Two-stage approach for risk estimation of fetal trisomy 21 and other aneuploidies using computational intelligence systems

ObjectiveTo estimate the risk of fetal trisomy 21 (T21) and other chromosomal abnormalities (OCA) at 11-13weeks' gestation using computational intelligence classification methods. MethodsAs a first step, a training dataset consisting of 72054 euploid pregnancies, 295 cases of T21 and 305 cases of OCA was used to train an artificial neural network. Then, a two-stage approach was used for stratification of risk and diagnosis of cases of aneuploidy in the blind set. In Stage 1, using four markers, pregnancies in the blind set were classified into no risk and risk. No-risk pregnancies were not examined further, whereas the risk pregnancies were forwarded to Stage 2 for further examination. In Stage 2, using seven markers, pregnancies were classified into three types of risk, namely no risk, moderate risk and high risk. ResultsOf 36328 unknown to the system pregnancies (blind set), 17512 euploid, two T21 and 18 OCA were classified as no risk in Stage 1. The remaining 18796 cases were forwarded to Stage 2, of which 7895 euploid, two T21 and two OCA cases were classified as no risk, 10464 euploid, 83T21 and 61 OCA as moderate risk and 187 euploid, 50T21 and 52 OCA as high risk. The sensitivity and the specificity for T21 in Stage 2 were 97.1% and 99.5%, respectively, and the false-positive rate from Stage 1 to Stage 2 was reduced from 51.4% to approximate to 1%, assuming that the cell-free DNA test could identify all euploid and aneuploid cases. ConclusionWe propose a method for early diagnosis of chromosomal abnormalities that ensures that most T21 cases are classified as high risk at any stage. At the same time, the number of euploid cases subjected to invasive or cell-free DNA examinations was minimized through a routine procedure offered in two stages. Our method is minimally invasive and of relatively low cost, highly effective at T21 identification and it performs better than do other existing statistical methods. Copyright (c) 2017 ISUOG. Published by John Wiley & Sons Ltd

Deep Learning to Predicting Live Births and Aneuploid Miscarriages from Images of Blastocysts Combined with Maternal Age

Objectives: Making an artificial intelligence (AI) classifier that uses the maternal age and an image of the implanted blastocyst to determine the probability of getting a live birth. Methods: The dataset comprised maternal age data and 407 images of blastocysts which led to live births and 246 images of blastocysts which led to aneuploid miscarriages, matched for maternal age. An AI system using deep learning was developed for predicting the classification and probability of a live birth. Results: The accuracy, sensitivity, specificity, and positive and negative predictive values of the developed AI classifier were 0.75, 0.82, 0.64, 0.79, and 0.68, respectively. The area under the curve was 0.73 ± 0.04 (mean ± standard error). Conclusions: A classifier using AI for a blastocyst image combined with the maternal age showed potential in determining the probability of a live birth

First Trimester Noninvasive Prenatal Diagnosis:A Computational Intelligence Approach

The objective of this study is to examine the potential value of using machine learning techniques such as artificial neural network (ANN) schemes for the noninvasive estimation, at 11-13 weeks of gestation, the risk for euploidy, trisomy 21 (T21), and other chromosomal aneuploidies (O.C.A.), from suitable sonographic, biochemical markers, and other relevant data. A database(1) consisted of 51,208 singleton pregnancy cases, while undergoing first trimester screening for aneuploidies has been used for the building, training, and verification of the proposed method. From all the data collected for each case from the mother and the fetus, the following 9 are considered by the collaborating obstetricians as the most relevant to the problem in question: maternal age, previous pregnancy with T21, fetal crown-rump length, serum free beta-hCG in multiples of the median (MoM), pregnancy-associated plasma protein-A in MoM, nuchal translucency thickness, nasal bone, tricuspid flow, and ductus venosus flow. The dataset was randomly divided into a training set that was used to guide the development of various ANN schemes, support vector machines, and k-nearest neighbor models. An evaluation set used to determine the performance of the developed systems. The evaluation set, totally unknown to the proposed system, contained 16,898 cases of euploidy fetuses, 129 cases of T21, and 76 cases of O.C.A. The best results were obtained by the ANN system, which identified correctly all T21 cases, i.e., 0% false negative rate (FNR) and 96.1% of euploidies, i.e., 3.9% false positive rate (FPR), meaning that no child would have been born with T21 if only that 3.9% of all pregnancies had been sent for invasive testing. The aim of this work is to produce a practical tool for the obstetrician which will ideally provide 0% FNR and to recommend the minimum possible number of cases for further testing such as invasive. In conclusion, it was demonstrated that ANN schemes can provide an effective early screening for fetal aneuploidies at a low FPR with results that compare favorably to those of existing systems

Preface : in silico pipeline for accurate cell-free fetal DNA fraction prediction

Objective During routine noninvasive prenatal testing (NIPT), cell-free fetal DNA fraction is ideally derived from shallow-depth whole-genome sequencing data, preventing the need for additional experimental assays. The fraction of aligned reads to chromosome Y enables proper quantification for male fetuses, unlike for females, where advanced predictive procedures are required. This study introduces PREdict FetAl ComponEnt (PREFACE), a novel bioinformatics pipeline to establish fetal fraction in a gender-independent manner. Methods PREFACE combines the strengths of principal component analysis and neural networks to model copy number profiles. Results For sets of roughly 1100 male NIPT samples, a cross-validated Pearson correlation of 0.9 between predictions and fetal fractions according to Y chromosomal read counts was noted. PREFACE enables training with both male and unlabeled female fetuses. Using our complete cohort (n(female) = 2468, n(male) = 2723), the correlation metric reached 0.94. Conclusions Allowing individual institutions to generate optimized models sidelines between-laboratory bias, as PREFACE enables user-friendly training with a limited amount of retrospective data. In addition, our software provides the fetal fraction based on the copy number state of chromosome X. We show that these measures can predict mixed multiple pregnancies, sex chromosomal aneuploidies, and the source of observed aberrations

Down Syndrome and Other Chromosome Abnormalities

This book provides a concise yet comprehensive source of current information on Down syndrome and other chromosomal abnormalities. Research workers, scientists, medical graduates and paediatricians will find it an excellent source for reference and review. Key features of this book are as follows: • Mechanisms of aneuploidy. • Effect of sociodemographic factors on different congenital disorders. • Haematological malignancies and congenital heart disease in Down syndrome. • Prenatal screening, management and counselling to detect Down syndrome and other chromosomal abnormalities. While aimed primarily at research workers on Down syndrome and different types of chromosomal disorders, 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 the parents and relatives of children suffering from Down syndrome and other chromosomal abnormality syndromes

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

Machine Learning in Fetal Cardiology: What to Expect

In fetal cardiology, imaging (especially echocardiography) has demonstrated to help in the diagnosis and monitoring of fetuses with a compromised cardiovascular system potentially associated with several fetal conditions. Different ultrasound approaches are currently used to evaluate fetal cardiac structure and function, including conventional 2-D imaging and M-mode and tissue Doppler imaging among others. However, assessment of the fetal heart is still challenging mainly due to involuntary movements of the fetus, the small size of the heart, and the lack of expertise in fetal echocardiography of some sonographers. Therefore, the use of new technologies to improve the primary acquired images, to help extract measurements, or to aid in the diagnosis of cardiac abnormalities is of great importance for optimal assessment of the fetal heart. Machine leaning (ML) is a computer science discipline focused on teaching a computer to perform tasks with specific goals without explicitly programming the rules on how to perform this task. In this review we provide a brief overview on the potential of ML techniques to improve the evaluation of fetal cardiac function by optimizing image acquisition and quantification/segmentation, as well as aid in improving the prenatal diagnoses of fetal cardiac remodeling and abnormalities

Prenatal Diagnosis

This book provides detailed and comprehensive coverage on various aspects of prenatal diagnosis-with particular emphasis on sonographic and molecular diagnostic issues. It features sections dedicated to fundamentals of clinical, ultrasound and genetics diagnosis of human diseases, as well as current and future health strategies related to prenatal diagnosis. This book highlights the importance of utilizing fetal ultrasound/clinical/genetics knowledge to promote and achieve optimal health in fetal medicine. It will be a very useful resource to practitioners and scientists in fetal medicine

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