Cluster-Based Artificial Neural Network on Ultrasonographic Parameters for Fetal Weight Estimation Reporter ： Huang Kun-Yi From ： International Federation.

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Cluster-Based Artificial Neural Network on Ultrasonographic Parameters for Fetal Weight Estimation Reporter ： Huang Kun-Yi From ： International Federation for Medical and Biological Engineering. BY Yueh-Chin Cheng, Chi-Chun Hsia, Fong-Ming Chang, Chun-Ju Hou, Yu-Hsien Chiu, and Kao-Chi Chung.

Outline  Introduction  Material and Methods  Experiments and Results  Discussion 2

Introduction  Accurate estimation of fetal weight (EFW) and fetal growth rate become an important is in obstetrics.  In 2008, fetal birth weight.[1]  Low birth weight (less than 2.5 Kg) ： 8.54%  Macrosomia (equal to or more than 4 Kg) ： 1.87%  Low birth weight infants have high risk incidences of cerebral dysfunction. 3

Introduction  Based on ultrasonographic parameters (USPs), fetal weight estimation methods ：  Multiple regression models.  Artificial neural network models. (ANN)  Large estimation error is a thorny problem in the clinical treatment for obstetricians.  The accuracy of fetal weight estimated is eagerly waiting to be improved. 4

Introduction  This study proposes a cluster-based ANN model to estimate fetal weight for different body figure. 5

System Diagram 6

Material and Method 7  Fetal biometric measurements were quantified by ultrasound with a 3.5 MHz convex transducer.  Numerical parameters ： 7  Nominal parameters ： 2

Material and Method 8 ParameterAbbreviationChinese Biparietal diameterBPD 頂骨直徑 Occipitofrontal diameterOFD 額頭直徑 Abdominal circumferenceAC 腹圍 Head circumferenceHC 頭圍 Femur lengthFL 股骨長度 Gestational ageGA 胎齡 Birth weightBW 出生重量 GenderSEX 性別 Fetal presentationFP 胎兒介紹

Material and Method 9 U is the total numbers of USPs. F is the total numbers of fetal.

Material and Method 10 Use Singular value decomposition. (SVD) K-means Method for Fetal Size Classification.

Material and Method 11 Cluster-Based ANN Modeling.

Experiments and Results  Estimated fetal weights and the birth weights.  Mean absolute error(MAE).  Mean absolute percent error(MAPE). 12

Experiments and Results 13 ClusterTrain DataTest DataMAEMAPE All ±110.2g4.9±3.5% I ±93.6g5.4±4.7% II ±108.4g4.9±3.6% III ±111.2g4.8±3.2% IV ±19.2g2.9±2.5%

Experiments and Results 14

Discussion  ANN mode is trained predicting fetal weight for each body figure cluster based on BPN algorithm and has also verified that the accuracy of fetal weight estimation of the cluster-based ANN model is genuinely preferable than those previous models. 15

Thank you for your attend~ 16