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.

Importance of Prenatal Diagnosis of Ileal Atresia in Gestational Diabetes Cases.

BACKGROUND Maldevelopment of the fetal bowel can result in the rare condition of intestinal atresia, which results in congenital bowel obstruction. This report describes a case of prenatal diagnosis of fetal ileal atresia at 22 weeks' gestation. CASE REPORT Here, we present a 24-year old woman who was 22 weeks into her first pregnancy when she underwent routine fetal ultrasound. She was diagnosed with gestational diabetes mellitus. Her body mass index was normal and she had normal weight gain. The ultrasonographic examination performed revealed a hyperechoic bowel and a small dilatation of the bowel. The couple was counselled for possible intestinal atresia and its postnatal implications. At 33 weeks of gestation, polyhydramnios appeared, and the intestinal distension was much more pronounced, with hyperechoic debris in the intestinal lumen (succus-entericus). After birth, surgery was performed and we concluded the patient had type II atresia, which was surgically treated. CONCLUSIONS This report has highlighted the importance of antenatal ultrasound in detecting fetal abnormalities, and has shown that rare conditions such as intestinal atresia can be accurately diagnosed and successfully managed. Surgical correction, if implemented promptly after stabilizing the general condition, can have a relatively good prognosis. Coexisting fetal ileal atresia and gestational diabetes mellitus are rare occurrences, which can make each condition even more difficult to treat.

Echogenic intracardiac foci detection and location in the second-trimester ultrasound and association with fetal outcomes: A systematic literature review.

BACKGROUND: Echogenic Intracardiac Foci (EIF) are non-structural markers identified during the routine 18-20-week foetal anomaly ultrasound scan yet their clinical significance on future outcomes for the infant is unclear. OBJECTIVE: To examine the association between EIF and risk of preterm birth, chromosomal abnormalities, and cardiac abnormalities. DESIGN: A review across four databases to identify English language journal articles of EIF using a cohort study design. All studies were reviewed for quality using the Critical Appraisal Skills Programme (CASP) checklist and data extracted for comparison and analysis. RESULTS: 19 papers from 9 different countries were included. Combining these studies showed 4.6% (95% CI = 4.55-4.65%) of all pregnancies had EIF which was on the left in 86% of cases, on the right in 3% of cases and bilaterally in 10%. There was no evidence that EIF was associated with higher rates of preterm birth. However, it is possible that infants with EIF were more likely to be terminated rather than be born preterm as there was a 2.1% (range 0.3-4.2%) rate of termination or death of the foetus after week 20 among those with EIF. There was no evidence that EIF alone is highly predictive of chromosomal abnormalities. There was evidence that EIF is associated with higher rates of minor cardiac abnormalities (e.g. ventricular septal defect, tricuspid regurgitation or mitral regurgitation)) with 5.1% (224 of 4385) of those with EIF showing cardiac abnormalities (3.08% in retrospective studies and 17.85% in prospective studies). However, the risk of cardiac defects was only higher with right-sided EIF and where the EIF persisted into the third trimester. However, this is a rare event and would be seen in an estimated 4 per 10,000 pregnancies. CONCLUSION: EIF alone was not associated with adverse outcomes for the infant. Only persistent EIF on the right side showed evidence of carrying a higher risk of cardiac abnormality and would warrant further follow-up.

Comparing three cardiothoracic ratio measurement techniques and creating multivariable scoring system to predict Bart's hydrops fetalis at 17-22 weeks' gestation.

To assess the diagnostic performance of three cardiothoracic (CT) ratio techniques, including diameter, circumference, and area, for predicting hemoglobin (Hb) Bart's disease between 17 and 22 weeks' gestation, and to create a multivariable scoring system using multiple ultrasound markers. Before invasive testing, three CT ratio techniques and other ultrasound markers were obtained in 151 singleton pregnancies at risk of Hb Bart's disease. CT diameter ratio demonstrated the highest sensitivity among the other techniques. Significant predictors included CT diameter ratio > 0.5, middle cerebral artery-peak systolic velocity (MCA-PSV) > 1.5 multiples of the median, and placental thickness > 3 cm. MCA-PSV exhibited the highest sensitivity (97.8%) in predicting affected fetuses. A multivariable scoring achieved excellent sensitivity (100%) and specificity (84.9%) for disease prediction. CT diameter ratio exhibited slightly outperforming the other techniques. Increased MCA-PSV was the most valuable ultrasound marker. Multivariable scoring surpassed single-parameter analysis in predictive capabilities.

Deep learning prediction of renal anomalies for prenatal ultrasound diagnosis.

Deep learning algorithms have demonstrated remarkable potential in clinical diagnostics, particularly in the field of medical imaging. In this study, we investigated the application of deep learning models in early detection of fetal kidney anomalies. To provide an enhanced interpretation of those models' predictions, we proposed an adapted two-class representation and developed a multi-class model interpretation approach for problems with more than two labels and variable hierarchical grouping of labels. Additionally, we employed the explainable AI (XAI) visualization tools Grad-CAM and HiResCAM, to gain insights into model predictions and identify reasons for misclassifications. The study dataset consisted of 969 ultrasound images from unique patients; 646 control images and 323 cases of kidney anomalies, including 259 cases of unilateral urinary tract dilation and 64 cases of unilateral multicystic dysplastic kidney. The best performing model achieved a cross-validated area under the ROC curve of 91.28% ± 0.52%, with an overall accuracy of 84.03% ± 0.76%, sensitivity of 77.39% ± 1.99%, and specificity of 87.35% ± 1.28%. Our findings emphasize the potential of deep learning models in predicting kidney anomalies from limited prenatal ultrasound imagery. The proposed adaptations in model representation and interpretation represent a novel solution to multi-class prediction problems.

IMU-Assisted Manual 3D-Ultrasound Imaging Using Motion-Constrained Swept-Fan Scans.

Three-dimensional (3D) ultrasonic imaging can enable post-facto plane of interest selection. It can be performed with devices such as wobbler probes, matrix probes, and sensor-based probes. Ultrasound systems that support 3D-imaging are expensive with added hardware complexity compared to 2D-imaging systems. An inertial measurement unit (IMU) can potentially be used for 3D-imaging by using it to track the motion of a one-dimensional array probe and constraining its motion in one degree of freedom (1-DoF) rotation (swept-fan). This work demonstrates the feasibility of an affordable IMU-assisted manual 3D-ultrasound scanner (IAM3US). A consumer-grade IMU-assisted 3D scanner prototype is designed with two support structures for swept-fan. After proper IMU calibration, an appropriate KF-based algorithm estimates the probe orientation during the swept-fan. An improved scanline-based reconstruction method is used for volume reconstruction. The evaluation of the IAM3US system is done by imaging a tennis ball filled with water and the head region of a fetal phantom. From fetal phantom reconstructed volumes, suitable 2D planes are extracted for biparietal diameter (BPD) manual measurements. Later, in-vivo data is collected. The novel contributions of this paper are (1) the application of a recently proposed algorithm for orientation estimation of swept-fan for 3D imaging, chosen based on the noise characteristics of selected consumer grade IMU (2) assessment of the quality of the 1-DoF swept-fan scan with a deflection detector along with monitoring of maximum angular rate during the scan and (3) two probe holder designs to aid the operator in performing the 1-DoF rotational motion and (4) end-to-end 3D-imaging system-integration. Phantom studies and preliminary in-vivo obstetric scans performed on two patients illustrate the usability of the system for diagnosis purposes.

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.

Malformations of cortical development: Fetal imaging and genetics.

BACKGROUND: Malformations of cortical development (MCD) are a group of congenital disorders characterized by structural abnormalities in the brain cortex. The clinical manifestations include refractory epilepsy, mental retardation, and cognitive impairment. Genetic factors play a key role in the etiology of MCD. Currently, there is no curative treatment for MCD. Phenotypes such as epilepsy and cerebral palsy cannot be observed in the fetus. Therefore, the diagnosis of MCD is typically based on fetal brain magnetic resonance imaging (MRI), ultrasound, or genetic testing. The recent advances in neuroimaging have enabled the in-utero diagnosis of MCD using fetal ultrasound or MRI. METHODS: The present study retrospectively reviewed 32 cases of fetal MCD diagnosed by ultrasound or MRI. Then, the chromosome karyotype analysis, single nucleotide polymorphism array or copy number variation sequencing, and whole-exome sequencing (WES) findings were presented. RESULTS: Pathogenic copy number variants (CNVs) or single-nucleotide variants (SNVs) were detected in 22 fetuses (three pathogenic CNVs [9.4%, 3/32] and 19 SNVs [59.4%, 19/32]), corresponding to a total detection rate of 68.8% (22/32). CONCLUSION: The results suggest that genetic testing, especially WES, should be performed for fetal MCD, in order to evaluate the outcomes and prognosis, and predict the risk of recurrence in future pregnancies.

A deep learning framework for identifying and segmenting three vessels in fetal heart ultrasound images.

BACKGROUND: Congenital heart disease (CHD) is one of the most common birth defects in the world. It is the leading cause of infant mortality, necessitating an early diagnosis for timely intervention. Prenatal screening using ultrasound is the primary method for CHD detection. However, its effectiveness is heavily reliant on the expertise of physicians, leading to subjective interpretations and potential underdiagnosis. Therefore, a method for automatic analysis of fetal cardiac ultrasound images is highly desired to assist an objective and effective CHD diagnosis. METHOD: In this study, we propose a deep learning-based framework for the identification and segmentation of the three vessels-the pulmonary artery, aorta, and superior vena cava-in the ultrasound three vessel view (3VV) of the fetal heart. In the first stage of the framework, the object detection model Yolov5 is employed to identify the three vessels and localize the Region of Interest (ROI) within the original full-sized ultrasound images. Subsequently, a modified Deeplabv3 equipped with our novel AMFF (Attentional Multi-scale Feature Fusion) module is applied in the second stage to segment the three vessels within the cropped ROI images. RESULTS: We evaluated our method with a dataset consisting of 511 fetal heart 3VV images. Compared to existing models, our framework exhibits superior performance in the segmentation of all the three vessels, demonstrating the Dice coefficients of 85.55%, 89.12%, and 77.54% for PA, Ao and SVC respectively. CONCLUSIONS: Our experimental results show that our proposed framework can automatically and accurately detect and segment the three vessels in fetal heart 3VV images. This method has the potential to assist sonographers in enhancing the precision of vessel assessment during fetal heart examinations.

Routine Antenatal Echocardiography in High-Prevalence Areas of Rheumatic Heart Disease: A WHO-Guideline Systematic Review.

Background: Rheumatic Heart Disease (RHD) is the most common cause of valvular heart disease worldwide. Undiagnosed or untreated RHD can complicate pregnancy and lead to poor maternal and fetal outcomes and is a significant factor in non-obstetric morbidity. Echocardiography has an emerging role in screening for RHD. We aimed to critically analyse the evidence on the use of echocardiography for screening pregnant women for RHD in high-prevalence areas. Methods: We searched MEDLINE and Embase to identify the relevant reports. Two independent reviewers assessed the reports against the eligibility criteria in a double-blind process. Results: The searches (date: 4 April 2023) identified 432 records for screening. Ten non-controlled observational studies were identified, five using portable or handheld echocardiography, comprising data from 23,166 women. Prevalence of RHD varied across the studies, ranging from 0.4 to 6.6% (I2, heterogeneity >90%). Other cardiac abnormalities (e.g., congenital heart disease and left ventricular systolic dysfunction) were also detected <1% to 2% of cases. Certainty of evidence was very low. Conclusion: Echocardiography as part of antenatal care in high-prevalence areas may detect RHD or other cardiac abnormalities in asymptomatic pregnant women, potentially reducing the rates of disease progression and adverse labor-associated outcomes. However, this evidence is affected by the low certainty of evidence, and lack of studies comparing echocardiography versus standard antenatal care. Prospective Registration: PROSPERO 2022 July 4; CRD42022344081 Available from: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=344081. Research question: 'In areas with a high prevalence of rheumatic heart disease, should handheld echocardiography be added to routine antenatal care?'

Deep-learning computer vision can identify increased nuchal translucency in the first trimester of pregnancy.

OBJECTIVE: Many fetal anomalies can already be diagnosed by ultrasound in the first trimester of pregnancy. Unfortunately, in clinical practice, detection rates for anomalies in early pregnancy remain low. Our aim was to use an automated image segmentation algorithm to detect one of the most common fetal anomalies: a thickened nuchal translucency (NT), which is a marker for genetic and structural anomalies. METHODS: Standardized mid-sagittal ultrasound images of the fetal head and chest were collected for 560 fetuses between 11 and 13 weeks and 6 days of gestation, 88 (15.7%) of whom had an NT thicker than 3.5 mm. Image quality was graded as high or low by two fetal medicine experts. Images were divided into a training-set (n = 451, 55 thick NT) and a test-set (n = 109, 33 thick NT). We then trained a U-Net convolutional neural network to segment the fetus and the NT region and computed the NT:fetus ratio of these regions. The ability of this ratio to separate thick (anomalous) NT regions from healthy, typical NT regions was first evaluated in ground-truth segmentation to validate the metric and then with predicted segmentation to validate our algorithm, both using the area under the receiver operator curve (AUROC). RESULTS: The ground-truth NT:fetus ratio detected thick NTs with 0.97 AUROC in both the training and test sets. The fetus and NT regions were detected with a Dice score of 0.94 in the test set. The NT:fetus ratio based on model segmentation detected thick NTs with an AUROC of 0.96 relative to clinician labels. At a 91% specificity, 94% of thick NT cases were detected (sensitivity) in the test set. The detection rate was statistically higher (p = 0.003) in high versus low-quality images (AUROC 0.98 vs. 0.90, respectively). CONCLUSION: Our model provides an explainable deep-learning method for detecting increased NT. This technique can be used to screen for other fetal anomalies in the first trimester of pregnancy.

[Congenital diaphragmatic hernia : Imaging-from diagnosis to aftercare]. / Kongenitale Zwerchfellhernie : Bildgebung von der Diagnose bis zur Nachsorge.

STANDARD RADIOLOGICAL METHODS: Fetal: Ultrasound and magnetic resonance imaging (MRI); postnatal: conventional X­ray diagnostics, computed tomography (CT) and MRI. METHODICAL INNOVATIONS: MRI-based lung ventilation and perfusion measurement. PRACTICAL RECOMMENDATIONS: Lifelong follow-up care should be provided, in which radiology is part of the treatment team.

[Developmental disorders of the gastrointestinal tract]. / Entwicklungsstörungen des Gastrointestinaltrakts.

BACKGROUND: Developmental disorders of the gastrointestinal tract comprise a broad spectrum of congenital malformations of different etiologies and locations from the mouth to the anus. METHODS: The authors present the most important malformations of the gastrointestinal tract on the basis of basic and current reviews. RESULTS: Gastrointestinal developmental disorders occur both sporadically and in connection with malformation syndromes. Symptoms are highly variable and range from postnatal emergencies to asymptomatic abnormalities, which may be incidental radiological findings. Prenatal ultrasound examinations can often identify gastrointestinal developmental disorders at an early stage. Here, fetal magnetic resonance imaging can be a useful addition to the diagnostic process. In the first few days of life, simple X­ray overview images, supplemented by images after the administration of contrast medium, are often sufficient. CONCLUSION: Many patients with a malformation of the gastrointestinal tract require lifelong medical care, so that not only pediatric radiologists need specific knowledge about this group of diseases.

Fetal magnetic resonance imaging, ultrasound, and echocardiography findings in twin reversed arterial perfusion sequence.

BACKGROUND: Twin reversed arterial perfusion (TRAP) sequence is a rare complication of monochorionic multiple gestation pregnancies, in which the pump twin provides hemodynamic support to a nonviable co-twin (acardius). Fetal magnetic resonance imaging (MRI) is used to detect pump twin abnormalities, particularly brain ischemia, prior to fetal intervention to interrupt umbilical blood flow to the acardius. OBJECTIVE: To summarize the imaging findings of TRAP sequence pregnancies in a large series. MATERIALS AND METHODS: A single-center retrospective review was performed of all TRAP sequence pregnancies referred for fetal MRI (2004-2021). Fetal MRI, ultrasound, and echocardiography data were collected. RESULTS: Eighty-eight TRAP sequence pregnancies with MRI were included (mean gestational age, 19.8±2.8 weeks). Demise of the pump twin was noted in two pregnancies at the time of MRI. By MRI, 12% (10/86) of live pump twins had abnormalities, including 3% (3/86) with brain abnormalities and 9% (8/86) with extra-cranial abnormalities. By echocardiography, 7% (6/86) of pump twins had structural cardiac abnormalities. Three acardius morphological subtypes were identified by MRI: acephalus (55%, 48/88), anceps (39%, 34/88), and amorphous (7%, 6/88). The mean ultrasound acardius to pump twin ratio A/P ratio, calculated for each twin pair as the ratio of the acardius trunk (and head, if present) plus limb volume to the pump twin estimated fetal weight) differed among the three acardius subtypes (P=.03). The mean A/P ratio moderately correlated with pump twin cardiothoracic ratio and combined cardiac output (Pearson's r=0.45 and 0.48, respectively, both P<.001). CONCLUSION: Fetal MRI of TRAP sequence pregnancies found anomalies in a substantial number of pump twins. The three acardius subtypes differed in A/P ratio, which moderately correlated with the pump twin cardiothoracic ratio and combined cardiac output.

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.

Predicting High-Risk Fetal Cardiac Disease Anticipated to Need Immediate Postnatal Stabilization and Intervention with Planned Pediatric Cardiac Operating Room Delivery.

BACKGROUND: Distances between delivery and cardiac services can make the care of fetuses with cardiac disease at risk of acute cardiorespiratory instability at birth a challenge. In 2013 we implemented a fetal echocardiography-based algorithm targeting fetuses considered high risk for acute cardiorespiratory instability at ≤2 hours of birth for delivery in our pediatric cardiac operating room of our children's hospital, and, herein, examine our experience. METHODS AND RESULTS: We reviewed maternal and postnatal medical records of all fetuses with cardiac disease encountered January 2013 to March 2022 considered high risk for acute cardiorespiratory instability. Secondary analysis was performed including all fetuses with diagnoses of d-transposition of the great arteries/intact ventricular septum (d-TGA/IVS) and hypoplastic left heart syndrome (HLHS) encountered over the study period. Forty fetuses were considered high risk for acute cardiorespiratory instability: 15 with d-TGA/IVS and 7 with HLHS with restrictive atrial septum, 4 with absent pulmonary valve syndrome, 3 with obstructed anomalous pulmonary veins, 2 with severe Ebstein anomaly, 2 with thoracic/intracardiac tumors, and 7 others. Pediatric cardiac operating room delivery occurred for 33 but not for 7 (5 with d-TGA/IVS, 2 with HLHS with restrictive atrial septum). For high-risk cases, fetal echocardiography had a positive predictive value of 50% for intervention/extracorporeal membrane oxygenation/death at ≤2 hours and 70% at ≤24 hours. Of "low-risk" cases, 6/46 with d-TGA/IVS and 0/45 with HLHS required intervention at ≤2 hours. Fetal echocardiography for predicting intervention/extracorporeal membrane oxygenation/death at ≤2 hours had a sensitivity of 67%, specificity 93%, and positive and negative predictive values of 80% and 87%, respectively, for d-TGA/IVS, and 100%, 95%, 71%, and 100% for HLHS, respectively. CONCLUSIONS: Fetal echocardiography can predict the need for urgent intervention in a majority with d-TGA/IVS and HLHS and in half of the entire spectrum of high-risk cardiac disease.

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.

Two-dimensional (2D) placental ultrasound measurements - The correlation with placental volume measured by magnetic resonance imaging (MRI).

INTRODUCTION: Information about placental size in ongoing pregnancies may aid the identification of pregnancies with increased risk of adverse outcome. Placental volume can be measured using magnetic resonance imaging (MRI). However, this method is not universally available in antenatal care. Ultrasound is the diagnostic tool of choice in pregnancy. Therefore, we studied whether simple two-dimensional (2D) ultrasound placental measurements were correlated with placental volume measured by MRI. METHODS: We examined a convenience sample of 104 ongoing pregnancies at gestational week 27, using both ultrasound and MRI. The ultrasound measurements included placental length, width and thickness. Placental volume was measured using MRI. The correlation between each 2D placental ultrasound measurement and placental volume was estimated by applying Pearson's correlation coefficient (r). RESULTS: Mean placental length was 17.2 cm (SD 2.1 cm), mean width was 14.7 cm (SD 2.1 cm), and mean thickness was 3.2 cm (SD 0.6 cm). Mean placental volume was 536 cm3 (SD 137 cm3). The 2D ultrasound measurements showed poor correlation with placental volume (placental length; r = 0.27, width; r = 0.37, and thickness r = 0.13). DISCUSSION: Simple 2D ultrasound measurements of the placenta were poorly correlated with placental volume and cannot be used as proximate measures of placental volume. Our finding may be explained by the large variation between pregnancies in intrauterine placental shape.

Three-dimensional volume rendering ultrasound for assessing placenta accreta spectrum severity and discriminating it from simple scar dehiscence.

BACKGROUND: Prenatal ultrasound discrimination between placenta accreta spectrum and scar dehiscence with underlying nonadherent placenta is challenging both prenatally and intraoperatively, which often leads to overtreatment. In addition, accurate prenatal prediction of surgical difficulty and morbidity in placenta accreta spectrum is difficult, which precludes appropriate multidisciplinary planning. The advent of advanced 3-dimensional volume rendering and contrast enhancement techniques in modern ultrasound systems provides a comprehensive prenatal assessment, revealing details that are not discernible in traditional 2-dimensional imaging. OBJECTIVE: This study aimed to evaluate the use of 3-dimensional volume rendering ultrasound techniques in determining the severity of placenta accreta spectrum and distinguishing between placenta accreta spectrum and scar dehiscence with underlying nonadherent placenta. STUDY DESIGN: A prospective, cohort study was conducted between July 2022 and July 2023 in the fetal medicine unit of Dr Soetomo Academic General Hospital, Surabaya, Indonesia. All pregnant individuals with anterior low-lying placenta or placenta previa with a previous caesarean section who were referred with suspicion of placenta accreta spectrum were consented and screened using the standardised 2-dimensional and Doppler ultrasound imaging. Additional 3-dimensional volumes were obtained from the sagittal section of the uterus with a filled urinary bladder. These were analyzed by rotating the region of interest to be perpendicular to the uterovesical interface. The primary outcomes were the clinical and histologic severity in the cases of placenta accreta spectrum and correct diagnosis of dehiscence with nonadherent placenta underneath. The strength of association between ultrasound and clinical outcomes was determined. Multivariate logistic regression analyses and diagnostic testing of accuracy were used to analyze the data. RESULTS: A total of 70 patients (56 with placenta accreta spectrum and 14 with scar dehiscence) were included in the analysis. Multivariate logistic regression of all 2-dimensional and 3-dimensional signs revealed the 3-dimensional loss of clear zone (P<.001) and the presence of bridging vessels on 2-dimensional Doppler ultrasound (P=.027) as excellent predictors in differentiating scar dehiscence and placenta accreta spectrum. The 3-dimensional loss of clear zone demonstrated a high diagnostic accuracy with an area under the curve of 0.911 (95% confidence interval, 0.819-1.002), with a sensitivity of 89.3% (95% confidence interval, 78.1-95.97%) and specificity of 92.9% (95% confidence interval, 66.1-99.8%). The presence of bridging vessels on 2-dimensional Doppler demonstrated an area under the curve of 0.848 (95% confidence interval, 0.714-0.982) with a sensitivity of 91.1% (95% confidence interval, 80.4-97.0%) and specificity of 78.6% (95% confidence interval, 49.2-95.3%). A subgroup analysis among the placenta accreta spectrum group revealed that the presence of a 3-dimensional disrupted bladder serosa with obliteration of the vesicouterine space was associated with vesicouterine adherence (P<.001). CONCLUSION: Three-dimensional volume rendering ultrasound is a promising tool for effective discrimination between scar dehiscence with underlying nonadherent placenta and placenta accreta spectrum. It also shows potential in predicting the clinical severity with urinary bladder involvement in cases of placenta accreta spectrum.