An artificial intelligent diagnostic system with neural networks to determine genetical disorders and fetal health by using maternal serum markers

An Intelligent Prenatal Screening System for the Prediction of Trisomy-21 Using Triple Test Variables: The Hacettepe System

“…In this study, we have used a backpropagation learning algorithm (a supervised artificial neural network) to develop an intelligent antenatal screening system (heretofore referred as Hacettepe System) (10). TT variables were used as input variables, while "Down syndrome" (DS) and "non-DS" fetuses were the output of the algorithm.…”

“…Recent technological improvements enable physicians to use artificial intelligent systems (AIS) in clinical decision making (7)(8)(9). We have previously reported an artificial intelligent diagnostic system with neural networks to determine genetic disorders and fetal health using the TT biochemical markers (10). Unconjugated estriol (E3), beta-human chorionic gonadotropin (β-hCG), and α-feto protein (AFP) with gestational week and maternal age were used in the TT (6).…”

Using Rule-Based Reasoning and Object-Oriented Methodologies to Diagnose Diabetes

“…An example of ANN technique applied in the medical domain is Hacettepe System, consisting of two ANN models which were developed to reduce perinatal morbidity and mortality. One model is used to diagnose genetical disorder and the other is used to diagnose midpregnancy fetal health (Beksac et al, 1995). ANN can be used to represent complex parameter interactions and multiple variables (Mobley et al, 2000;Mobley et al, 2005), however, prediction can be difficult if the data are to be considered thoroughly as they are generally hidden (Long, 2001;Pandey and Mishra, 2009).…”

“…Discusses the proposed framework based on RBR-OO. (Ahmad and Bergen, 2010;Arshadi and Jurisica, 2005;Beksac et al, 1995, Clancey et al, 1979Cundell et al, 2001;Deng and Tsacle, 2000;Gorunescu, 2007;Gorzalczany and Piasta, 1999;Huang et al, 2007;Koutsojannis and Hatzilygeroudis, 2006;Kupinski and Giger, 1997;Lin, 2009;Llora and Garrell, 1999;Long, 2001;Lucas et al, 1999;Mobley et al, 2000;Mobley et al, 2005;Montani et al, 2003;Moorkamp, 2005;Nouaouria-Amri and Laskri, 2005;Pandey and Mishra, 2009;Park et al, 2011;Pena-Reyes and Sipper, 1999;Phuong and Kreinovich 2001;Podgorelec et al, 1999;Quellec et al, 2007;Renner and Ekart, 2003;Schmidt and Gierl, 2001;Shortliffe, 1986;Tsipouras et al, 2008;Turkoglu et al, 2003;Uckun et al, 1993;Vinterbo and Ohno-Machado, 2000;Vitez et al, 1996;Wang, 2007;Whitley, 1994) …”

Comparison of neural networks and statistical models to predict gestational age at birth

“…Applications include the prediction of patient response to warfarin [8], the likelihood of relapse following breast cancer [9] and the development of an expert system to predict foetal well being and the presence of specific genetic disorders in the unborn child [10]. Each of these applications utilised the supervised Multi-Layer Perceptron (MLP) neural network architecture [11].…”