A determination of the standards of morphometrics variables of the stomatognathic system of a fetus

Introduction: Many factors affect the growth and development of the mandible. The most common one is micrognathia; this can poseand neonatal emergency. Early recognition of mandibular and other face anomalies could provide immediate care for these infants,and presence of neonatologist or other doctors in the delivery room. The aim: Aim of this study was to develop normal ranges of the facial markers: mandibular length, jaw index and the facial angle inthe fetus using 3D ultrasound. Material and methods: The research was conducted as a cross-sectional study in the second trimester of pregnancy. Fetuses (femalen=23 and male n=27) from singleton pregnancy between 29-37 week of gestation were examined by ultrasound. All images wereacquired transabdominally, using Voluson E16. Ultrasound was performed by an experienced operator (SM) and measured thevalues of head circumference, abdominal circumference, biparietal diameter, femur length, body mass. For mandibular length,inferior facial angle, and the jaw index was calculated (Jaw Index =AP mandibular diameter / BPD * 100), the profile images wereused (only images in the exact midsagittal plane were used). The characteristics of the fetal profiles were determined by the Schwartzand Ricketts profile analysis using soft tissue landmarks and analysis of the profile photographs. Results: The results show that the jaw index ranged from 25.33 and 34.06 with an average of 26.00 for all examined fetuses. Conclusion: The physiological position of the mandible is retrognathic and that the average physiological length of the mandible inthe third trimester is 2.31cm. There is no difference in mandibular length between genders

Application of Neural Networks for classification of Patau, Edwards, Down, Turner and Klinefelter Syndrome based on first trimester maternal serum screening data, ultrasonographic findings and patient demographics

Abstract Background The usage of Artificial Neural Networks (ANNs) for genome-enabled classifications and establishing genome-phenotype correlations have been investigated more extensively over the past few years. The reason for this is that ANNs are good approximates of complex functions, so classification can be performed without the need for explicitly defined input-output model. This engineering tool can be applied for optimization of existing methods for disease/syndrome classification. Cytogenetic and molecular analyses are the most frequent tests used in prenatal diagnostic for the early detection of Turner, Klinefelter, Patau, Edwards and Down syndrome. These procedures can be lengthy, repetitive; and often employ invasive techniques so a robust automated method for classifying and reporting prenatal diagnostics would greatly help the clinicians with their routine work. Methods The database consisted of data collected from 2500 pregnant woman that came to the Institute of Gynecology, Infertility and Perinatology “Mehmedbasic” for routine antenatal care between January 2000 and December 2016. During first trimester all women were subject to screening test where values of maternal serum pregnancy-associated plasma protein A (PAPP-A) and free beta human chorionic gonadotropin (β-hCG) were measured. Also, fetal nuchal translucency thickness and the presence or absence of the nasal bone was observed using ultrasound. Results The architectures of linear feedforward and feedback neural networks were investigated for various training data distributions and number of neurons in hidden layer. Feedback neural network architecture out performed feedforward neural network architecture in predictive ability for all five aneuploidy prenatal syndrome classes. Feedforward neural network with 15 neurons in hidden layer achieved classification sensitivity of 92.00%. Classification sensitivity of feedback (Elman’s) neural network was 99.00%. Average accuracy of feedforward neural network was 89.6% and for feedback was 98.8%. Conclusion The results presented in this paper prove that an expert diagnostic system based on neural networks can be efficiently used for classification of five aneuploidy syndromes, covered with this study, based on first trimester maternal serum screening data, ultrasonographic findings and patient demographics. Developed Expert System proved to be simple, robust, and powerful in properly classifying prenatal aneuploidy syndromes