Neonatal hearing screening: modelling cost and effectiveness of hospital- and community-based screening

This is the final version of the article. Available from the publisher via the DOI in this record.BACKGROUND: Children with congenital hearing impairment benefit from early detection and management of their hearing loss. These and related considerations led to the recommendation of universal newborn hearing screening. In 2001 the first phase of a national Newborn Hearing Screening Programme (NHSP) was implemented in England. Objective of this study was to assess costs and effectiveness for hospital and community-based newborn hearing screening systems in England based on data from this first phase with regard to the effects of alterations to parameter values. METHODS: DESIGN: Clinical effectiveness analysis using a Markov Model. OUTCOME MEASURE: quality weighted detected child months (QCM). RESULTS: Both hospital and community programmes yielded 794 QCM at the age of 6 months with total costs of 3,690,000 pound sterling per 100,000 screened children in hospital and 3,340,000 pound sterling in community. Simulated costs would be lower in hospital in 48% of the trials. Any statistically significant difference between hospital and community in prevalence, test sensitivity, test specificity and costs would result in significant differences in cost-effectiveness between hospital and community. CONCLUSION: This modelling exercise informs decision makers by a quantitative projection of available data and the explicit and transparent statements about assumptions and the degree of uncertainty. Further evaluation of the cost-effectiveness should focus on the potential differences in test parameters and prevalence in these two settings.This work was undertaken by the Evaluation Team for the Implementation Pilot of Newborn Hearing Screening in England which received funding from the Department of Health

Multi-task learning for joint weakly-supervised segmentation and aortic arch anomaly classification in fetal cardiac MRI

Congenital Heart Disease (CHD) is a group of cardiac malformations present already during fetal life, representing the prevailing category of birth defects globally. Our aim in this study is to aid 3D fetal vessel topology visualisation in aortic arch anomalies, a group which encompasses a range of conditions with significant anatomical heterogeneity. We present a multi-task framework for automated multi-class fetal vessel segmentation from 3D black blood T2w MRI and anomaly classification. Our training data consists of binary manual segmentation masks of the cardiac vessels' region in individual subjects and fully-labelled anomaly-specific population atlases. Our framework combines deep learning label propagation using VoxelMorph with 3D Attention U-Net segmentation and DenseNet121 anomaly classification. We target 11 cardiac vessels and three distinct aortic arch anomalies, including double aortic arch, right aortic arch, and suspected coarctation of the aorta. We incorporate an anomaly classifier into our segmentation pipeline, delivering a multi-task framework with the primary motivation of correcting topological inaccuracies of the segmentation. The hypothesis is that the multi-task approach will encourage the segmenter network to learn anomaly-specific features. As a secondary motivation, an automated diagnosis tool may have the potential to enhance diagnostic confidence in a decision support setting. Our results showcase that our proposed training strategy significantly outperforms label propagation and a network trained exclusively on propagated labels. Our classifier outperforms a classifier trained exclusively on T2w volume images, with an average balanced accuracy of 0.99 (0.01) after joint training. Adding a classifier improves the anatomical and topological accuracy of all correctly classified double aortic arch subjects.Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2023:01

Dynamics of T2* and deformation in the placenta and myometrium during pre-labour contractions

Pre-labour uterine contractions, occurring throughout pregnancy, are an important phenomenon involving the placenta in addition to the myometrium. They alter the uterine environment and thus potentially the blood supply to the fetus and may thus provide crucial insights into the processes of labour. Assessment in-vivo is however restricted due to their unpredictability and the inaccessible nature of the utero-placental compartment. While clinical cardiotocography (CTG) only allows global, pressure-based assessment, functional magnetic resonance imaging (MRI) provides an opportunity to study contractile activity and its effects on the placenta and the fetus in-vivo. This study aims to provide both descriptive and quantitative structural and functional MR assessments of pre-labour contractions in the human uterus. A total of 226 MRI scans (18–41 weeks gestation) from ongoing research studies were analysed, focusing on free-breathing dynamic quantitative whole uterus dynamic T2* maps. These provide an indirect measure of tissue properties such as oxygenation. 22 contractile events were noted visually and both descriptive and quantitative analysis of the myometrial and placental changes including volumetric and T2* variations were undertaken. Processing and analysis was successfully performed, qualitative analysis shows distinct and highly dynamic contraction related characteristics including; alterations in the thickness of the low T2* in the placental bed and other myometrial areas, high intensity vessel-like structures in the myometrium, low-intensity vessel structures within the placental parenchyma and close to the chorionic plate. Quantitative evaluation shows a significant negative correlation between T2* in both contractile and not-contractile regions with gestational age (p 0.5). The quantitative and qualitative description of uterine pre-labour contractions including dynamic changes and key characteristics aims to contribute to the sparsely available in-vivo information and to provide an in-vivo tool to study this important phenomenon. Further work is required to analyse the origins of these subclinical contractions, their effects in high-risk pregnancies and their ability to determine the likelihood of a successful labour. Assessing T2* distribution as a marker for placental oxygenation could thus potentially complement clinically used cardiotocography measurements in the future

Incidence and clinical value of prolonged I–V interval in NICU infants after failing neonatal hearing screening

Infants admitted to neonatal intensive care units (NICUs) have a higher incidence of perinatal complications and delayed maturational processes. Parameters of the auditory brainstem response (ABR) were analyzed to study the prevalence of delayed auditory maturation or neural pathology. The prevalence of prolonged I–V interval as a measure of delayed maturation and the correlation with ABR thresholds were investigated. All infants admitted to the NICU Sophia Children’s Hospital between 2004 and 2009 who had been referred for ABR measurement after failing neonatal hearing screening with automated auditory brainstem response (AABR) were included. The ABR parameters were retrospectively analyzed. Between 2004 and 2009, 103 infants were included: 46 girls and 57 boys. In 58.3% (60 infants) of our population, the I–V interval was recordable in at least one ear at first diagnostic ABR measurement. In 4.9%, the I–V interval was severely prolonged. The median ABR threshold of infants with a normal or mildly prolonged I–V interval was 50 dB. The median ABR threshold of infants with a severely prolonged I–V interval was 30 dB. In conclusion, in case both peak I and V were measurable, we found only a limited (4.9%) incidence of severely prolonged I–V interval (≥0.8 ms) in this high-risk NICU population. A mild delay in maturation is a more probable explanation than major audiologic or neural pathology, as ABR thresholds were near normal in these infants

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

Management of the ataxias towards best clinical practice

This document aims to provide recommendations for healthcare professionals on the diagnosis and management of people with progressive ataxia. The progressive ataxias are rare neurological conditions, and are often poorly understood by healthcare professionals. Diagnosis has generally been a long process because of the rarity and complexity of the different ataxias1. In addition, many healthcare professionals are unsure how best to manage the conditions and there is sometimes a feeling that little can be done for these patients1,2 Although there are no disease-modifying treatments for the majority of the progressive ataxias, there are many aspects of the conditions that are treatable and it is thus important that this is recognised by the relevant healthcare professionals. The diagnosis and management of the few treatable causes is also of paramount importance. All this highlights the importance of producing these guidelines: in order to increase awareness and understanding of these conditions, and lead to their improved diagnosis and management. With new developments in genetic technologies and the discovery of more genes, diagnosis is improving and has great scope to continue to do so. In addition, research is advancing and many human trials to test medications are taking place, making us more optimistic that disease-modifying treatments will be found for the progressive ataxias. AB - This document aims to provide recommendations for healthcare professionals on the diagnosis and management of people with progressive ataxia. The progressive ataxias are rare neurological conditions, and are often poorly understood by healthcare professionals. Diagnosis has generally been a long process because of the rarity and complexity of the different ataxias1. In addition, many healthcare professionals are unsure how best to manage the conditions and there is sometimes a feeling that little can be done for these patients1,2 Although there are no disease-modifying treatments for the majority of the progressive ataxias, there are many aspects of the conditions that are treatable and it is thus important that this is recognised by the relevant healthcare professionals. The diagnosis and management of the few treatable causes is also of paramount importance. All this highlights the importance of producing these guidelines: in order to increase awareness and understanding of these conditions, and lead to their improved diagnosis and management. With new developments in genetic technologies and the discovery of more genes, diagnosis is improving and has great scope to continue to do so. In addition, research is advancing and many human trials to test medications are taking place, making us more optimistic that disease-modifying treatments will be found for the progressive ataxias

Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results

Segmentation is a critical step in analyzing the developing human fetal brain. There have been vast improvements in automatic segmentation methods in the past several years, and the Fetal Brain Tissue Annotation (FeTA) Challenge 2021 helped to establish an excellent standard of fetal brain segmentation. However, FeTA 2021 was a single center study, and the generalizability of algorithms across different imaging centers remains unsolved, limiting real-world clinical applicability. The multi-center FeTA Challenge 2022 focuses on advancing the generalizability of fetal brain segmentation algorithms for magnetic resonance imaging (MRI). In FeTA 2022, the training dataset contained images and corresponding manually annotated multi-class labels from two imaging centers, and the testing data contained images from these two imaging centers as well as two additional unseen centers. The data from different centers varied in many aspects, including scanners used, imaging parameters, and fetal brain super-resolution algorithms applied. 16 teams participated in the challenge, and 17 algorithms were evaluated. Here, a detailed overview and analysis of the challenge results are provided, focusing on the generalizability of the submissions. Both in- and out of domain, the white matter and ventricles were segmented with the highest accuracy, while the most challenging structure remains the cerebral cortex due to anatomical complexity. The FeTA Challenge 2022 was able to successfully evaluate and advance generalizability of multi-class fetal brain tissue segmentation algorithms for MRI and it continues to benchmark new algorithms. The resulting new methods contribute to improving the analysis of brain development in utero.Comment: Results from FeTA Challenge 2022, held at MICCAI; Manuscript submitted. Supplementary Info (including submission methods descriptions) available here: https://zenodo.org/records/1062864