1. 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.

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

3. 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

4. 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

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

6. System Diagram 6

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

8. 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

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

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

11. Material and Method 11 Cluster-Based ANN Modeling.

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

13. Experiments and Results 13 ClusterTrain DataTest DataMAEMAPE All1489638149.4±110.2g4.9±3.5% I9540104.5±93.6g5.4±4.7% II743319147.1±108.4g4.9±3.6% III617264166.2±111.2g4.8±3.2% IV341519.8±19.2g2.9±2.5%

14. Experiments and Results 14

15. 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

16. Thank you for your attend~ 16