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Background: Antenatally detected occipital encephalocele and polycystic kidneys are a common presentation of ciliopathies like Joubert syndrome and Meckel Gruber syndrome which have considerable genetic and phenotypic overlap. Case reports: We describe 3 cases of antenatally diagnosed occipital encephalocele and enlarged kidneys with fetal autopsy, histopathology & exome sequencing results. A novel nonsense variant in the CEP290 gene was reported in first case (Meckel syndrome). The second case shows the importance of fetal exome where the parents were carriers for 2 ciliopathy genes (TMEM138 & SDCCAG8). Diagnosis in this case was confirmed by fetal exome sequencing (Joubert syndrome). Multiexon deletion in TMEM67 and KIF14 present in trans was identified in the third case (Meckel syndrome), likely resulting in digenic inheritance. Conclusion: We report 2 cases of Meckel syndrome with a novel variant and multiexon deletion, and 1 case of Joubert syndrome which depicts the limitations of preconceptional carrier screening in ciliopathies due to overlapping phenotypes. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) makes a substantial contribution to decision-making in many intricate application areas of the medical sciences. One such application is organ classification in maternal–fetal ultrasound scans using sophisticated AI methodology like deep neural networks for better analysis. In this paper, we present a novel and highly dense deep neural architecture specifically designed for the multi-organ classification of fetal ultrasound scans. Our proposed approach introduces a unique combination of densely connected layers, convolution layers, and skip connections, tailored to accurately identify and classify multiple fetal organs simultaneously. To the best of our knowledge, this is the first study to address the comprehensive classification of multiple organs in fetal ultrasound images using such a highly dense neural network. The architecture is designed to capture both local and global features, critical for distinguishing intricate organ structures in ultrasound images. The model also minimizes the gradient loss for faster convergence of the model parameters in the training phase. Through extensive evaluation of a curated dataset of fetal ultrasound scans, we demonstrate that our novel architecture achieves superior classification accuracy—96.85%, precision—97.12%, recall—96.66%, F1-score—96.88%, and AUC-ROC score—97.27%, outperforming state-of-the-art methods in the context of multi-organ classification. Thus, the proposed highly dense deep neural architecture presents a promising avenue for enhancing fetal ultrasound imaging, bringing potential benefits to prenatal care, and contributing to improved neonatal outcomes. [ABSTRACT FROM AUTHOR]

Ultrasound imaging is the most commonly used imaging during pregnancy for tracking the fetus's growth and monitoring other biological parameters. The assessment of the development of the baby's growth requires imaging-based analysis in every trimester. The automatic computerized software and systems provide the platform for radiologists to more accurately access the fetus's head circumference as compared to manual estimation. The improvement of such computerized algorithms is always the key demand to improve accuracy and precision. This paper proposes an improved encoder-decoder model for the segmentation of the fetal head segmentation in 2D-ultrasound images. The proposed model uses regression in combination with attention to the encoder-decoder model to determine the fetus's head circumference. The model is further extended with the post-processing ellipse fitting to superimpose the segmentation region on ultrasound images for clear visualization of the fetus's head. Further, the proposed model performance is evaluated by using various statistical measures using segmented regions and available ground truth. The experimental results demonstrate a similarity score of 94.56%. The comparative result suggests that the proposed model is providing a more accurate fetus head segmentation region on 2D-ultrasound images as compared to other existing approaches. • Presents a new Attention layer encoded Encoder-Decoder model for Fetus segmentation. • Introduced a Regression layer in the decoding phase of the Encoder-Decoder model for loss minimization. • Presents an automated approach that predicts the bounding box coordinates of the fetal head. • The results are evaluated on various statistical measures for performance analysis. [ABSTRACT FROM AUTHOR]

Microcephaly is a frequent feature of neurodevelopmental disorders (NDDs). Our study presents the heterogeneous spectrum of genetic disorders in patients with microcephaly either in isolated form or in association with other neurological and extra-neural abnormalities. We present data of 91 patients from 87 unrelated families referred to our clinic during 2016–2020 and provide a comprehensive clinical and genetic landscape in the studied cohort. Molecular diagnosis using exome sequencing was made in 45 families giving a yield of 51.7%. In 9 additional families probable causative variants were detected. We identified disease causing variations in 49 genes that are involved in different functional pathways Among these, 36 had an autosomal recessive pattern, 8 had an autosomal dominant pattern (all inherited de novo), and 5 had an X-linked pattern. In 41 probands where sequence variations in autosomal recessive genes were identified 31 were homozygotes (including 16 from non-consanguineous families). The study added 28 novel pathogenic/likely pathogenic variations. The study also calls attention to phenotypic variability and expansion in spectrum as well as uncovers genes where microcephaly is not reported previously or is a rare finding. We here report phenotypes associated with the genes for ultra-rare NDDs with microcephaly namely ATRIP , MINPP1 , PNPLA8 , AIMP2 , ANKLE2 , NCAPD2 and TRIT1. [ABSTRACT FROM AUTHOR]