with feature fusion for simultaneous brain extraction and quality assessment. Besides, a transformer architecture is introduced into the feature extraction module and the segmentation head. We utilize a multi-step training strategy to guarantee a stable and successful training of all modules. Finally, we validate our method by a 5-fold cross-validation and ablation study on a dataset with fetal brain MRI slices in different qualities, and perform a cross-dataset validation in addition. Experiments show that the proposed framework achieves very promising performance.

Automating the Human Action of First-Trimester Biometry Measurement from Real-World Freehand Ultrasound.

OBJECTIVE: Automated medical image analysis solutions should closely mimic complete human actions to be useful in clinical practice. However, more often an automated image analysis solution represents only part of a human task, which restricts its practical utility. In the case of ultrasound-based fetal biometry, an automated solution should ideally recognize key fetal structures in freehand video guidance, select a standard plane from a video stream and perform biometry. A complete automated solution should automate all three subactions. METHODS: In this article, we consider how to automate the complete human action of first-trimester biometry measurement from real-world freehand ultrasound. In the proposed hybrid convolutional neural network (CNN) architecture design, a classification regression-based guidance model detects and tracks fetal anatomical structures (using visual cues) in the ultrasound video. Several high-quality standard planes that contain the mid-sagittal view of the fetus are sampled at multiple time stamps (using a custom-designed confident-frame detector) based on the estimated probability values associated with predicted anatomical structures that define the biometry plane. Automated semantic segmentation is performed on the selected frames to extract fetal anatomical landmarks. A crown-rump length (CRL) estimate is calculated as the mean CRL from these multiple frames. RESULTS: Our fully automated method has a high correlation with clinical expert CRL measurement (Pearson's p = 0.92, R-squared [R2] = 0.84) and a low mean absolute error of 0.834 (weeks) for fetal age estimation on a test data set of 42 videos. CONCLUSION: A novel algorithm for standard plane detection employs a quality detection mechanism defined by clinical standards, ensuring precise biometric measurements.

Early prenatal diagnosis of causative homozygous variants in ASCC1 in a fetus with cystic hygroma and additional homozygous variants of unknown significance associated with a neurological phenotype not visible in early gestation: Dual diagnosis or not?

A consanguineous couple was referred at 10 weeks of gestation (WG) for prenatal genetic investigations due to isolated cystic hygroma. Prenatal trio exome sequencing identified causative homozygous truncating variants in ASCC1 previously implicated in spinal muscular atrophy with congenital bone fractures. Prenatal manifestations in ASCC1 can usually include hydramnios, fetal hypo-/akinesia, arthrogryposis, contractures and limb deformities, hydrops fetalis and cystic hygroma. An additional truncating variant was identified in CSPP1 associated with Joubert syndrome. Presentations in CSPP1 include cerebellar and brainstem malformations with vermis hypoplasia and molar tooth sign, difficult to visualize in early gestation. A second pregnancy was marked by the recurrence of isolated increased nuchal translucency at 10 + 2 WG. Sanger prenatal diagnosis targeted on ASCC1 and CSPP1 variants showed the presence of the homozygous familial ASCC1 variant. In this case, prenatal exome sequencing analysis is subject to a partial ASCC1 phenotype and an undetectable CSPP1 phenotype at 10 weeks of gestation. As CSPP1 contribution is unclear or speculative to a potentially later in pregnancy or postnatal phenotype, it is mentioned as a variant of uncertain significance. The detection of pathogenic or likely pathogenic variants involved in severe disorders but without phenotype-genotype correlation because the pregnancy is in the early stages or due to prenatally undetectable phenotypes, will encourage the clinical community to define future practices in molecular prenatal reporting.

MR insights into fetal brain development: what is normal and what is not.

Fetal brain development is a complex, rapid, and multi-dimensional process that can be documented with MRI. In the second and third trimesters, there are predictable developmental changes that must be recognized and differentiated from disease. This review delves into the key biological processes that drive fetal brain development, highlights normal developmental anatomy, and provides a framework to identify pathology. We will summarize the development of the cerebral hemispheres, sulci and gyri, extra-axial and ventricular cerebrospinal fluid, and corpus callosum and illustrate the most common abnormal findings in the clinical setting.

Ultrasonographic assessment of abnormal fetal growth related to uteroplacental-fetal biometrics and Doppler (U-AID) indices: Protocol for multicenter retrospective cohort study trial.

Fetal growth restriction (FGR) is one of the leading causes of perinatal morbidity and mortality. Many studies have reported an association between FGR and fetal Doppler indices focusing on umbilical artery (UA), middle cerebral artery (MCA), and ductus venosus (DV). The uteroplacental-fetal circulation which affects the fetal growth consists of not only UA, MCA, and DV, but also umbilical vein (UV), placenta and uterus itself. Nevertheless, there is a paucity of large-scale cohort studies that have assessed the association between UV, uterine wall, and placental thickness with perinatal outcomes in FGR, in conjunction with all components of the uteroplacental-fetal circulation. Therefore, this multicenter study will evaluate the association among UV absolute flow, placental thickness, and uterine wall thickness and adverse perinatal outcome in FGR fetuses. This multicenter retrospective cohort study will include singleton pregnant women who undergo at least one routine fetal ultrasound scan during routine antepartum care. Pregnant women with fetuses having structural or chromosomal abnormalities will be excluded. The U-AID indices (UtA, UA, MCA, and UV flow, placental and uterine wall thickness, and estimated fetal body weight) will be measured during each trimester of pregnancy. The study population will be divided into two groups: (1) FGR group (pregnant women with FGR fetuses) and (2) control group (those with normal growth fetus). We will assess the association between U-AID indices and adverse perinatal outcomes in the FGR group and the difference in U-AID indices between the two groups.