Placental Imaging Research Articles

Diagnostic accuracy of magnetic resonance imaging to predict peripartum hysterectomy and neonatal mortality in total placenta previa: A retrospective cohort study

ObjectivesTo assess the reliability of placental magnetic resonance imaging measurements in predicting peripartum hysterectomy and neonatal outcomes in patients with total placenta previa. Study DesignThis retrospective cohort study, conducted at a single tertiary center, identified 372 pregnant women diagnosed with placenta previa. 277 singleton pregnancies that met the inclusion criteria and were diagnosed with total placenta previa in the third trimester were divided into two groups according to whether a placental MRI was performed. Two radiologists analyzed the MRI findings of 150 pregnant women with total placenta previa. Measurements were conducted for the placental volume of the upper and lower uterine sectors, cervical canal length, and cervical canal dilatation. A comparison was made between the surgical progression of these pregnant women and 127 pregnant women with total placenta previa who did not undergo an MRI. After pathological examination, 122 (63.2%) of 193 pregnant women diagnosed with placenta accreta spectrum underwent peripartum total abdominal hysterectomy. The results were compared using logistic regression analysis. ResultsReduced placental volume in the upper uterine segment and increased volume in the lower uterine segment significantly correlated with a higher probability of peripartum hysterectomy (cut-off: ≤343.4 and ≥ 403.4 cm3; OR: 0.993, 95 % CI: 0.990–0.995 and OR: 1.007, 95 % CI: 1.005–1.009, respectively). Shortened cervical canal length and increased dilatation raise the risk of peripartum hysterectomy (cut-off: ≤34, ≥11 mm; OR: 0.82, 95 % CI: 0.77 − 0.88 and OR: 1.7, 95 % CI: 1.4 − 2.1, respectively). The risk of neonatal death is 32 times higher in those < 34 weeks than in those 34 weeks or higher (95 % CI: 4.2–250, p = 0.001). ConclusionsPlacental MRI significantly contributes to predicting peripartum total abdominal hysterectomy and neonatal mortality in patients with total placenta previa associated with placenta accreta spectrum.

M‐SSD based on anchor proposal and ResNet101 backbone for placental haemorrhage MRI detection

AbstractMRI (magnetic resonance imaging) images can effectively show the placental haemorrhage area. In view of the special properties and real‐time detection requirements of placental haemorrhage MRI images, this paper has systematically improved the single‐shot multi‐box detector (SSD) target detection algorithm (M‐SSD). First, taking advantage of the particularity of the MRI image, the maximum stable extremum region (MSER) algorithm was used as the anchor proposal network which integrated the proposal information into the feature layer of SSD to avoid the hungry traversal of the original algorithm. Second, after the scale statistics of the placental haemorrhage area in MRI images, the bounding box matching the size of the placental haemorrhage area was redefined, in this way, the scale of the bounding box will have application pertinence, which can effectively improve the detection accuracy of the algorithm. Third, due to the small target property of the placental haemorrhage area in the MRI image, the VGG16 basic network in the original SSD was replaced by ResNet101, this made the algorithm have higher performance in small target detection. Finally, the Placental Haemorrhage MRI Detection Database (PHMD) has been built which is not only a base for this paper, but also for further research in this area.

Prediction of hemorrhage in placenta previa: Radiomics analysis of pelvic MRI images

IntroductionPrediction of intraoperative massive hemorrhage is still challenging in placenta previa. Radiomics analysis has been investigated as a new evaluation method for analyzing medical images. We used radiomics analysis on placental magnetic resonance imaging (MRI) images to predict intraoperative hemorrhage in placenta previa. MethodsWe used the sagittal MRI T2-weighted sequence in placenta previa. Using the rectangular region from the uterine os to the anterior wall, we extracted 97 radiomics features. We also collected patient demographics and blood test data as clinical variables. Combining these radiomics features and clinical variables, logistic regression models with a stepwise method were built to predict the risk of hemorrhage, defined as blood loss of > 2000 ml. We evaluated the prediction performance of the models using accuracy and area under the curve (AUC), also analyzing the important variables for the prediction by stepwise methods. ResultsWe enrolled a total of 63 placenta previa cases including 30 hemorrhage cases from two institutes. The model combining clinical variables and radiomics features showed the best prediction performance with an accuracy of 0.70 and an AUC of 0.69 in the internal validation data, and accuracy of 0.41 and an AUC of 0.70 in the external validation data, compared with human experts (accuracy of 0.62). Regarding variable selection, two radiomics features. ’original_glrlm_LowGrayLevelRunEmphasis,’ and ’diagnostics_Image-original_Minimum,’ were important predictors for hemorrhage by the stepwise method. DiscussionRadiomics features based on MRI could be used as effective predictive variables for hemorrhage in placenta previa. Radiomics analysis of placental imaging could lead to further analysis of quantitative variables related to obstetric diseases.

Associating Maternal Placental Growth Factor Levels with Placental Imaging and Pathology: Insights from a High-Diabetes Prevalence Population

Associating Maternal Placental Growth Factor Levels with Placental Imaging and Pathology: Insights from a High-Diabetes Prevalence Population

PFAS alters placental arterial vasculature in term human placentae: A prospective pregnancy cohort study

IntroductionPerfluoroalkyl substances (PFAS) are synthetic chemicals used in industrial and consumer goods that are widely detected in human populations and are associated with adverse health outcomes, including perinatal health risks and child health. One mechanism of influence may be the impact of PFAS exposure on placental structure and function. ObjectivesThe objective of this study is to investigate the relationship between maternal prenatal exposure to PFAS and measures of placental vascularization, and to assess whether changes in vascularization play a role in mediating the impact of PFAS on birth outcomes. MethodsUsing data from a prospective cohort study, we examined associations between second trimester PFAS (individually and as mixtures using Bayesian kernel machine regression) and placental arterial vasculature in term placentae (N = 158); secondarily we evaluated the degree to which alterations in placental arterial vasculature explained associations between PFAS exposure and birth outcomes. Placental arterial vasculature features were collected from arterial tracings of each placental image. ResultsIn both linear regression and mixture models, natural log-transformed perfluorooctanoic acid concentrations were negatively associated with surface vasculature, indexed by the mean distance from arterial end point to perimeter (β = −0.23, 95% CI: −0.41, −0.041); additionally, maximum arterial tortuosity was negatively associated with placental weight (β = −0.19, 95% CI: −0.34, −0.051). There were no reliable differences in effect by fetal sex. DiscussionThe findings provide some of the first evidence of PFAS exposure shaping a key measure of placental vascular function, which may underlie the impact of PFAS on perinatal and child health risks.

Prediction of antenatal bleeding and preterm deliveries using placental magnetic resonance imaging in patients with placenta previa

PurposeThis study aimed to clarify associations between subacute hematoma on placental magnetic resonance imaging (MRI), antenatal bleeding, and preterm deliveries in patients with placenta previa (PP) without placenta accreta spectrum (PAS).Materials and methodsThis retrospective study investigated 78 consecutive patients with PP (median age, 34.5 years; interquartile range [IQR], 31–37 years) who underwent placental MRI in the third trimester. Patients with PAS detected intraoperatively or pathologically were excluded. Two radiologists evaluated the presence of subacute hematomas and their locations on placental MRI. We examined associations between presence of subacute hematoma and antenatal bleeding, emergency cesarean section (CS), hysterectomy, gestational age (GA) at delivery, birth weight, and amount of blood loss at CS. We also examined the association between perinatal outcome and subacute hematoma location: marginal, retro-placental, or intra-placental. Inter-observer agreement for the detection of subacute hematoma was calculated using kappa analysis.ResultsSubacute hematomas were identified on MRI in 39 of the 78 patients (50.0%). Antenatal bleeding and emergency CS were more prevalent in patients with subacute hematoma on MRI (20 patients [51.3%] and 18 patients [46.2%], respectively) than in patients without (7 patients [17.9%], Fisher’s exact test, p = 0.004 and 7 patients [17.9%], p = 0.014, respectively). GA at delivery was significantly lower in patients with subacute hematoma (median 36w3d, IQR 35w4d–37w1d) than in patients without (median 37w1d, IQR 36w4d–37w2d; Mann–Whitney test: p = 0.048). Marginal hematoma was significantly associated with antenatal bleeding and emergency CS. Inter-observer agreement for the presence of subacute hematoma was moderate (κ = 0.573).ConclusionSubacute hematoma on placental MRI was associated with antenatal bleeding, emergency CS and shorter GA at delivery in patients with PP. Marginal hematoma was also associated with antenatal bleeding and emergency CS. Placental MRI appears useful for predicting antenatal bleeding and preterm delivery in patients with PP.

Влияние непрерывного профилактического приема ацетилсалициловой кислоты на гистологическую картину плаценты у беременных группы высокого риска развития задержки роста плода

Background: fetal growth restriction (FGR) is the main cause of perinatal mortality. We have relatively effective methods of predicting and preventing this condition, however, it has not yet been possible to achieve a significant reduction of the FGR incidence. Acetylsalicylic acid (ASA) is the only effective pharmacological method for the prevention of FGR, but the mechanism of its action is still in doubt. Aim: to study the placental morphology in pregnant women at high risk of FGR according to the first trimester screening (FTS), depending on whether female patients took ASA for the FGR prevention or not. Patients and Methods: the study was conducted in 2019–2021. By that time, the clinical recommendations concerning «Insufficient fetal growth requiring the provision of medical care to the mother (fetal growth restriction)», developed by the Ministry of Health of the Russian Federation, had not yet come into effect. In a prospective analysis, the placental morphology of 125 pregnant women at high risk of FGR and 554 placentas of pregnant women at low risk was studied according to the FTS results. Pregnant women at high risk of FGR were divided into 2 groups: 51 female patients received ASA from 14 to 36 weeks pregnant, 74 did not receive ASA; the control group consisted of 554 pregnant women at low risk of FGR according to the FTS. The placenta was examined in 3 stages according to the conventional management scheme, including macroscopic analysis, material incisure and histology. Results: placental pathological changes (dystrophic changes, syncytial knots formation, fibrinoid deposition, disorders of placental villous maturation) in patients who did not receive ASA were significantly more common than in the control group and in the subgroup receiving ASA. Conclusion: FGR incidence decrease in patients receiving ASA was due to an improvement in the histological structure of the placenta. KEYWORDS: fetal growth restriction, small for gestational age, placenta, acetylsalicylic acid. FOR CITATION: Shamugiya V.V., Kuznetsov P.A., Dobrokhotova Yu.E. Effect of the continuous prophylaxis with acetylsalicylic acid on the placental histological image in pregnant women at high risk of fetal growth restriction. Russian Journal of Woman and Child Health. 2024;7(1):12–17 (in Russ.). DOI: 10.32364/2618-8430-2024-7-1-2.

Design of Placental Image Segmentation Reconstruction System Based on MRI Data

Design of Placental Image Segmentation Reconstruction System Based on MRI Data

EP18.21: Trial of quantitative and visualised analysis of the placental blood flow image in abnormal pregnancy

EP18.21: Trial of quantitative and visualised analysis of the placental blood flow image in abnormal pregnancy

Perceiving placental ultrasound image texture evolution during pregnancy with normal and adverse outcome through machine learning prism

IntroductionThe objective was to perform placental ultrasound image texture (UPIA) in first (T1), second(T2) and third(T3) trimesters of pregnancy using machine learning( ML). MethodsIn this prospective observational study the 2D placental ultrasound (US) images from 11-14 weeks, 20-24 weeks, and 28-32 weeks were taken. The image data was divided into training, validating, and testing subsets in the ratio of 80%, 10%, and 10%. Three different ML techniques, deep learning, transfer learning, and vision transformer were used for UPIA. ResultsOut of 1008 cases included in the study, 59.5% (600/1008) had a normal outcome. The image texture classification was compared between T1&T2, T2 &T3 and T1&T3 pairs. Using Inception v3 model, to classify T1& T2 images, gave the accuracy, Cohen Kappa score of 83.3%, 0.662 respectively. The image classification between T1&T3 achieved best results using EfficientNetB0 model, having the accuracy, Cohen Kappa score, sensitivity and specificity of 87.5%, 0.749, 83.4%, and 88.9% respectively. Comparison of placental image texture among cases with materno-fetal adverse outcome and controls was done using Efficient Net B0. The F1 score, was found to be 0.824 , 0.820, and 0.892 in T1, T2 and T3 respectively. The sensitivity and specificity of the model was 77.4% at 80.2% at T1 but increased to 81.0% and 93.9% at T2 &T3 respectively. DiscussionThe study presents a novel technique to classify placental ultrasound image texture using ML models and could differentiate first and third-trimester normal placenta and normal and adverse pregnancy outcome images with good accuracy.

Segmentation methods applied to MRI-derived radiomic analysis for the prediction of placenta accreta spectrum in patients with placenta previa.

To retrospectively evaluate the performance of different manual segmentation methods of placenta MR images for predicting Placenta Accreta Spectrum (PAS) disorders in patients with placenta previa (PP) using a Machine Learning (ML) Radiomics analysis. 64 patients (n=41 with PAS and n= 23 without PAS) with PP who underwent MRI examination for suspicion of PAS were retrospectively selected. All MRI examinations were acquired on a 1.5 T using T2-weighted (T2w) sequences on axial, sagittal and coronal planes. Ten different manual segmentation methods were performed on sagittal placental T2-weighted images obtaining five sets of 2D regions of interest (ROIs) and five sets of 3D volumes of interest (VOIs) from each patient. In detail, ROIs and VOIs were positioned on the following areas: placental tissue, retroplacental myometrium, cervix, placenta with underneath myometrium, placenta with underneath myometrium and cervix. For feature stability testing, the same process was repeated on 30 randomly selected placental MRI examinations by two additional radiologists, working independently and blinded to the original segmentation. Radiomic features were extracted from all available ROIs and VOIs. 100 iterations of 5-fold cross-validation with nested feature selection, based on recursive feature elimination, were subsequently run on each ROI/VOI to identify the best-performing method to classify instances correctly. Among the segmentation methods, the best performance in predicting PAS was obtained by the VOIs covering the retroplacental myometrium (Mean validation score: 0.761, standard deviation: 0.116). Our preliminary results show that the VOI including the retroplacental myometrium using T2w images seems to be the best method when segmenting images for the development of ML radiomics predictive models to identify PAS in patients with PP.

Placenta accreta spectrum in early and late pregnancy from an imaging perspective. A scoping review

Placenta accreta spectrum (PAS) disorders (with increasing order of the depth of invasion: accreta, increta, percreta) are quite challenging for the purpose of diagnosis and treatment. Pathological examination or imaging evaluation are not very dependable when considered as stand-alone diagnostic tools. On the other hand, timely diagnosis is of great importance, as maternal and fetal mortality drastically increases if patient goes through the third phase of delivery in a not well-suited facility. A multidisciplinary approach for diagnosis (incorporating clinical, imaging, and pathological evaluation) is mandatory, particularly in complicated cases. For imaging evaluation, the diagnostic modality of choice in most scenarios is ultrasound (US) exam; patients are referred for MRI when US is equivocal, inconclusive, or not visualizing placenta properly. Herewith, we review the reported US and MRI features of PAS disorders (mainly focusing on MRI), going over the normal placental imaging and imaging pitfalls in each section, and lastly, covering the imaging findings of PAS disorders in the first trimester and cesarean section pregnancy (CSP).

El espectro de placenta acreta en la etapa temprana y final del embarazo. Un repaso a través de la imagen

Placenta accreta spectrum (PAS) disorders (with increasing order of the depth of invasion: accreta, increta, percreta) are quite challenging for the purpose of diagnosis and treatment. Pathological examination or imaging evaluation are not very dependable when considered as stand-alone diagnostic tools. On the other hand, timely diagnosis is of great importance, as maternal and fetal mortality drastically increases if patient goes through the third phase of delivery in a not well-suited facility. A multidisciplinary approach for diagnosis (incorporating clinical, imaging, and pathological evaluation) is mandatory, particularly in complicated cases. For imaging evaluation, the diagnostic modality of choice in most scenarios is ultrasound (US) exam; patients are referred for MRI when US is equivocal, inconclusive, or not visualizing placenta properly. Herewith, we review the reported US and MRI features of PAS disorders (mainly focusing on MRI), going over the normal placental imaging and imaging pitfalls in each section, and lastly, covering the imaging findings of PAS disorders in the first trimester and cesarean section pregnancy (CSP).

Automated segmentation and morphological characterization of placental intervillous space based on a single labeled image

In this study, a novel method of data augmentation has been presented for the segmentation of placental histological images when the labeled data are scarce. This method generates new realizations of the placenta intervillous morphology while maintaining the general textures and orientations. As a result, a diversified artificial dataset of images is generated that can be used for training deep learning segmentation models. We have observed that on average the presented method of data augmentation led to a 42% decrease in the binary cross-entropy loss of the validation dataset compared to the common approach in the literature. Additionally, the morphology of the intervillous space is studied under the effect of the proposed image reconstruction technique, and the diversity of the artificially generated population is quantified. We have demonstrated that the proposed method results in a more accurate morphological characterization of the placental intervillous space with an average feature relative error of 6.5%, which is significantly lower than the 11.5% error observed with conventional augmentation techniques. Due to the high resemblance of the generated images to the real ones, applications of the proposed method may not be limited to placental histological images, and it is recommended that other types of tissue be investigated in future studies.

Ultrasound Placental Remodeling Patterns and Pathology Characteristics in Patients with History of Mild SARS-CoV-2 Infection during Pregnancy

Introduction: This research aims to describe a progressive pattern of ultrasound placental remodeling in patients with a history of SARS-CoV-2 infection during pregnancy. Materials and Methods: This was a longitudinal, cohort study which enrolled 23 pregnant women with a history of former mild SARS-CoV-2 infection during the current pregnancy. Four obstetricians analyzed placental ultrasound images from different gestational ages following COVID infection and identified the presence and degree of remodeling. We assessed the inter-rater agreement and the interclass correlation coefficients. Pathology workup included placental biometry, macroscopic and microscopic examination. Results: Serial ultrasound evaluation of the placental morphology revealed a progressive pattern of placental remodeling starting from 30-32 weeks of gestation towards term, occurring approximately 8-10 weeks after the SARS-CoV-2 infection. Placental changes-the "starry sky" appearance and the "white line" along the basal plate-were identified in all cases. Most placentas presented normal subchorionic perivillous fibrin depositions and focal stem villi perivillous fibrin deposits. Focal calcifications were described in only 13% of the cases. Conclusions: We identified two ultrasound signs of placental remodeling as potential markers of placental viral shedding following mild SARS-CoV-2. The most likely pathology correspondence for the imaging aspect is perivillous and, respectively, massive subchorionic fibrin deposits identified in most cases.

Automated placental abruption identification using semantic segmentation, quantitative features, SVM, ensemble and multi-path CNN

The placenta is a fundamental organ throughout the pregnancy and the fetus’ health is closely related to its proper function. Because of the importance of the placenta, any suspicious placental conditions require ultrasound image investigation. We propose an automated method for processing fetal ultrasonography images to identify placental abruption using machine learning methods in this paper. The placental imaging characteristics are used as the semantic identifiers of the region of the placenta compared with the amniotic fluid and hard organs. The quantitative feature extraction is applied to the automatically identified placental regions to assign a vector of optical features to each ultrasonographic image. In the first classification step, two methods of kernel-based Support Vector Machine (SVM) and decision tree Ensemble classifier are elaborated and compared for identification of the abruption cases and controls. The Recursive Feature Elimination (RFE) is applied for optimizing the feature vector elements for the best performance of each classifier. In the second step, the deep learning classifiers of multi-path ResNet-50 and Inception-V3 are used in combination with RFE. The resulting performances of the algorithms are compared together to reveal the best classification method for the identification of the abruption status. The best results were achieved for optimized ResNet-50 with an accuracy of 82.88% ± SD 1.42% in the identification of placental abruption on the testing dataset. These results show it is possible to construct an automated analysis method with affordable performance for the detection of placental abruption based on ultrasound images.

Association of intraplacental oxygenation patterns on dual-contrast MRI with placental abnormality and fetal brain oxygenation.

Most human in-vivo placental imaging techniques are unable to distinguish and characterize various placental compartments, such as the intervillous space (IVS), placental vessels (PV) and placental tissue (PT), limiting their specificity. We describe a method that employs T2* and diffusion-weighted magnetic resonance imaging (MRI) data to differentiate automatically placental compartments, quantify their oxygenation properties and identify placental lesions (PL) in vivo. We also investigate the association between placental oxygenation patterns and fetal brain oxygenation. This was a prospective study conducted between 2018 and 2021 in which dual-contrast clinical MRI data (T2* and diffusion-weighted MRI) were acquired from patients between 20 and 38 weeks' gestation. We trained a fuzzy clustering method to analyze T2* and diffusion-weighted MRI data and assign placental voxels to one of four clusters, based on their distinct imaging domain features. The new method divided automatically the placenta into IVS, PV, PT and PL compartments and characterized their oxygenation changes throughout pregnancy. A total of 27 patients were recruited, of whom five developed pregnancy complications. Total placental oxygenation level and T2* did not demonstrate a statistically significant temporal correlation with gestational age (GA) (R2 = 0.060, P = 0.27). In contrast, the oxygenation level reflected by T2* values in the placental IVS (R2 = 0.51, P = 0.0002) and PV (R2 = 0.76, P = 1.1 × 10-7 ) decreased significantly with advancing GA. Oxygenation levels in the PT did not show any temporal change during pregnancy (R2 = 0.00044, P = 0.93). A strong spatial-dependent correlation between PV oxygenation level and GA was observed. The strongest negative correlation between PV oxygenation and GA (R2 = 0.73, P = 4.5 × 10-7 ) was found at the fetal-vessel-dominated region close to the chorionic plate. The location and extent of the placental abnormality were automatically delineated and quantified in the five women with clinically confirmed placental pathology. Compared to the averaged total placental oxygenation, placental IVS oxygenation level best reflected fetal brain oxygenation level during fetal development. Based on clinically feasible dual-MRI, our method enables accurate spatiotemporal quantification of placental compartment and fetal brain oxygenation across different GAs. This information should improve our knowledge of human placenta development and its relationship with normal and abnormal pregnancy. © 2022 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Pregnancy characteristics of patients with placenta acreta spectrum at Prof. Dr. I.G.N.G. Ngoerah Hospital Denpasar, Bali-Indonesia during January 2020 - January 2022

Introduction: Placenta accreta spectrum is a problem during delivery when the placenta is not completely separated from the uterus and is followed by massive obstetric bleeding and can cause postnatal morbidity and mortality. The purpose of this study was to describe the pregnancy characteristics of patients with placenta accreta spectrum at Prof. Dr. I.G.N.G. Ngoerah Hospital Denpasar, Bali-Indonesia during period 1 January 2020 - 1 January 2022. Methods: This study is a descriptive study that describes the basic sociodemographic characteristics, ligation procedures, ultrasonographic findings, and histopathology. Results: A total of 48 patients were sampled in this study, the highest proportion of pregnant women with placenta accreta spectrum with lacuna grade II (39.6%), loss of basal hypoechoic zone (64.6%), myometrial thickness &lt;1 mm (39.6%), anterior placenta location (81.3%), regular vesicouterine border (75%), no exophytic mass (97.9%), bridging vessels (68.8%), no feeding vessels (85.4%), loss of placental basal Doppler image (97.9%), no hypervascularity of the vesicouterine area (66.7%), no lacunae vascular turbulence (83.3%) and the highest PAI score with a value of less than 5.37 (56.3%). Conclusion: The spectrum of placenta accreta was found with grade II lacunae, loss of basal hypoechoic zone, myometrial thickness &lt;1 mm, anterior placenta location, regular vesicouterine border, no exophytic mass, no bridging vessels, no feeding vessels, loss of placental basal Doppler image, there was no hypervascularity in the vesicouterine area, no vascular lacunae turbulence and the highest PAI score with a value of less than 5.37.

Diagnostic models for the detection of intrauterine growth restriction and placental insufficiency severity based on magnetic resonance imaging of the placenta.

We aimed to provide diagnostic models based on different parameters of placental magnetic resonance imaging (MRI) to detect intrauterine growth restriction (IUGR), as well as the severity of placental insufficiency. We included 44 foetuses with appropriate weight for gestational age (AGA) and 46 foetuses with documented IUGR, defined as the estimated foetal weight (EFW) below the 10th centile. Using Doppler ultrasound, IUGR cases were divided into 2 groups: 1) IUGR with severity signs: EFW < 3rd centile, or cerebroplacental ratio < 5th centile, or abnormal umbilical/uterine artery pulsatility index; and 2) non-severe IUGR without any of this criterion. For all these participants, placental MRI was performed in the third gestational trimester, and its parameters were compared between AGA and IUGR, as well as between the severe and non-severe IUGR groups. Two diagnostic models consisting of significant predictors were developed, and their performance was investigated with accuracy metrics. The severity signs were detected in 25 (54.3%) IUGR cases. The diagnostic model for the differentiation of IUGR from AGA revealed an acceptable performance (area under the curve [AUC] of 0.749) and consisted of 2 variables: 1) the largest size of infarct ≥ 25 mm (odds ratio [OR] = 5.01, p = 0.001), and 2) thickness : volume ratio ≥ 0.043 (OR = 3.76, p = 0.027); while, the logistic regression model for detection of the severity signs was even better, with AUC = 0.862, and comprised of 2 predictors: 1) placental infarct percent ≥ 10% (OR = 26.73, p = 0.004), and 2) placental globular shape (OR = 5.40, p = 0.034). Placental MRI parameters can differentiate IUGR from AGA, and more precisely, assess the severity of placental insufficiency in IUGR foetuses.

Placental Morphology and Morphometry: Is It a Prerequisite for Future Pathological Investigations?

Successful pregnancy is dependent on implantation, nutrient and gas exchange, as well as fetal protection from the immunologic attack. Placental pathologies and preterm delivery closely correlate with the size and shape of the placenta. Additionally, normal vaginal microbiota is disturbed during viral insults such as SARS-CoV-2 and HIV, with consequent placental anomalies. This chapter focuses on placental development, morphology, and pathology while also investigating placental bed structure and function. Placental anomalies with regard to HIV-1 and SARS-CoV-2 infection and placental morphometric image analysis and its relevance for verification of placental pathology are explored. Since image analysis remains optional for routine diagnostic purposes, authentication of placental appraisal warrants the use of measurable predefined definitions. Immunohistochemical analyses of placental morphology and angiogenic, epithelial, and apoptotic mechanisms facilitate research into etiopathogenetic pathways involved in placental anomalies with a focus on discovering novel diagnostic foci. Thus, image analyses as an adjunct to complement pathological investigations are recommended.