کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
3975642 1600983 2012 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Efficient fetal size classification combined with artificial neural network for estimation of fetal weight
موضوعات مرتبط
علوم پزشکی و سلامت پزشکی و دندانپزشکی زنان، زایمان و بهداشت زنان
پیش نمایش صفحه اول مقاله
Efficient fetal size classification combined with artificial neural network for estimation of fetal weight
چکیده انگلیسی

ObjectivesA novel analysis was undertaken to select a significant ultrasonographic parameter (USP) for classifying fetuses to support artificial neural network (ANN), and thus to enhance the accuracy of fetal weight estimation.MethodsIn total, 2127 singletons were examined by prenatal ultrasound within 3 days before delivery. First, correlation analysis was used to determine a significant USP for fetal grouping. Second, K-means algorithm was utilized for fetal size classification based on the selected USP. Finally, stepwise regression analysis was used to examine input parameters of the ANN model.ResultsThe estimated fetal weight (EFW) of the new model showed mean absolute percent error (MAPE) of 5.26 ± 4.14% and mean absolute error (MAE) of 157.91 ± 119.90 g. Comparison of EFW accuracy showed that the new model significantly outperformed the commonly-used EFW formulas (all p < 0.05).ConclusionWe proved the importance of choosing a specific grouping parameter for ANN to improve EFW accuracy.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Taiwanese Journal of Obstetrics and Gynecology - Volume 51, Issue 4, December 2012, Pages 545–553
نویسندگان
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