First-trimester 3D fetal neurosonography: five standardised views.

BACKGROUND: There are several international guidelines for foetal anomalies scanning at 11-14 weeks' gestation. The aim of this study is to present our first-trimester specialist neurosonography protocol with examples of pathology in order to develop a systematic approach to evaluating the first-trimester foetal brain. METHODS: Women undergoing a first-trimester foetal medicine ultrasound scan between 2010 and 2020 for multiple indications underwent neurosonography according to a set protocol. 3D transvaginal brain examination was performed in all cases (2000 pregnancies scanned). We retrospectively reviewed all imaging to develop this protocol. RESULTS: We propose that the following five axial-plane parallel views should be obtained when performing neurosonography in the first trimester, moving from cranial to caudal: 1. Lateral ventricles; 2. Third ventricle; 3. Thalamus and mesencephalon; 4. Cerebellum; 5. Fourth ventricle. Examples of these images and abnormalities that can be seen in each plane are given. CONCLUSIONS: We have presented a specialist protocol for systematically assessing the foetal brain in the first trimester and given examples of pathology which may be seen in each plane. Further work is needed to prospectively assess detection rates of major abnormalities using this protocol and assess the reproducibility and learning curve of this technique.

This article suggests a way in which specialists scanning babies at 11­14 weeks of pregnancy can check the brain in a structured way. This involves looking at the brain at five levels or planes to view the developing structures. The suggested scan protocol is similar to images produced of the brain and heart at the second trimester (20 week) scan. We hope that specialists will find it useful to check the brain in this way if there are concerns raised at the dating (12 week) scan, and that this will lead to earlier detection of brain abnormalities or differences.

How automated techniques ease functional assessment of the fetal heart: Applicability of two-dimensional speckle-tracking echocardiography for comprehensive analysis of global and segmental cardiac deformation using fetalHQ®.

BACKGROUND: Prenatal echocardiographic assessment of fetal cardiac function has become increasingly important. Fetal two-dimensional speckle-tracking echocardiography (2D-STE) allows the determination of global and segmental functional cardiac parameters. Prenatal diagnostics is relying increasingly on artificial intelligence, whose algorithms transform the way clinicians use ultrasound in their daily workflow. The purpose of this study was to demonstrate the feasibility of whether less experienced operators can handle and might benefit from an automated tool of 2D-STE in the clinical routine. METHODS: A total of 136 unselected, normal, singleton, second- and third-trimester fetuses with normofrequent heart rates were examined by targeted ultrasound. 2D-STE was performed separately by beginner and expert semiautomatically using a GE Voluson E10 (FetalHQ®, GE Healthcare, Chicago, IL). Several fetal cardiac parameters were calculated (end-diastolic diameter [ED], sphericity index [SI], global longitudinal strain [EndoGLS], fractional shortening [FS]) and assigned to gestational age (GA). Bland-Altman plots were used to test agreement between both operators. RESULTS: The mean maternal age was 33 years, and the mean maternal body mass index prior to pregnancy was 24.78 kg/m2. The GA ranged from 16.4 to 32.0 weeks (average 22.9 weeks). Averaged endoGLS value of the beginner was -18.57% ± 6.59 percentage points (pp) for the right and -19.58% ± 5.63 pp for the left ventricle, that of the expert -14.33% ± 4.88 pp and -16.37% ± 5.42 pp. With increasing GA, right ventricular endoGLS decreased slightly while the left ventricular was almost constant. The statistical analysis for endoGLS showed a Bland-Altman-Bias of -4.24 pp ± 8.06 pp for the right and -3.21 pp ± 7.11 pp for the left ventricle. The Bland-Altman-Bias of the ED in both ventricles in all analyzed segments ranged from -.49 mm ± 1.54 mm to -.10 mm ± 1.28 mm, that for FS from -.33 pp ± 11.82 pp to 3.91 pp ± 15.56 pp and that for SI from -.38 ± .68 to -.15 ± .45. CONCLUSIONS: Between both operators, our data indicated that 2D-STE analysis showed excellent agreement for cardiac morphometry parameters (ED and SI), and good agreement for cardiac function parameters (EndoGLS and FS). Due to its complexity, the application of fetal 2D-STE remains the domain of scientific-academic perinatal ultrasound and should be placed preferably in the hands of skilled operators. At present, from our perspective, an implementation into clinical practice "on-the-fly" cannot be recommended.

Current Understanding of Indications, Technical Aspects and Outcomes of Fetal Cardiac Interventions.

With the improvement in the detection of congenital heart disease in fetal life, fetal cardiac interventions are pushing the envelope in hopes of either altering the natural history of disease or improving survival in certain high-risk lesions. These interventions include fetal aortic valvuloplasty for evolving hypoplastic left heart syndrome, fetal atrial septoplasty with or without atrial septal stenting for hypoplastic left heart syndrome and variants with intact or severely restrictive atrial septum, and fetal pulmonary valvuloplasty for severe pulmonary stenosis or pulmonary atresia with intact ventricular septum. This review discusses their indications, technical aspects, and outcomes based on available literature.

[THE RISK FOR CLINICALLY SIGNIFICANT COPY NUMBER VARIANTS IN PREGNANCIES WITH TWO SOFT MARKERS].

INTRODUCTION: Soft sonographic markers, such as an intracardiac echogenic focus, are demonstrated in one out of 150 live births and are associated with a slightly increased risk of trisomy 21 and 18. In the case of an isolated soft marker, the recommendation to perform invasive tests such as amniocentesis or placental cyst testing depends to a large extent on the results of biochemical first and second trimester maternal serum screening. In the case of two soft markers, the women are referred to genetic counseling, and invasive testing is funded by the Ministry of Health. OBJECTIVES: To estimate the risk for clinically significant copy number variants (CNVs) in pregnancies with two soft markers. METHODS: This retrospective cohort study included all prenatal microarray tests performed during 2013-2021, due to demonstration of two soft markers (namely: echogenic intracardiac foci, choroid plexus cyst, single umbilical artery and mild pyelectasis). The rates of clinically significant (pathogenic and likely pathogenic) microarray findings were compared to a previously published cohort of 7235 pregnancies with normal ultrasound, in which 87 (1.2%) abnormal CNVs were noted. RESULTS: Of the 150 pregnancies with two soft markers, two (1.3%) clinically significant CNVs were found. The rate of abnormal microarray findings did not differ from baseline risk in pregnancies with normal ultrasound - relative risk of 1.11 (95% confidence interval 0.28-4.40). CONCLUSIONS: The risk for abnormal microarray findings in pregnancies with two soft markers was not significantly increased in comparison to control group of pregnancies with normal sonography. DISCUSSION: These results undermine the current national policy of genetic counseling and Ministry of Health-funded invasive testing in pregnancies with a combination of two soft markers. These findings are important for additional countries with similar management, and may facilitate the genetic counseling and informed decision-making in such cases.

Investigation on ultrasound images for detection of fetal congenital heart defects.

Congenital heart defects (CHD) are one of the serious problems that arise during pregnancy. Early CHD detection reduces death rates and morbidity but is hampered by the relatively low detection rates (i.e., 60%) of current screening technology. The detection rate could be increased by supplementing ultrasound imaging with fetal ultrasound image evaluation (FUSI) using deep learning techniques. As a result, the non-invasive foetal ultrasound image has clear potential in the diagnosis of CHD and should be considered in addition to foetal echocardiography. This review paper highlights cutting-edge technologies for detecting CHD using ultrasound images, which involve pre-processing, localization, segmentation, and classification. Existing technique of preprocessing includes spatial domain filter, non-linear mean filter, transform domain filter, and denoising methods based on Convolutional Neural Network (CNN); segmentation includes thresholding-based techniques, region growing-based techniques, edge detection techniques, Artificial Neural Network (ANN) based segmentation methods, non-deep learning approaches and deep learning approaches. The paper also suggests future research directions for improving current methodologies.

Statistical Characterisation of Fetal Anatomy in Simple Obstetric Ultrasound Video Sweeps.

OBJECTIVE: We present a statistical characterisation of fetal anatomies in obstetric ultrasound video sweeps where the transducer follows a fixed trajectory on the maternal abdomen. METHODS: Large-scale, frame-level manual annotations of fetal anatomies (head, spine, abdomen, pelvis, femur) were used to compute common frame-level anatomy detection patterns expected for breech, cephalic, and transverse fetal presentations, with respect to video sweep paths. The patterns, termed statistical heatmaps, quantify the expected anatomies seen in a simple obstetric ultrasound video sweep protocol. In this study, a total of 760 unique manual annotations from 365 unique pregnancies were used. RESULTS: We provide a qualitative interpretation of the heatmaps assessing the transducer sweep paths with respect to different fetal presentations and suggest ways in which the heatmaps can be applied in computational research (e.g., as a machine learning prior). CONCLUSION: The heatmap parameters are freely available to other researchers (https://github.com/agleed/calopus_statistical_heatmaps).

An innovative supervised longitudinal learning procedure of recurrent neural networks with temporal data augmentation: Insights from predicting fetal macrosomia and large-for-gestational age.

BACKGROUND: Longitudinal data in health informatics studies often present challenges due to sparse observations from each subject, limiting the application of contemporary deep learning for prediction. This issue is particularly relevant in predicting birthweight, a crucial factor in identifying conditions such as macrosomia and large-for-gestational age (LGA). Previous approaches have relied on empirical formulas for estimated fetal weights (EFWs) from ultrasound measurements and mixed-effects models for interim predictions. METHOD: The proposed novel supervised longitudinal learning procedure features a three-step approach. First, EFWs are generated using empirical formulas from ultrasound measurements. Second, nonlinear mixed-effects models are applied to create augmented sequences of EFWs, spanning daily gestational timepoints. This augmentation transforms sparse longitudinal data into a dense parallel sequence suitable for training recurrent neural networks (RNNs). A tailored RNN architecture is then devised to incorporate the augmented sequential EFWs along with non-sequential maternal characteristics. RESULTS: The RNNs are trained on augmented data to predict birthweights, which are further classified for macrosomia and LGA. Application of this supervised longitudinal learning procedure to the Successive Small-for-Gestational-Age Births study yields improved performance in classification metrics. Specifically, sensitivity, area under the receiver operation characteristic curve, and Youden's Index demonstrate enhanced results, indicating the effectiveness of the proposed approach in overcoming sparsity challenges in longitudinal health informatics data. CONCLUSIONS: The integration of mixed-effects models for temporal data augmentation and RNNs on augmented sequences shows effective in accurately predicting birthweights, particularly in the context of identifying excessive fetal growth conditions.

Prenatal diagnosis of fetuses with absent/hypoplastic nasal bone in second-trimester using chromosomal microarray analysis.

BACKGROUND: Pathogenic copy number variants (pCNVs) are associated with fetal ultrasound anomalies, which can be efficiently identified through chromosomal microarray analysis (CMA). The primary objective of the present study was to enhance understanding of the genotype-phenotype correlation in fetuses exhibiting absent or hypoplastic nasal bones using CMA. METHODS: Enrolled in the present study were 94 cases of fetuses with absent/hypoplastic nasal bone, which were divided into an isolated absent/hypoplastic nasal bone group (n = 49) and a non-isolated group (n = 45). All pregnant women enrolled in the study underwent karyotype analysis and CMA to assess chromosomal abnormalities in the fetuses. RESULTS: Karyotype analysis and CMA detection were successfully performed in all cases. The results of karyotype and CMA indicate the presence of 11 cases of chromosome aneuploidy, with trisomy 21 being the most prevalent among them. A small supernumerary marker chromosome (sSMC) detected by karyotype analysis was further interpreted as a pCNV by CMA. Additionally, CMA detection elicited three cases of pCNVs, despite normal findings in their karyotype analysis results. Among them, one case of Roche translocation was identified to be a UPD in chromosome 15 with a low proportion of trisomy 15. Further, a significant difference in the detection rate of pCNVs was observed between non-isolated and isolated absent/hypoplastic nasal bone (24.44% vs. 8.16%, p < .05). CONCLUSION: The present study enhances the utility of CMA in diagnosing the etiology of absent or hypoplastic nasal bone in fetuses. Further, isolated cases of absent or hypoplastic nasal bone strongly suggest the presence of chromosomal abnormalities, necessitating genetic evaluation through CMA.

Probe motion during mid-trimester fetal anomaly scan in the clinical setting: A prospective observational study.

INTRODUCTION: This study aims to investigate probe motion during full mid-trimester anomaly scans. METHODS: We undertook a prospective, observational study of obstetric sonographers at a UK University Teaching Hospital. We collected prospectively full-length video recordings of routine second-trimester anomaly scans synchronized with probe trajectory tracking data during the scan. Videos were reviewed and trajectories analyzed using duration, path metrics (path length, velocity, acceleration, jerk, and volume) and angular metrics (spectral arc, angular area, angular velocity, angular acceleration, and angular jerk). These trajectories were then compared according to the participant level of expertise, fetal presentation, and patient BMI. RESULTS: A total of 17 anomaly scans were recorded. The average velocity of the probe was 12.9 ± 3.4 mm/s for the consultants versus 24.6 ± 5.7 mm/s for the fellows (p = 0.02), the average acceleration 170.4 ± 26.3 mm/s2 versus 328.9 ± 62.7 mm/s2 (p = 0.02), and the average jerk 7491.7 ± 1056.1 mm/s3 versus 14944.1 ± 3146.3 mm/s3 (p = 0.02), the working volume 9.106 ± 4.106 mm3 versus 29.106 ± 11.106 mm3 (p = 0.03), respectively. The angular metrics were not significantly different according to the participant level of expertise, the fetal presentation, or to patients BMI. CONCLUSION: Some differences in the probe path metrics (velocity, acceleration, jerk and working volume) were noticed according to operator's level.

Enhancing parental understanding of congenital heart disease through personalized prenatal counseling with 3D printed hearts.

OBJECTIVES: In addition to a correct prenatal diagnosis of congenital heart disease (CHD), comprehensive parental counseling is crucial to ensure that parents are well-informed about the condition of the fetus. This study aims to investigate whether there is a significant difference in the information acquired by parents through traditional counseling, utilizing 2-dimensional (2D) illustrations and images, compared to an advanced approach utilizing personalized three-dimensional (3D) printed models of the fetal heart developed from 3D ultrasound imaging. METHODS: This study, designed as a pilot randomized control trial, enrolled pregnant women with gestational ages greater than 18 weeks, whose fetuses were diagnosed with CHD and referred to our center between November, 2020 and June, 2021. Two groups of patients were included in the study. The first group received standard medical counseling with 2D images and illustrations, while the second group underwent advanced counseling with 3D-printed patient-specific heart models. Both groups were then required to complete the same survey in which the knowledge of the CHD was investigated. The 3D models were created from 3D ultrasound imaging and printed using resin materials in both 1:1 and 5:1 scale. RESULTS: A comparison of the scores obtained from the two groups revealed that 3D visualization of the fetus's heart has the potential to increase parental knowledge about CHD and the required surgical procedures. Furthermore, all couples expressed interest in receiving a 1:1 scale model of their baby's heart. CONCLUSION: Personalized prenatal counseling with 3D-ultrasound-based heart models positively impacts parents' understanding of CHD. The use of 3D models provides a more comprehensive and accessible representation of the condition, contributing to an increased knowledge gain, and potentially helping to support informed decisions regarding their child's care.

Deep learning-based differentiation of ventricular septal defect from tetralogy of Fallot in fetal echocardiography images.

BACKGROUND: Congenital heart disease (CHD) seriously affects children's health and quality of life, and early detection of CHD can reduce its impact on children's health. Tetralogy of Fallot (TOF) and ventricular septal defect (VSD) are two types of CHD that have similarities in echocardiography. However, TOF has worse diagnosis and higher morality than VSD. Accurate differentiation between VSD and TOF is highly important for administrative property treatment and improving affected factors' diagnoses. OBJECTIVE: TOF and VSD were differentiated using convolutional neural network (CNN) models that classified fetal echocardiography images. METHODS: We collected 105 fetal echocardiography images of TOF and 96 images of VSD. Four CNN models, namely, VGG19, ResNet50, NTS-Net, and the weakly supervised data augmentation network (WSDAN), were used to differentiate the two congenital heart diseases. The performance of these four models was compared based on sensitivity, accuracy, specificity, and AUC. RESULTS: VGG19 and ResNet50 performed similarly, with AUCs of 0.799 and 0.802, respectively. A superior performance was observed with NTS-Net and WSDAN specific for fine-grained image categorization tasks, with AUCs of 0.823 and 0.873, respectively. WSDAN had the best performance among all models tested. CONCLUSIONS: WSDAN exhibited the best performance in differentiating between TOF and VSD and is worthy of further clinical popularization.

Clinical implementation of first trimester screening for congenital heart defects.

OBJECTIVE: To examine the feasibility and performance of implementing a standardized fetal cardiac scan at the time of a routine first-trimester ultrasound scan. METHOD: A retrospective, single-center study in an unselected population between March 2021 and July 2022. A standardized cardiac scan protocol consisting of a four-chamber and 3-vessel trachea view with color Doppler was implemented as part of the routine first-trimester scan. Sonographers were asked to categorize the fetal heart anatomy. Data were stratified into two groups based on the possibility of evaluating the fetal heart. The influence of maternal and fetal characteristics and the detection of major congenital heart disease were investigated. RESULTS: A total of 5083 fetuses were included. The fetal heart evaluation was completed in 84.9%. The proportion of successful scans increased throughout the study period from 76% in the first month to 92% in the last month. High maternal body mass index and early gestational age at scan significantly decreased the feasibility. The first-trimester detection of major congenital heart defects was 7/16, of which four cases were identified by the cardiac scan protocol with no false-positive cases. CONCLUSION: First-trimester evaluation of the fetal heart by a standardized scan protocol is feasible to implement in daily practice. It can contribute to the earlier detection of congenital heart defects at a very low false positive rate.

The value of fetal magnetic resonance imaging in diagnosis of congenital anomalies of the fetal body: a systematic review and meta-analysis.

OBJECTIVES: To undertake a systematic review to assess the accuracy of fetal MRI in diagnosis of non-CNS congenital anomalies of the fetal body in comparison with antenatal ultrasound when correlated to postnatal diagnosis. METHODS: Searches were conducted from electronic databases, key journals and reference lists for eligible papers. Inclusion criteria was original research studies comparing the diagnostic results of antenatal ultrasound, fetal MRI and final postnatal diagnosis via imaging, surgery or post-mortem testing. Studies of CNS anomalies were excluded. Studies were assessed for risk of bias by two reviewers working independently and data was then extracted by a single reviewer. RESULTS: 12 studies were included with a total of 361 eligible patients who underwent USS and MRI and had a postnatal diagnosis. USS alone had a diagnostic accuracy of 60.6% whereas MRI had an improved diagnostic accuracy of 86.4%. The overall odds ratio was 0.86 (CI 0.202-1.519 and p-value < 0.01). CONCLUSION: Fetal MRI makes a significant contribution to accurate diagnosis of congenital abnormalities of the fetal body; especially in genito-urinary anomalies. More research is needed to improve the evidence base for the role of fetal MRI in diagnosis of congenital anomalies in other body systems.

Ultrasonographic placental parameters at 11-13+6 weeks' gestation in the prediction of complications in pregnancy after assisted reproductive technology.

OBJECTIVE: To evaluate the performance of maternal factors, biophysical and biochemical markers at 11-13 + 6 weeks' gestation in the prediction of gestational diabetes mellitus with or without large for gestational age (GDM ± LGA) fetus and great obstetrical syndromes (GOS) among singleton pregnancy following in-vitro fertilisation (IVF)/embryo transfer (ET). MATERIALS AND METHODS: A prospective cohort study was conducted between December 2017 and January 2020 including patients who underwent IVF/ET. Maternal mean arterial pressure (MAP), ultrasound markers including placental volume, vascularisation index (VI), flow index (FI) and vascularisation flow index (VFI), mean uterine artery pulsatility index (mUtPI) and biochemical markers including placental growth factor (PlGF) and soluble fms-like tyrosine kinase-1 (sFlt-1) were measured at 11-13 + 6 weeks' gestation. Logistic regression analysis was performed to determine the significant predictors of complications. RESULTS: Among 123 included pregnancies, 38 (30.9%) had GDM ± LGA fetus and 28 (22.8%) had GOS. The median maternal height and body mass index were significantly higher in women with GDM ± LGA fetus. Multivariate logistic regression analysis demonstrated that in the prediction of GDM ± LGA fetus and GOS, there were significant independent contributions from FI MoM (area under curve (AUROC) of 0.610, 95% CI 0.492-0.727; p = 0.062) and MAP MoM (AUROC of 0.645, 95% CI 0.510-0.779; p = 0.026), respectively. CONCLUSION: FI and MAP are independent predictors for GDM ± LGA fetus and GOS, respectively. However, they have low predictive value. There is a need to identify more specific novel biomarkers in differentiating IVF/ET pregnancies that are at a higher risk of developing complications.

Absent or hypoplastic nasal bone: What to tell the prospective parents?

BACKGROUND: Absent or hypoplastic nasal bone (AHNB) on first or second-trimester ultrasonography (USG) is an important soft marker of Down syndrome. However, due to its varied incidence in euploid and aneuploid fetuses, there is always a dilemma of whether to go for invasive fetal testing for isolated AHNB. This study aims to assess outcomes specifically within the context of Indian ethnicity women. MATERIALS AND METHODS: This was a prospective observational study. All patients who reported with AHNB in the first- or second-trimester USG were included. Genetic counseling was done, and noninvasive and invasive testing was offered. Chromosomal anomalies were meticulously recorded, and pregnancy was monitored. RESULTS: The incidence of AHNB in our study was 1.16% (47/4051). Out of 47 women with AHNB, the isolated condition was seen in 32 (0.78%) cases, while AHNB with structural anomalies was seen in nine cases (0.22%). Thirty-nine women opted for invasive testing. Six out of 47 had aneuploidy (12.7%), while two euploid cases (4.25%) developed nonimmune hydrops. The prevalence of Down syndrome in fetuses with AHNB was 8.5% (4/47) and 0.42% (17/4004) in fetuses with nasal bone present. This difference was statistically significant (p = .001). CONCLUSION: The results indicate that isolated AHNB cases should be followed by a comprehensive anomaly scan rather than immediately recommending invasive testing. However, invasive testing is required when AHNB is associated with other soft markers or abnormalities. As chromosomal microarray is more sensitive than standard karyotype in detecting chromosomal aberrations, it should be chosen over karyotype.

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.

Automatic segmentation of 15 critical anatomical labels and measurements of cardiac axis and cardiothoracic ratio in fetal four chambers using nnU-NetV2.

BACKGROUND: Accurate segmentation of critical anatomical structures in fetal four-chamber view images is essential for the early detection of congenital heart defects. Current prenatal screening methods rely on manual measurements, which are time-consuming and prone to inter-observer variability. This study develops an AI-based model using the state-of-the-art nnU-NetV2 architecture for automatic segmentation and measurement of key anatomical structures in fetal four-chamber view images. METHODS: A dataset, consisting of 1,083 high-quality fetal four-chamber view images, was annotated with 15 critical anatomical labels and divided into training/validation (867 images) and test (216 images) sets. An AI-based model using the nnU-NetV2 architecture was trained on the annotated images and evaluated using the mean Dice coefficient (mDice) and mean intersection over union (mIoU) metrics. The model's performance in automatically computing the cardiac axis (CAx) and cardiothoracic ratio (CTR) was compared with measurements from sonographers with varying levels of experience. RESULTS: The AI-based model achieved a mDice coefficient of 87.11% and an mIoU of 77.68% for the segmentation of critical anatomical structures. The model's automated CAx and CTR measurements showed strong agreement with those of experienced sonographers, with respective intraclass correlation coefficients (ICCs) of 0.83 and 0.81. Bland-Altman analysis further confirmed the high agreement between the model and experienced sonographers. CONCLUSION: We developed an AI-based model using the nnU-NetV2 architecture for accurate segmentation and automated measurement of critical anatomical structures in fetal four-chamber view images. Our model demonstrated high segmentation accuracy and strong agreement with experienced sonographers in computing clinically relevant parameters. This approach has the potential to improve the efficiency and reliability of prenatal cardiac screening, ultimately contributing to the early detection of congenital heart defects.

Accuracy and repeatability of fetal facial measurements in 3D ultrasound: A longitudinal study.

OBJECTIVE: Fetal face measurements in prenatal ultrasound can aid in identifying craniofacial abnormalities in the developing fetus. However, the accuracy and reliability of ultrasound measurements can be affected by factors such as fetal position, image quality, and the sonographer's expertise. This study assesses the accuracy and reliability of fetal facial measurements in prenatal ultrasound. Additionally, the temporal evolution of measurements is studied, comparing prenatal and postnatal measurements. METHODS: Three different experts located up to 23 facial landmarks in 49 prenatal 3D ultrasound scans from normal Caucasian fetuses at weeks 20, 26, and 35 of gestation. Intra- and inter-observer variability was obtained. Postnatal facial measurements were also obtained at 15 days and 1 month postpartum. RESULTS: Most facial landmarks exhibited low errors, with overall intra- and inter-observer errors of 1.01 mm and 1.60 mm, respectively. Landmarks on the nose were found to be the most reliable, while the most challenging ones were those located on the ears and eyes. Overall, scans obtained at 26 weeks of gestation presented the best trade-off between observer variability and landmark visibility. The temporal evolution of the measurements revealed that the lower face area had the highest rate of growth throughout the latest stages of pregnancy. CONCLUSIONS: Craniofacial landmarks can be evaluated using 3D fetal ultrasound, especially those located on the nose, mouth, and chin. Despite its limitations, this study provides valuable insights into prenatal and postnatal biometric changes over time, which could aid in developing predictive models for postnatal measurements based on prenatal data.

Prenatal diagnosis of interrupted aortic arch using high-definition flow render mode and spatiotemporal image correlation.

OBJECTIVES: To evaluate the clinical utility of two dimensional (2D) ultrasound combined with spatiotemporal image correlation (STIC) in diagnosing interrupted aortic arch (IAA) in fetal life. METHODS: A total of 53 cases of fetal IAA were diagnosed using 2D ultrasound combined with STIC, and 53 normal fetuses of the same gestational week were selected. These cases were retrospectively analyzed to assess the utility of employing 2D ultrasound combined with STIC in the diagnosis of IAA. RESULTS: 2D ultrasound combined with STIC detected 22 cases of type A IAA, 24 cases of type B IAA, and seven cases of type C IAA. Furthermore, combining 2D ultrasound with STIC enabled dynamic visualization of the IAA, aiding in prenatal diagnosis. The diagnostic coincidence rate of IAA was found to be higher in the HD-flow combined with STIC than that in the 2D combined with HD-flow. CONCLUSION: HD-flow combined with STIC can assist in diagnosing fetal IAA, and this technique has important clinical value.