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

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

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

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

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

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

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.

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

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

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

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

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

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

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

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

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

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

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

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

Pattern Recognition and Anomaly Detection in fetal morphology using Deep Learning and Statistical learning (PARADISE): protocol for the development of an intelligent decision support system using fetal morphology ultrasound scan to detect fetal congenital anomaly detection.

INTRODUCTION: Congenital anomalies are the most encountered cause of fetal death, infant mortality and morbidity. 7.9 million infants are born with congenital anomalies yearly. Early detection of congenital anomalies facilitates life-saving treatments and stops the progression of disabilities. Congenital anomalies can be diagnosed prenatally through morphology scans. A correct interpretation of the morphology scan allows a detailed discussion with the parents regarding the prognosis. The central feature of this project is the development of a specialised intelligent system that uses two-dimensional ultrasound movies obtained during the standard second trimester morphology scan to identify congenital anomalies in fetuses. METHODS AND ANALYSIS: The project focuses on three pillars: committee of deep learning and statistical learning algorithms, statistical analysis, and operational research through learning curves. The cross-sectional study is divided into a training phase where the system learns to detect congenital anomalies using fetal morphology ultrasound scan, and then it is tested on previously unseen scans. In the training phase, the intelligent system will learn to answer the following specific objectives: (a) the system will learn to guide the sonographer's probe for better acquisition; (b) the fetal planes will be automatically detected, measured and stored and (c) unusual findings will be signalled. During the testing phase, the system will automatically perform the above tasks on previously unseen videos.Pregnant patients in their second trimester admitted for their routine scan will be consecutively included in a 32-month study (4 May 2022-31 December 2024). The number of patients is 4000, enrolled by 10 doctors/sonographers. We will develop an intelligent system that uses multiple artificial intelligence algorithms that interact between themselves, in bulk or individual. For each anatomical part, there will be an algorithm in charge of detecting it, followed by another algorithm that will detect whether anomalies are present or not. The sonographers will validate the findings at each intermediate step. ETHICS AND DISSEMINATION: All protocols and the informed consent form comply with the Health Ministry and professional society ethics guidelines. The University of Craiova Ethics Committee has approved this study protocol as well as the Romanian Ministry of Research Innovation and Digitization that funded this research. The study will be implemented and reported in line with the STROBE (STrengthening the Reporting of OBservational studies in Epidemiology) statement. TRIAL REGISTRATION NUMBER: The study is registered under the name 'Pattern recognition and Anomaly Detection in fetal morphology using Deep Learning and Statistical Learning', project number 101PCE/2022, project code PN-III-P4-PCE-2021-0057. TRIAL REGISTRATION: ClinicalTrials.gov, unique identifying number NCT05738954, date of registration: 2 November 2023.

Artificial intelligence assistance for fetal development: evaluation of an automated software for biometry measurements in the mid-trimester.

BACKGROUND: This study presents CUPID, an advanced automated measurement software based on Artificial Intelligence (AI), designed to evaluate nine fetal biometric parameters in the mid-trimester. Our primary objective was to assess and compare the CUPID performance of experienced senior and junior radiologists. MATERIALS AND METHODS: This prospective cross-sectional study was conducted at Shenzhen University General Hospital between September 2022 and June 2023, and focused on mid-trimester fetuses. All ultrasound images of the six standard planes, that enabled the evaluation of nine biometric measurements, were included to compare the performance of CUPID through subjective and objective assessments. RESULTS: There were 642 fetuses with a mean (±SD) age of 22 ± 2.82 weeks at enrollment. In the subjective quality assessment, out of 642 images representing nine biometric measurements, 617-635 images (90.65-96.11%) of CUPID caliper placements were determined to be accurately placed and did not require any adjustments. Whereas, for the junior category, 447-691 images (69.63-92.06%) were determined to be accurately placed and did not require any adjustments. In the objective measurement indicators, across all nine biometric parameters and estimated fetal weight (EFW), the intra-class correlation coefficients (ICC) (0.843-0.990) and Pearson correlation coefficients (PCC) (0.765-0.978) between the senior radiologist and CUPID reflected good reliability compared with the ICC (0.306-0.937) and PCC (0.566-0.947) between the senior and junior radiologists. Additionally, the mean absolute error (MAE), percentage error (PE), and average error in days of gestation were lower between the senior and CUPID compared to the difference between the senior and junior radiologists. The specific differences are as follows: MAE (0.36-2.53 mm, 14.67 g) compared to (0.64- 8.13 mm, 38.05 g), PE (0.94-9.38%) compared to (1.58-16.04%), and average error in days (3.99-7.92 days) compared to (4.35-11.06 days). In the time-consuming task, CUPID only takes 0.05-0.07 s to measure nine biometric parameters, while senior and junior radiologists require 4.79-11.68 s and 4.95-13.44 s, respectively. CONCLUSIONS: CUPID has proven to be highly accurate and efficient software for automatically measuring fetal biometry, gestational age, and fetal weight, providing a precise and fast tool for assessing fetal growth and development.

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

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

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

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

Identification of copy number variations among fetuses with isolated ultrasound soft markers in pregnant women not of advanced maternal age.

BACKGROUND: Pathogenic (P) copy number variants (CNVs) may be associated with second-trimester ultrasound soft markers (USMs), and noninvasive prenatal screening (NIPS) can enable interrogate the entire fetal genome to screening of fetal CNVs. This study evaluated the clinical application of NIPS for detecting CNVs among fetuses with USMs in pregnant women not of advanced maternal age (AMA). RESULTS: Fetal aneuploidies and CNVs were identified in 6647 pregnant women using the Berry Genomics NIPS algorithm.Those with positive NIPS results underwent amniocentesis for prenatal diagnosis. The NIPS and prenatal diagnosis results were analyzed and compared among different USMs. A total of 96 pregnancies were scored positive for fetal chromosome anomalies, comprising 37 aneuploidies and 59 CNVs. Positive predictive values (PPVs) for trisomy 21, trisomy 18, trisomy 13, and sex chromosome aneuploidies were 66.67%, 80.00%, 0%, and 30.43%, respectively. NIPS sensitivity for aneuploidies was 100%. For CNVs, the PPVs were calculated as 35.59% and false positive rate of 0.57%. There were six P CNVs, two successfully identified by NIPS and four missed, of which three were below the NIPS resolution limit and one false negative. The incidence of aneuploidies was significantly higher in fetuses with absent or hypoplastic nasal bone, while that of P CNVs was significantly higher in fetuses with aberrant right subclavian artery (ARSA), compared with other groups. CONCLUSIONS: NIPS yielded a moderate PPV for CNVs in non-AMA pregnant women with fetal USM. However, NIPS showed limited ability in identifying P CNVs. Positive NIPS results for CNVs emphasize the need for further prenatal diagnosis. We do not recommend the use of NIPS for CNVs screening in non-AMA pregnant women with fetal USM, especially in fetuses with ARSA.

Fetal maceration and partial retention of fetal bones in 2 mares.

Pregnancy loss after Day 40 in mares usually results in the expulsion (abortion) of the fetus and placental membranes. However, fetal retention within the uterus is also a possible outcome, leading to either fetal mummification or maceration. Fetal maceration is septic decomposition of fetal tissues within the uterus following failure of expulsion. It requires the presence of bacteria and oxygen within the uterus, likely originating from an open cervix, and results in tissue autolysis, leaving only fetal bones remaining in the mare. Fetal maceration is a rare complication of pregnancy in mares that is usually associated with a recent history of abortion, a persistent vaginal discharge, and retention of numerous fetal bones. Here, we report 2 cases of fetal maceration with retention of only a few fetal bones in mares that were presented without noticeable clinical signs. Key clinical message: The unusual presentation of fetal maceration in these mares (only a few fetal bones and no noticeable clinical signs) brings attention to the potential insidious nature of fetal retention. It highlights the importance of a thorough reproductive examination before breeding, along with careful and ongoing monitoring after breeding and throughout pregnancy.

Macération fœtale et rétention partielle d'os fœtaux chez 2 juments. L'interruption de gestation après le Jour 40 chez les juments résulte généralement par l'expulsion (avortement) du fœtus et des membranes fœtales. Toutefois, une rétention fœtale dans l'utérus est également un résultat possible, entraînant soit une momification ou une macération fœtale. La macération fœtale est la décomposition septique des tissus fœtaux à l'intérieur de l'utérus à la suite d'un échec d'expulsion. Elle nécessite la présence de bactéries et d'oxygène dans l'utérus, résultant probablement d'une ouverture du cervix, et résulte en une autolyse des tissus, laissant uniquement des os fœtaux à l'intérieur de la jument. La macération fœtale est une complication rare de la gestation chez les juments qui est généralement associée avec une histoire récente d'avortement, une décharge vaginale persistante, et la rétention de nombreux os fœtaux. Nous rapportons ici 2 cas de macération fœtale avec rétention de seulement quelques os chez des juments présentées avec aucun signe clinique notable.Message clinique clé :La présentation inhabituelle de macération fœtale chez ces juments (uniquement quelques os fœtaux et aucun signe clinque notable) met en lumière la nature potentiellement insidieuse de la rétention fœtale. Elle souligne l'importance d'un examen reproducteur complet avant l'accouplement, avec un suivi minutieux et continu après l'accouplement et durant toute la gestation.(Traduit par Dr Serge Messier).

Lifetime attributable risks (LARs) of cancer in the fetus associated with maternal radiography examinations.

PURPOSE: For various reasons, pregnant women are occasionally exposed to ionizing radiation during radiology examinations. In these situations, it is essential to determine the radiation dose to the fetus and any associated risks. The present study attempts to calculate the mean dose for the fetus to estimate the possible cancer induction and cancer mortality risks resulting from maternal radiography exams. MATERIAL AND METHODS: The GATE Monte Carlo platform and a standard voxelized pregnant phantom were employed to calculate fetal radiation dose during maternal radiography exams. The data published in Biological Effects of Ionizing Radiation VII were used to convert fetal dose to lifetime attributable risks (LARs) of cancer incidence and cancer-related mortality. RESULTS: The fetal doses and LARs of cancer incidence and cancer-related mortality for the radiographs of the chest and skull were negligible. The maximum LAR values for the lateral view of the abdomen in computed and digital radiography are 5598.29 and 2238.95 per 100,000 individuals, respectively. The computed radiography of the lateral view of the abdomen revealed the highest LAR of cancer-related mortality (2074.30 deaths for every 100,000 people). CONCLUSION: The radiation dose incurred by the fetus due to chest and skull radiographs was minimal and unlikely to cause any abnormalities in the fetus. The discernible elevation in the lifetime attributable risk associated with cancer incidence and mortality arising from lateral computed radiography examinations of the abdomen warrants careful consideration within the realm of maternal radiography examinations.

Genotype-phenotype correlation study of structural abnormalities in a fetal brain caused by a novel KDM4B variant.

BACKGROUND: Fetal ventriculomegaly (VM), a common brain structure malformation detected during prenatal ultrasound diagnosis, is associated with an increased risk of neurodevelopmental disorders (NDDs) after birth. KDM4B encodes a lysine-specific demethylase that interacts with histone H3K23me3. Variations in KDM4B are reportedly associated with human NDDs; however, only 11 such patients have been reported. Herein, we report a fetus with VM and agenesis of the corpus callosum (ACC), which suggests that KDM4B plays an important role in fetal brain development. METHODS: Fetal skin tissue and parental peripheral venous blood samples were collected. Whole-exome and Sanger sequencing were performed to analyze fetal germline variants. Human 293T cells transfected with wild-type or mutant KDM4B were used for western blotting (WB) to analyze protein expression levels. RESULTS: An insertion variant of KDM4B, NM_015015.3: c.2889_2890insGAGAGCATCACGGTGAGCTGTGGGGTGGGGCAGGGGGCGGGGGGAGGCTGGGAGCACAGTGACAACCTGTACCCC, was identified in the fetal tissue; however, the parents carried the wild-type gene. The WB results indicated significantly reduced expression of the mutant protein, likely owing to decreased stability. CONCLUSIONS: The structural abnormalities in the brain of the studied fetus may be attributed to an insertion variant of KDM4B. This study highlights the importance of screening for KDM4B variants and considering potential copy number variations when observing VM or ACC in prenatal ultrasound imaging.

A Dempster-Shafer Approach to Trustworthy AI With Application to Fetal Brain MRI Segmentation.

Deep learning models for medical image segmentation can fail unexpectedly and spectacularly for pathological cases and images acquired at different centers than training images, with labeling errors that violate expert knowledge. Such errors undermine the trustworthiness of deep learning models for medical image segmentation. Mechanisms for detecting and correcting such failures are essential for safely translating this technology into clinics and are likely to be a requirement of future regulations on artificial intelligence (AI). In this work, we propose a trustworthy AI theoretical framework and a practical system that can augment any backbone AI system using a fallback method and a fail-safe mechanism based on Dempster-Shafer theory. Our approach relies on an actionable definition of trustworthy AI. Our method automatically discards the voxel-level labeling predicted by the backbone AI that violate expert knowledge and relies on a fallback for those voxels. We demonstrate the effectiveness of the proposed trustworthy AI approach on the largest reported annotated dataset of fetal MRI consisting of 540 manually annotated fetal brain 3D T2w MRIs from 13 centers. Our trustworthy AI method improves the robustness of four backbone AI models for fetal brain MRIs acquired across various centers and for fetuses with various brain abnormalities.

Sodium Channel Gene Variants in Fetuses with Abnormal Sonographic Findings: Expanding the Prenatal Phenotypic Spectrum of Sodium Channelopathies.

Voltage-gated sodium channels (VGSCs) are responsible for the initiation and propagation of action potentials in the brain and muscle. Pathogenic variants in genes encoding VGSCs have been associated with severe disorders including epileptic encephalopathies and congenital myopathies. In this study, we identified pathogenic variants in genes encoding the α subunit of VGSCs in the fetuses of two unrelated families with the use of trio-based whole exome sequencing, as part of a larger cohort study. Sanger sequencing was performed for variant confirmation as well as parental phasing. The fetus of the first family carried a known de novo heterozygous missense variant in the SCN2A gene (NM_001040143.2:c.751G>A p.(Val251Ile)) and presented intrauterine growth retardation, hand clenching and ventriculomegaly. Neonatally, the proband also exhibited refractory epilepsy, spasms and MRI abnormalities. The fetus of the second family was a compound heterozygote for two parentally inherited novel missense variants in the SCN4A gene (NM_000334.4:c.4340T>C, p.(Phe1447Ser), NM_000334.4:c.3798G>C, p.(Glu1266Asp)) and presented a severe prenatal phenotype including talipes, fetal hypokinesia, hypoplastic lungs, polyhydramnios, ear abnormalities and others. Both probands died soon after birth. In a subsequent pregnancy of the latter family, the fetus was also a compound heterozygote for the same parentally inherited variants. This pregnancy was terminated due to multiple ultrasound abnormalities similar to the first pregnancy. Our results suggest a potentially crucial role of the VGSC gene family in fetal development and early lethality.