| 李笑天,庄依亮,常才,张珏华.神经网络在预测胎儿体重中的作用[J].中国医学影像技术,1999,15(5):400~402 |
| 神经网络在预测胎儿体重中的作用 |
| The Value of a Neural Network for the Ultrasonographic Expectation of Fetal Weight at Delivery |
| 投稿时间：1999-01-14 |
| DOI： |
| 中文关键词: 神经网络 妊娠 胎儿体重 超声 |
| 英文关键词:Neural network Pregnancy Birth weight Ultrasonography |
| 基金项目: |
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| 摘要点击次数: 1446 |
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| 中文摘要: |
| 目的对胎儿超声参数进行神经网络处理,建立预测出生时的胎儿体重的前馈神经网络(BP)模型。方法本网络由输入层、隐藏层和输出层三部分组成,输入层有9个神经元(Neurons,Nij表示第i层第j个神经元),分别表示BPD、AD和FL的10th%、50th%和90th%.隐藏层分两层,第一层有18个神经元,第二层有6个神经元,这两层对输入信号进行处理。输出层有3个神经元,分别代表胎儿体重的10th%、50th%和90th%.用21个典型的信号模式训练网络,要求最大误差小于0.05,用Sigmoid公式转换BP的输入和输出信号。BP网络建成后用371例临床资料评价该BP模型。结果BP预测值与实际结果之间有明显的正相关(r=0.6850,P<0.001),预测误差平均为318g,约10.97%.误差小于100g的占21.2%,小于200g的占39.08%,小于300g的占53.46%,小于500g的占79.26%.BP的准确性受胎儿出生体重影响(r=0.6,P<0.05),但与胎龄、超声检查与分娩的时间差等因素无关。结论BP网络预测胎儿体重十分有效,但网络的结果以及参数的设定等方面有待于进一步的改进。 |
| 英文摘要: |
| Objective To evaluate the ultrasonographic expection of fetal weight at delivery with a nural network computer model.Methods A four layer backpropagation neural network(BP) was built with 9 neurons in input layer,18 neurons in first hiden layer,6 neuron in second hiden layer,and 3 neurons in output layer.The Sigmoid function was used to translate code between decimol and binery number in preoperative and postoperative proccession,and the BP was trained with 21 idealised patterns.The rasults of this predictive BP model were compared with the actural fetal weights at delivery in 371 cases third tri term pregnancy fetus.Results There were significant relationship between the BP model predictive value and actual fetal weights( r =0 6850, P <0 01),the average error of prediction was 318 g(10 97%),the error in 21 2% cases were less than 100g,the error in 39 0% cases were less than 200g,in 53 6% less than 300g,in 79 26% cases less than 500g.the precision of this BP model was affected by fetal weight( P <0 05),but were not been done by gestational age,and weeks to delivery.Conclusion this BP neural network was very effecive and perspective,though the constructure and prameters need to be improved.This BP model provided a new method to treat the Ultrasonography and Obstetrics complex data. |
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