Intelligent Noninvasive Diagnosis of Aneuploidy:Raw Values and Highly Imbalanced Dataset

The objective of this paper is to introduce a noninvasive diagnosis procedure for aneuploidy and to minimize the social and financial cost of prenatal diagnosis tests that are performed for fetal aneuploidies in an early stage of pregnancy. We propose a method by using artificial neural networks trained with data from singleton pregnancy cases, while undergoing first trimester screening. Three different datasets' with a total of 122 362 euploid and 967 aneuploid cases were used in this study. The data for each case contained markers collected from the mother and the fetus. This study, unlike previous studies published by the authors for a similar problem differs in three basic principles: 1) the training of the artificial neural networks is done by using the markers' values in their raw form (unprocessed), 2) a balanced training dataset is created and used by selecting only a representative number of euploids for the training phase, and 3) emphasis is given to the financials and suggest hierarchy and necessity of the available tests. The proposed artificial neural networks models were optimized in the sense of reaching a minimum false positive rate and at the same time securing a 100% detection rate for Trisomy 21. These systems correctly identify other aneuploidies (Trisomies 13&18, Turner, and Triploid syndromes) at a detection rate greater than 80%. In conclusion, we demonstrate that artificial neural network systems can contribute in providing noninvasive, effective early screening for fetal aneuploidies with results that compare favorably to other existing methods

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

Pathogenic and low-frequency variants in children with central precocious puberty

Background: Central precocious puberty (CPP) due to premature activation of GnRH secretion results in early epiphyseal fusion and to a significant compromise in the achieved final adult height. Currently, few genetic determinants of children with CPP have been described. In this translational study, rare sequence variants in MKRN3, DLK1, KISS1, and KISS1R genes were investigated in patients with CPP. Methods: Fifty-four index girls and two index boys with CPP were first tested by Sanger sequencing for the MKRN3 gene. All children found negative (n = 44) for the MKRN3 gene were further investigated by whole exome sequencing (WES). In the latter analysis, the status of variants in genes known to be related with pubertal timing was compared with an in-house Cypriot control cohort (n = 43). The identified rare variants were initially examined by in silico computational algorithms and confirmed by Sanger sequencing. Additionally, a genetic network for the MKRN3 gene, mimicking a holistic regulatory depiction of the crosstalk between MKRN3 and other genes was designed. Results: Three previously described pathogenic MKRN3 variants located in the coding region of the gene were identified in 12 index girls with CPP. The most prevalent pathogenic MKRN3 variant p.Gly312Asp was exclusively found among the Cypriot CPP cohort, indicating a founder effect phenomenon. Seven other CPP girls harbored rare likely pathogenic upstream variants in the MKRN3. Among the 44 CPP patients submitted to WES, nine rare DLK1 variants were identified in 11 girls, two rare KISS1 variants in six girls, and two rare MAGEL2 variants in five girls. Interestingly, the frequent variant rs10407968 (p.Gly8Ter) of the KISS1R gene appeared to be less frequent in the cohort of patients with CPP. Conclusion: The results of the present study confirm the importance of the MKRN3-imprinted gene in genetics of CPP and its key role in pubertal timing. Overall, the results of the present study have emphasized the importance of an approach that aligns genetics and clinical aspects, which is necessary for the management and treatment of CPP

Artificial neural networks to investigate the importance and the sensitivity to various parameters used for the prediction of chromosomal abnormalities

A selection of artificial neural network models were built and implemented for systematically study the contribution and the sensitivity of the main influencing parameters as important contributing factors for the non-invasive prediction of chromosomal abnormalities. The parameters that had been investigated are: the previous medical history of the pregnant mother, the nasal bone, the tricuspid flow, the ductus venosus flow, the PAPP-A value, the b-hCG value, the crown rump length (CRL), the changes in nuchal translucency (deltaNT) and the mother’s age. The main conclusions drawn are: 1) The deltaNT is the most significant factor for the overall prediction, while the CRL the least significant. 2) The previous medical history of the pregnant mother is not a significant factor for the prediction of the abnormal cases. 3) The nasal bone, the tricuspid flow and the ductus venosus flow contribute significantly in the prediction of trisomy 21 but not in the prediction of the “normal” cases. 4) The PAPP-A, the b-hCG and the mother’s age are of intermediate importance. Also, a sensitivity analysis of the attributes PAPP-A, b-hCG, CRL, deltaNT and of the mother’s age was done. This analysis showed that the CRL and deltaNT are more sensitive when their values are decreased, the PAPP-A is more sensitive when its values are increased and the b-hCG is insensitive to variations in its value

Artificial neural networks to investigate the significance of PAPPA and b-hCG for the prediction of chromosomal abnormalities

A systematic approach has been done, to investigate different neural network structures for the appraisal of the significance of the free b-human chorionic gonadotrophin (b-hCG) and the pregnancy associated plasma protein-A (PAP-PA) as important parameters for the prediction of the existence of chromosomal abnormalities in fetuses. The database that has been used was highly unbalanced. It was composed of 35,687 cases of pregnant women. In the vast majority of cases (35,058) there had not been any chromosomal abnormalities, while in the remaining 629 (1.76%) some kind of chromosomal defect had been confirmed. 8,181 cases were kept as a totally unknown database that was used only for the verification of the predictability of each network, and for evaluating the importance of PAPP-A and b-hCG as significant predicting factors. In this unknown data set, there were 76 cases of chromosomal defects. The system was trained by using 8 input parameters that were considered to be the most influential at characterizing the risk of occurrence of these types of chromosomal anomalies. Then, the PAPP-A and the b-hCG were removed from the inputs in order to ascertain their contributory effects. The best results were obtained when using a multilayer neural structure having an input, an output and two hidden layers. It was found that both of PAPP-A and b-hCG are needed in order to achieve high correct classifications and high sensitivity of 88.2% in the totally unknown verification data set. When both the b-hCG and PAPP-A were excluded from the training, the diagnostic yield dropped down to 65%

Prenatal diagnosis of aneuploidy using artificial neural networks in relation to health economics

The early detection of fetal chromosomal abnormalities such as aneuploidies, has been an important subject in medicine over the last thirty years. A pregnant woman is advised by the doctor to perform an amniocentesis test, after the identification of increased risk for fetal aneuploidy. Even though the amniocentesis test is almost perfectly accurate, it has several drawbacks. It is an invasive test with around 1% risk for miscarriage; it is financially expensive and requires laboratories and special equipment. In this work we propose a non-invasive method for aneuploidy detection using a dataset with pre-natal examinations of pregnant women and artificial neural networks. We have used a dataset with 50,517 euploid and 691 aneuploid cases. Biological markers of the mother such as the age, blood proteins and ultrasonographic information from the fetus are used as input to the networks. A training set is used to construct neural networks and a test set is used for validation. Each unknown case is assigned into a class between “euploid” and “aneuploid” using a cut-off value on the network output. We create a ROC curve by computing the sensitivity and the specificity for a set of different cut-off values. From the ROC curve, we indicate the importance of the cut-off values in terms of health economics and social affection. It is shown that by increasing the cut-off value, the false positive rate reduces with the cost of an increased false negative rate

Testing the predictability of the Cyprus Stock Exchange: The case of an emerging market

Proceedings of the International Joint Conference on Neural Networks, Volume 6A systematic investigation of the effect of different neural network architecture alternatives for predicting the future course of stock prices in the Cyprus Stock Exchange (CSE) market is conducted. This market exhibited an abrupt increase in the general index price during the last year, thus forming a very interesting case for research purposes. The influence of various economic and political factors, from both the local and the international scene, have also been examined. The main conclusion drawn is that the CSE market is governed by unique conditions which when properly modeled can yield successful predictions

Prenatal Diagnosis of Aneuploidy Using Artificial Neural Networks in Relation to Health Economics

RASimAs (Regional Anaesthesia Simulator and Assistant) is a EU FP7 project that aims at increasing the application, the effectiveness and the success rates of regional anaesthesia by developing two independent but complementary systems, one system for training by using patient-specific computer models, and one for guidance in the assistance of nerve’s location during the actual intervention. In this context, the present document focuses on the training system, which will be deployed in multiple participating hospitals that will be connected to a central information system. In particular, this paper deals with the software architecture of the aforementioned integrated environment and the components that constitute it. We present indicative key components and functionalities such as the user authentication and authorization service, the user profile and performance metrics management service, the role based access control system, the VPH (Virtual Physiological Human) library, and the synchronization between the training centres and the central information system