Down Syndrome

Down syndrome, the most cutting-edge book in the field congenital disorders. This book features up-to-date, well referenced research and review articles on Down syndrome. Research workers, scientists, medical graduates and pediatricians will find it to be an excellent source for references and review. It is hoped that such individuals will view this book as a resource that can be consulted during all stages of their research and clinical investigations. Key features of this book are: Common diseases in Down syndrome Molecular Genetics Neurological Disorders Prenatal Diagnosis and Genetic Counselling Whilst aimed primarily at research workers on Down syndrome, we hope that the appeal of this book will extend beyond the narrow confines of academic interest and be of interest to a wider audience, especially parents, relatives and health-care providers who work with infants and children with Down syndrome

Endothelial plasticity in cardiovascular development : role of growth factors VEGF and PDGF

The central cell type within vascular development is the endothelial cell (EC). It forms during (lymph)vasculogenesis, proliferates during angiogenesis and instructs medial cells during arteriogenesis. The venous population also gives rise to a subset of the lymphatic endothelium and the endocardium is instructive in formation of the primitive heart. We show that endothelial plasticity is very high in the developing embryo/fetus and that its outcome is dependent on the VEGF, Notch and PDGF-signaling pathways. Alterations in VEGF and Notch-signaling abrogate endocardial and endothelial differentiation, cardiac development and coronary maturation. Alterations in these pathways are most likely also involved in abnormal lymphatic development as seen in fetuses with increased nuchal translucency. In this thesis, lymphatic endothelial plasticity is particularly underscored, as lymphatic ECs gain arterial characteristics in certain pathological situations. Additionally, we show that impaired VEGF, Notch and PDGF-B/PDGFR-_-signaling in ECs and/or vSMCs severely impairs coronary arteriogenesis. In conclusion, many growth factors either influencing the EC (such as VEGF) or produced by the EC (such as PDGF) play a role in regulating and fine-tuning these processes. Increasing our knowledge on how these factors influence (ab)normal vascular development will improve our understanding of many pathological conditions and might increase therapeutic approaches.The work presented in this thesis was supported by a grant of the Netherlands Heart Foundation (2001B057)UBL - phd migration 201

Genetics and Etiology of Down Syndrome

This book provides a concise yet comprehensive source of current information on Down syndrome. Research workers, scientists, medical graduates and paediatricians will find it an excellent source for reference and review. This book has been divided into four sections, beginning with the Genetics and Etiology and ending with Prenatal Diagnosis and Screening. Inside, you will find state-of-the-art information on: 1. Genetics and Etiology 2. Down syndrome Model 3. Neurologic, Urologic, Dental & Allergic disorders 4. Prenatal Diagnosis and Screening Whilst aimed primarily at research workers on Down syndrome, we hope that the appeal of this book will extend beyond the narrow confines of academic interest and be of interest to a wider audience, especially parents and relatives of Down syndrome patients

UWOMJ Volume 78, Issue 3

Schulich School of Medicine & Dentistryhttps://ir.lib.uwo.ca/uwomj/1017/thumbnail.jp

Imaging fetal anatomy.

Due to advancements in ultrasound techniques, the focus of antenatal ultrasound screening is moving towards the first trimester of pregnancy. The early first trimester however remains in part, a 'black box', due to the size of the developing embryo and the limitations of contemporary scanning techniques. Therefore there is a need for images of early anatomical developmental to improve our understanding of this area. By using new imaging techniques, we can not only obtain better images to further our knowledge of early embryonic development, but clear images of embryonic and fetal development can also be used in training for e.g. sonographers and fetal surgeons, or to educate parents expecting a child with a fetal anomaly. The aim of this review is to provide an overview of the past, present and future techniques used to capture images of the developing human embryo and fetus and provide the reader newest insights in upcoming and promising imaging techniques. The reader is taken from the earliest drawings of da Vinci, along the advancements in the fields of in utero ultrasound and MR imaging techniques towards high-resolution ex utero imaging using Micro-CT and ultra-high field MRI. Finally, a future perspective is given about the use of artificial intelligence in ultrasound and new potential imaging techniques such as synchrotron radiation-based CT to increase our knowledge regarding human development

Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes

The goal of this study was to evaluate the maturity of current Deep Learning classification techniques for their application in a real maternal-fetal clinical environment. A large dataset of routinely acquired maternal-fetal screening ultrasound images (which will be made publicly available) was collected from two different hospitals by several operators and ultrasound machines. All images were manually labeled by an expert maternal fetal clinician. Images were divided into 6 classes: four of the most widely used fetal anatomical planes (Abdomen, Brain, Femur and Thorax), the mother's cervix (widely used for prematurity screening) and a general category to include any other less common image plane. Fetal brain images were further categorized into the 3 most common fetal brain planes (Trans-thalamic, Trans-cerebellum, Trans-ventricular) to judge fine grain categorization performance. The final dataset is comprised of over 12,400 images from 1,792 patients, making it the largest ultrasound dataset to date. We then evaluated a wide variety of state-of-the-art deep Convolutional Neural Networks on this dataset and analyzed results in depth, comparing the computational models to research technicians, which are the ones currently performing the task daily. Results indicate for the first time that computational models have similar performance compared to humans when classifying common planes in human fetal examination. However, the dataset leaves the door open on future research to further improve results, especially on fine-grained plane categorization

A mixed method review and quality criteria analysis : towards improving decision aids and informing care models in prenatal testing

Introduction: Les incertitudes des pronostics cliniques et les dilemmes moraux associés aux technologies des tests prénataux affectent les expériences et les processus décisionnels des femmes et des couples. D’une part, la validité des normes relatives au ‘consentement autonome’ et au conseil ‘non directif’ est remise en question. D’autre part, les aides à la décision sont prônées pour rehausser la prise de décision éclairée. L’objectif de ce mémoire est de construire un modèle de l’expérience des femmes et des couples qui font face aux tests prénataux afin d’identifier les facteurs qui amélioreraient les expériences, la prise de décision et le rôle des aides à la décision et informeraient le modèle de soin. Méthodologie: La modélisation et l’analyse des expériences des femmes et des couples qui affrontent les tests prénataux reposent sur une méta-ethnographie des études qualitatives et sur une analyse narrative thématique des études quantitatives. La critique d’un outil (PT) en matière de tests prénataux est également effectuée en ayant recours aux critères de qualité de l’International Patient Decision Aid Standards (IPDAS). Résultats: Un cadre conceptuel décrivant les expériences vécues est construit et l’analyse thématique le complète en soulignant que la prise de décision n’est que rarement éclairée. Les normes d’une ‘décision autonome’ et d’un ‘conseil non directif’ sont problématiques pour les femmes. Les aides à la décision amélioraient les scores de connaissances, sans pour autant modifier la perception du risque, ni les niveaux d'anxiété. L’outil PT favorise une prise de décision basée sur les préférences, mais les critères IPDAS sont difficilement applicables et leur rôle dans une décision de qualité est incertain. Discussion et conclusion: Les résultats éclairent les facteurs macro, méso et micro pouvant améliorer les expériences vécues des femmes et des couples et affecter la prise de décision et l’utilisation des aides à la décision. Un changement de paradigme préconisant le concept d’autonomie relationnelle dans le modèle de soins est suggéré. Dans le contexte des avancées en matière de test prénataux, une réévaluation des normes de pratique et de modèles de soin est requise. Le rôle des aides à la décision devra être élucidé.Introduction: The clinical prognostic uncertainties and moral dilemmas associated with technological advances of prenatal testing impact the experiences and decision-making of women and couples. While the validity of the norms of ‘autonomous consent’ and ‘non-directive’ counseling is being questioned, decision aids are promoted to enhance informed decision-making. The goals of this thesis are to develop a model of the experiences of women and couples in prenatal testing so as to identify factors that may improve experiences, decision-making, the role of decision aids and inform the care model. Methods: A model of the experiences of prenatal testing is developed through a meta-ethnography of qualitative studies and a narrative synthesis of the themes explored in quantitative studies. A prenatal testing (PT) decision tool is critically assessed using the International Patient Decision Aids Standards (IPDAS) quality criteria for decision aids. Results: A conceptual framework of the experiences of women and couples in prenatal diagnosis is constructed and complemented by a narrative thematic analysis showing that decision-making is rarely informed and that the norms of an ‘autonomous decision’ and a ‘non-directive’ counselling are problematic for women. Decision aids improve knowledge scores, but do no modify risk perception or anxiety levels. A PT tool increases preference based informed decision-making, but quality criteria are not always applicable and their role in quality decision-making is unclear. Discussion and conclusion: The results highlight macro, meso and micro-level factors that may improve the experiences of women and couples and inform decision-making processes as well as the use of decision aids. A paradigm shift towards the concept of relational autonomy in the prenatal diagnosis model of care is suggested. Advances in prenatal testing require a re-evaluation of the norms of practice and care model. The role of decision aids requires further elucidation

Clinical risk modelling with machine learning: adverse outcomes of pregnancy

As a complex biological process, there are various health issues that are related to pregnancy. Prenatal care, a type of preventative healthcare at different points in gestation is comprised of management, treatment, and mitigation of such issues. This also includes risk prediction for adverse pregnancy outcomes, where probabilistic modelling is used to calculate individual’s risk at the early stages of pregnancy. This type of modelling can have a definite clinical scope such as in prenatal screening, and an educational aim where awareness of a healthy lifestyle is promoted, such as in health education. Currently, the most used models are based on traditional statistical approaches, as they provide sufficient predictive power and are easily interpreted by clinicians. Machine learning, a subfield of data science, contains methods for building probabilistic models with multidimensional data. Compared to existing prediction models related to prenatal care, machine learning models can provide better results by fitting more intricate nonlinear decision boundary areas, improve data-driven model fitting by generating synthetic data, and by providing more automation for routine model adjustment processes. This thesis presents the evaluation of machine learning methods to prenatal screening and health education prediction problems, along with novel methods for generating synthetic rare disorder data to be used for modelling, and an adaptive system for continuously adjusting a prediction model to the changing patient population. This way the thesis addresses all the four main entities related to predicting adverse outcomes of pregnancy: the mother or patient, the clinician, the screening laboratory and the developer or manufacturer of screening materials and systems.Kliinisen riskin mallinnus koneoppimismenetelmin: raskaudelle haitalliset lopputulemat Raskaus on kompleksinen biologinen prosessi, jonka etenemiseen liittyy useita terveysongelmia. Äitiyshoito voidaan kuvata ennalta ehkäiseväksi terveydenhuolloksi, jossa pyritään käsittelemään, hoitamaan ja lievittämään kyseisiä ongelmia. Tähän hoitoon sisältyy myös raskauden haitallisten lopputulemien riskilaskenta, missä probabilistista mallinnusta hyödynnetään määrittämään yksilön riski raskauden varhaisissa vaiheissa. Tällä mallinnuksella voi olla selkeä kliininen tarkoitus kuten prenataaliseulonta, tai terveyssivistyksellinen tarkoitus missä odottavalle äidille esitellään raskauden kannalta terveellisiä elämäntapoja. Tällä hetkellä eniten käytössä olevat ennustemallit perustuvat perinteiseen tilastolliseen mallinnukseen, sille ne tarjoavat riittävän ennustetehokkuuden ja ovat helposti tulkittavissa. Koneoppiminen on datatieteen osa-alue, joka pitää sisällään menetelmiä millä voidaan mallintaa moniulotteista dataa ennustekäyttöön. Verrattuna olemassa oleviin äitiyshoidon ennustemalleihin, koneoppiminen mahdollistaa parempien ennustetulosten tuottamisen sovittamalla hienojakoisempia epälineaarisia päätösalueita, tehostamalla datakeskeisten mallien sovitusta luomalla synteettisiä havaintoja ja tarjoamalla enemmän automaatiota rutiininomaiseen mallien hienosäätöön. Tämä väitös esittelee koneoppimismenetelmien evaluaation prenataaliseulonta-ja terveyssivistysongelmiin, ja uusia menetelmiä harvinaisten sairauksien datan luomiseen mallinnustarkoituksiin ja jatkuvan ennustemallin hienosäätämisen järjestelmän muuttuvia potilaspopulaatiota varten. Näin väitös käy läpi kaikki neljä asianomaista jotka liittyvät haitallisten lopputulemien ennustamiseen: odottava äiti eli potilas, kliinikko, seulontalaboratorio ja seulonnassa käytettävien materiaalien ja järjestelmien kehittäjä tai valmistaja

Segmentação de imagens fetais com potencial para desenvolvimento de ferramentas de apoio ao diagnóstico

Programa Doutoral em Engenharia Eletrónica e de ComputadoresDurante uma gravidez é aconselhável a realização de 3 exames ecográficos. O primeiro, e reconhecido pelos especialistas como mais importante, é o do primeiro trimestre. Neste exame, realizado entre as 11 e as 14 semanas, é possível avaliar a idade gestacional, o desenvolvimento fetal e, mais importante, as anomalias fetais. Na avaliação das anomalias fetais incluem-se as cromossómicas, que são detetáveis a partir da observação da medida da Translucência da Nuca mas que deve ser cruzada com a medida da Distância Crânio-Caudal e a idade materna. As medidas são retiradas manualmente e os seus valores variam com a disponibilidade física e a motivação do operador, pelo que os resultados mostram variabilidade intra e inter-operador. As imagens recolhidas pelos sistemas de aquisição baseados em ultrassons apresentam pouco detalhe, baixo contraste, baixa relação sinal/ruído e grande variabilidade morfológica que dificulta a tarefa de segmentação e, consequentemente, o desenvolvimento de sistemas de medição automáticos. Como tal, o seu tratamento exige a utilização de técnicas que reúnam características adequadas e que permitam o desenvolvimento de sistemas robustos. Este trabalho trata a questão da extração automática da medida da Distância Crânio-Caudal (DCC) a partir das imagens de ultrassons habitualmente usadas para este fim. Para tal, propõe a utilização de técnicas de Fuzzy Clustering, de Contornos Ativos e de Aprendizagem Máquina, nomeadamente SVMs, para a segmentação das imagens com vista à identificação do corpo do feto. Estas abordagens potenciaram a formulação de novos modelos que permitem enfrentar as dificuldades inerentes ao tratamento deste tipo de imagens. São também propostas metodologias automáticas de extração da medida DCC, sendo que algumas delas dependem dos processos de segmentação sugeridos. Os resultados obtidos para a medida da DCC apresentam um erro absoluto médio relativo dentro dos intervalos de variabilidade inter-operador referidos na literatura.During pregnancy it is advisable to conduct 3 ultrasound examinations. The first and most important is performed in the first trimester. In this exam, done between the 11th and 14th week, the gestational age, the fetal development and, most importantly, the fetal abnormalities can be assessed. The assessment of fetal anomalies include chromosomal, which are detectable from observation measuring the nuchal translucency size. However it should be crossed with a measure of the crown-rump length and the maternal age. These measures are manually performed and their values vary with the physical availability and motivation of the operator, so the results show intra and inter-operator variability. The images collected by acquisition systems based on ultrasounds have little detail, low contrast, low signal/noise ratio and great morphological variability which difficult the segmentation task and the development of automatic measuring systems. Because of these reasons, ultrasound image processing requires the use of techniques that meet appropriate characteristics and that enable the development of robust systems. This work treats the subject of automatic extraction of the crown-rump length from ultrasound images commonly used for this purpose. It uses Fuzzy Clustering, Active Contours and Machine Learning techniques for the segmentation of images in order to identify the fetal body. These approaches promoted the development of new models that allow face the inherent difficulties in treating this type of images. Methods for the crown-rump length automatic measurement are also proposed, some of which depend on the suggested segmentation methods. The results obtained for the crown-rump length presented a relative mean absolute error within inter-operator variability ranges reported in the literature