Evaluation of placental maturity by the sonographic textures

Evaluation of placental oxygenation by near-infrared spectroscopy in relation to ultrasound maturation grade in physiological term pregnancies

A prospective observational study (ClinicalTrial ID: NCT05771415) was conducted to compare placental oxygenation in low-risk, uncomplicated term pregnancies measured by near-infrared spectroscopy (NIRS) in relation to the placental maturity grade determined by ultrasound assessment according to the Grannum scale. We included 34 pregnancies divided into two groups according to placental maturation. For each pregnancy, measurements were taken at the site above the central part of the placenta (test) and at the site outside of the placenta on the lower abdomen (control). Student's t-test was used to compare tissue oxygenation index (TOI) values among the study groups. The normality of distribution was proven by the Kolmogorov‒Smirnov test. In women with low placental maturity grade, the mean TOI value above the placenta was 70.38 ± 3.72, which was lower than the respective value in women with high placental maturity grade (77.99 ± 3.71; p < 0.001). The TOI values above the placenta and the control site were significantly different in both groups (70.38 ± 3.72 vs 67.83 ± 3.21 and 77.99 ± 3.71 vs 69.41 ± 3.93; p < 0.001). The results offer a new perspective on placental function based on specific non-invasive real-time oxygenation measurements. Unfortunately, and because of technical limitations, NIRS cannot yet be implemented as a routine clinical tool.

Influence of maternal psychological distress during COVID-19 pandemic on placental morphometry and texture

The Coronavirus Disease 2019 (COVID-19) pandemic has been accompanied by increased prenatal maternal distress (PMD). PMD is associated with adverse pregnancy outcomes which may be mediated by the placenta. However, the potential impact of the pandemic on in vivo placental development remains unknown. To examine the impact of the pandemic and PMD on in vivo structural placental development using advanced magnetic resonance imaging (MRI), acquired anatomic images of the placenta from 63 pregnant women without known COVID-19 exposure during the pandemic and 165 pre-pandemic controls. Measures of placental morphometry and texture were extracted. PMD was determined from validated questionnaires. Generalized estimating equations were utilized to compare differences in PMD placental features between COVID-era and pre-pandemic cohorts. Maternal stress and depression scores were significantly higher in the pandemic cohort. Placental volume, thickness, gray level kurtosis, skewness and run length non-uniformity were increased in the pandemic cohort, while placental elongation, mean gray level and long run emphasis were decreased. PMD was a mediator of the association between pandemic status and placental features. Altered in vivo placental structure during the pandemic suggests an underappreciated link between disturbances in maternal environment and perturbed placental development. The long-term impact on offspring is currently under investigation.

Multimodal fusion model for classifying placenta ultrasound imaging in pregnancies with hypertension disorders

BACKGROUND

A multimodal fusion model was proposed to assist the traditional visual diagnosis in evaluating the placental features of hypertension disorders of pregnancy (HDP).

OBJECTIVE

The aim of this study was to analyse and compare the placental features between normal and HDP pregnancies and propose a multimodal fusion deep learning model for differentiating and characterizing the placental features from HDP to normal pregnancy.

METHODS

This observational prospective study included 654 pregnant women, including 75 with HDPs. Grayscale ultrasound images (GSIs) and Microflow images (MFIs) of the placentas were collected from all patients during routine obstetric examinations. On the basis of intelligent extraction and features fusion, after quantities of training and optimization, the classification model named GMNet (the intelligent network based on GSIs and MFIs) was introduced for differentiating the placental features of normal and HDP pregnancies. The distributions of placental features extracted by the deep convolutional neural networks (DCNNs) were visualized by Uniform Manifold Approximation and Projection for Dimension Reduction (UMAP). Metrics including sensitivity, specificity, accuracy, and the area under the curve (AUC) were used to score the model. Finally, placental tissue samples were randomly selected for microscopic analyses to prove the interpretability and effectiveness of the GMNet model.

RESULTS

Compared with the Normal group in ultrasonic images, the light spots were rougher and the parts with focal cystic or hypoechogenic lesions were increased in the HDP groups. The overall diagnostic performance of the GMNet model depending on the region of interest (ROI) was excellent (AUC: 97%), with a sensitivity of 90.0%, a specificity of 93.5%, and an accuracy of 93.1%. The fusion features of GSIs and MFIs in the placenta showed a higher discriminative power than single-mode features (fusion features vs GSI features vs MFI features, 97.0% vs 91.2% vs 94.8%). Furthermore, according to the microscopic analysis, unevenly distributed villi, increased syncyte nodules and aggregated intervillous cellulose deposition were particularly frequent in the HDP cases.

CONCLUSIONS

The GMNet model could sensitively identify abnormal changes in the placental microstructure in pregnancies with HDP.

Artificial Intelligence in Prenatal Ultrasound Diagnosis

The application of artificial intelligence (AI) technology to medical imaging has resulted in great breakthroughs. Given the unique position of ultrasound (US) in prenatal screening, the research on AI in prenatal US has practical significance with its application to prenatal US diagnosis improving work efficiency, providing quantitative assessments, standardizing measurements, improving diagnostic accuracy, and automating image quality control. This review provides an overview of recent studies that have applied AI technology to prenatal US diagnosis and explains the challenges encountered in these applications.

Normative placental structure in pregnancy using quantitative Magnetic Resonance Imaging

INTRODUCTION

To characterize normative morphometric, textural and microstructural placental development by applying advanced and quantitative magnetic resonance imaging (qMRI) techniques to the in-vivo placenta.

METHODS

We enrolled 195 women with uncomplicated, healthy singleton pregnancies in a prospective observational study. Women underwent MRI between 16- and 40-weeks' gestation. Morphometric and textural metrics of placental growth were calculated from T2-weighted (T2W) images, while measures of microstructural development were calculated from diffusion-weighted images (DWI). Normative tables and reference curves were constructed for each measured index across gestation and according to fetal sex.

RESULTS

Data from 269 MRI studies from 169 pregnant women were included in the analyses. During the study period, placentas undergo significant increases in morphometric measures of volume, thickness, and elongation. Placental texture reveals increasing variability with advancing gestation as measured by grey level non uniformity, run length non uniformity and long run high grey level emphasis. Placental microstructure did not vary with gestational age. Placental elongation was the only metric that differed significantly between male and female fetuses.

DISCUSSION

We report quantitative metrics of placental morphometry, texture and microstructure in a large cohort of healthy controls during the second and third trimesters of pregnancy. These measures can serve as normative references of in-vivo placental development to better understand placental function in high-risk conditions and allow for the early detection of placental mal-development.

Multiscale and multimodal imaging of utero-placental anatomy and function in pregnancy

Placental structures at the nano-, micro-, and macro scale each play important roles in contributing to its function. As such, quantifying the dynamic way in which placental structure evolves during pregnancy is critical to both clinical diagnosis of pregnancy disorders, and mechanistic understanding of their pathophysiology. Imaging the placenta, both exvivo and invivo, can provide a wealth of structural and/or functional information. This review outlines how imaging across modalities and spatial scales can ultimately come together to improve our understanding of normal and pathological pregnancies. We discuss how imaging technologies are evolving to provide new insights into placental physiology across disciplines, and how advanced computational algorithms can be used alongside state-of-the-art imaging to obtain a holistic view of placental structure and its associated functions to improve our understanding of placental function in health and disease.

New Frontiers in Placenta Tissue Imaging

The placenta is a highly vascularized organ with unique structural and metabolic complexities. As the primary conduit of fetal support, the placenta mediates transport of oxygen, nutrients, and waste between maternal and fetal blood. Thus, normal placenta anatomy and physiology is absolutely required for maintenance of maternal and fetal health during pregnancy. Moreover, impaired placental health can negatively impact offspring growth trajectories as well as increase the risk of maternal cardiovascular disease later in life. Despite these crucial roles for the placenta, placental disorders, such as preeclampsia, intrauterine growth restriction (IUGR), and preterm birth, remain incompletely understood. Effective noninvasive imaging and image analysis are needed to advance the obstetrician's clinical reasoning toolkit and improve the utility of the placenta in interpreting maternal and fetal health trajectories. Current paradigms in placental imaging and image analysis aim to improve the traditional imaging techniques that may be time-consuming, costly, or invasive. In concert with conventional clinical approaches such as ultrasound (US), advanced imaging modalities can provide insightful information on the structure of placental tissues. Herein we discuss such imaging modalities, their specific applications in structural, vascular, and metabolic analysis of placental health, and emerging frontiers in image analysis research in both preclinical and clinical contexts.

Evaluation of adenomyosis after gonadotrophin-releasing hormone agonist therapy using ultrasound post-processing imaging: a pilot study

Objective

We explored a method for the quantitative sonographic analysis of myometrial texture using computer-aided image analysis software to assess outcomes following treatment with gonadotrophin-releasing hormone (GnRH) agonist for adenomyosis in women with infertility.

Method

Data for patients with ultrasound images of the myometrium obtained at Taipei Medical University Hospital from 1 September 2018 to 5 April 5 2019 were analyzed. Only 10 patients with 20 ultrasound images matched the eligibility criteria. The images were divided into pre-treatment (n = 10) and post-treatment images (n = 10) and quantitative grayscale histograms were obtained from the ultrasound images using publicly available ImageJ computer-aided image analysis software. We analyzed the differences between the pre- and post-treatment images using the Mann–Whitney test and compared the results with outcomes assessed by serum CA-125 levels.

Results

Image analysis of the grayscale histograms revealed significant differences between before and after treatment. The classification of the myometrium pre-treatment and post-treatment was similar using CA-125 and histogram grayscale analysis.

Conclusion

Computer-aided image analysis of grayscale histograms of the myometrium obtained from ultrasound images is an alternative method for assessing myometrial conditions after GnRH agonist treatment in patients with adenomyosis.

In vivo textural and morphometric analysis of placental development in healthy & growth-restricted pregnancies using magnetic resonance imaging

BACKGROUND

The objective of this study was to characterize structural changes in the healthy in vivo placenta by applying morphometric and textural analysis using magnetic resonance imaging (MRI), and to explore features that may be able to distinguish placental insufficiency in fetal growth restriction (FGR).

METHODS

Women with healthy pregnancies or pregnancies complicated by FGR underwent MRI between 20 and 40 weeks gestation. Measures of placental morphometry (volume, elongation, depth) and digital texture (voxel-wise geometric and signal-intensity analysis) were calculated from T2W MR images.

RESULTS

We studied 66 pregnant women (32 healthy controls, 34 FGR); during the study period, placentas undergo significant increases in size; signal intensity remains relatively constant, however there is increasing variation in spatial arrangements, suggestive of progressive microstructural heterogeneity. In FGR, placental size is smaller, with great homogeneity of signal intensity and spatial arrangements.

CONCLUSION

We report quantitative textural and morphometric changes in the in vivo placenta in healthy controls over the second half of pregnancy. These MRI features demonstrate important differences in placental development in the setting of placental insufficiency that relate to onset and severity of FGR, as well as neonatal outcome.

Texture analysis of magnetic resonance images of the human placenta throughout gestation: A feasibility study

As fetal gestational age increases, other modalities such as ultrasound have demonstrated increased levels of heterogeneity in the normal placenta. In this study, we introduce and apply ROI-based texture analysis to a retrospective fetal MRI database to characterize the second-order statistics of placenta and to evaluate the relationship between heterogeneity and gestational age. Positive correlations were observed for several Haralick texture metrics derived from fetal-brain specific T2-weighted and gravid uterus T1-weighted and T2-weighted images, confirming a quantitative increase in placental heterogeneity with gestational age. Our study shows the importance of identifying baseline MR textural changes at certain gestational ages from which placental diseased states may be compared. Specifically, when evaluating for placental invasion or insufficiency, findings should be evaluated in the context of the normal placental aging process, which occurs throughout gestation.

Segmentation and classification in MRI and US fetal imaging: Recent trends and future prospects

Fetal imaging is a burgeoning topic. New advancements in both magnetic resonance imaging and (3D) ultrasound currently allow doctors to diagnose fetal structural abnormalities such as those involved in twin-to-twin transfusion syndrome, gestational diabetes mellitus, pulmonary sequestration and hypoplasia, congenital heart disease, diaphragmatic hernia, ventriculomegaly, etc. Considering the continued breakthroughs in utero image analysis and (3D) reconstruction models, it is now possible to gain more insight into the ongoing development of the fetus. Best prenatal diagnosis performances rely on the conscious preparation of the clinicians in terms of fetal anatomy knowledge. Therefore, fetal imaging will likely span and increase its prevalence in the forthcoming years. This review covers state-of-the-art segmentation and classification methodologies for the whole fetus and, more specifically, the fetal brain, lungs, liver, heart and placenta in magnetic resonance imaging and (3D) ultrasound for the first time. Potential applications of the aforementioned methods into clinical settings are also inspected. Finally, improvements in existing approaches as well as most promising avenues to new areas of research are briefly outlined.

Measurement of elasticity of normal placenta using the Virtual Touch quantification technique

Purpose:

The aim of this study was to measure the elasticity of normal placentas using the Virtual Touch quantification (VTQ) technique.

Methods:

This study was approved by the Institutional Ethics Committee. Fifty randomly selected, healthy pregnant women in their second trimester and 50 randomly selected, healthy pregnant women in their third trimester with a single fetus were included, and their placentas underwent VTQ through shear wave velocity (SWV) measurements. The measurements were performed at different locations to sample different areas of the placenta. Measurements were performed 3-4 times in each location, the mean shear wave velocities were calculated without the highest and lowest values of measurements in each region, and the results were compared.

Results:

The SWV of the placenta was 0.983±0.260 m/sec, and the minimal and maximal speed was 0.63 m/sec and 1.84 m/sec, respectively. There was no significant difference between the second and third trimester of VTQ of the placenta in terms of SWV (0.978±0.255 m/sec vs. 0.987±0.266 m/sec, P=0.711). The maternal age between second and third trimester was 27.9±4.3 years and 29.2±4.4 years, respectively; there was no significant difference between them (P=0.159).

Conclusion:

The results of this study show that the SWV of normal placenta tissue is 0.983±0.260 m/sec, it has little variation between the second and third trimesters, and the VTQ technique may potentially play an additional role in placenta evaluation.

Discriminative Learning for Automatic Staging of Placental Maturity via Multi-layer Fisher Vector

Currently, placental maturity is performed using subjective evaluation, which can be unreliable as it is highly dependent on the observations and experiences of clinicians. To address this problem, this paper proposes a method to automatically stage placenta maturity from B-mode ultrasound (US) images based on dense sampling and novel feature descriptors. Specifically, our proposed method first densely extracts features with a regular grid based on dense sampling instead of a few unreliable interest points. Followed by, these features are clustered using generative Gaussian mixture model (GMM) to obtain high order statistics of the features. The clustering representatives (i.e., cluster means) are encoded by Fisher vector (FV) for staging accuracy enhancement. Differing from the previous studies, a multi-layer FV is investigated to exploit the spatial information rather than the single layer FV. Experimental results show that the proposed method with the dense FV has achieved an area under the receiver of characteristics (AUC) of 96.77%, sensitivity and specificity of 98.04% and 93.75% for the placental maturity staging, respectively. Our experimental results also demonstrate that the dense feature outperforms the traditional sparse feature for placental maturity staging.

True Reproducibility of UltraSound Techniques (TRUST): systematic review of reliability studies in obstetrics and gynecology

OBJECTIVES

To examine the quality of methods used and the accuracy of the interpretation of agreement in existing studies that examine the reliability of ultrasound measurements and judgments in obstetrics and gynecology.

METHODS

A systematic search of MEDLINE was performed on 25 March 2014, looking for studies that examined the reliability of ultrasound measurements and judgments in obstetrics and gynecology with evaluation of concordance (CCC) or intraclass (ICC) correlation coefficients or kappa as a main objective.

RESULTS

Seven hundred and thirty-three records were examined on the basis of their title and abstract, of which 141 full-text articles were examined completely for eligibility. We excluded 29 studies because they did not report CCC/ICC/kappa, leaving 112 studies that were included in our analysis. Two studies reported both ICC and kappa and were counted twice, therefore, the number used as the denominator in the analyses was 114. Only 16/114 (14.0%) studies were considered to be well designed (independent acquisition and blinded analysis) and to have interpreted the results properly. Most errors occurring in the studies are likely to overestimate the reliability of the method examined.

CONCLUSIONS

The vast majority of published studies examined had important flaws in design, interpretation and/or reporting. Such limitations are important to identify as they might create false confidence in the existing measurements and judgments, jeopardizing clinical practice and future research. Specific guidelines aimed at improving the quality of reproducibility studies that examine ultrasound methods should be encouraged.