Gürgen F.Önal E.Varol F.G.2024-06-122024-06-1219970739-5175https://doi.org/10.1109/51.585518https://hdl.handle.net/20.500.14551/16292A study was conducted to determine the usefulness of ultrasonography with feedforward neural network (NN) in intrauterine growth retardation (IUGR) detection. Multiple parameters such as head circumference (HC), abdominal circumference (AC) and HC/AC are better than the prediction with a single parameter. Multiple examinations give better insight for IUGR detection than does single examination. NN is helpful in correlating many variables that if taken alone, may not be significant but as a group provide additional information to make the best decision.en10.1109/51.585518info:eu-repo/semantics/closedAccessApproximation Theory; Backpropagation; Computer Aided Diagnosis; Feedforward Neural Networks; Learning Algorithms; Mathematical Models; Pattern Recognition; Regression Analysis; Spurious Signal Noise; Ultrasonic Imaging; Biparietal Diameter (Bpd); Head Circumference (Hc); Intrauterine Growth Retardation (Iugr); Sigmoidal Basis Functions; Ultrasonography; Fetal Monitoring; Article; Artificial Neural Network; Calculation; Controlled Study; Echography; Fetus; Gestational Age; Human; Intrauterine Growth Retardation; Abdomen; Algorithms; Diagnosis, Computer-Assisted; Embryonic And Fetal Development; Female; Fetal Growth Retardation; Forecasting; Gestational Age; Head; Humans; Neural Networks (Computer); Pattern Recognition, Automated; Pregnancy; Ultrasonography, PrenatalArticle16355582-s2.0-18422909959158986N/A