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Vol 80, No 12 (2009)
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Prognosis of the IVF ICSI/ET procedure efficiency with the use of artificial neural networks among patients of the Department of Reproduction and Gynecological Endocrinology

Sławomir Wołczyński, Jacek Szamatowicz, Anna Justyna Milewska, Jan Domitrz, Jacek Jamiołkowski, Robert Milewski
Ginekol Pol 2009;80(12).

open access

Vol 80, No 12 (2009)
ARTICLES

Abstract

Summary Objectives: Prognosis of pregnancy for patients treated with IVF ICSI/ET methods, using artificial neural networks. Material and methods: Retrospective study of 1007 cycles of infertility treatment of 899 patients of Department of Reproduction and Gynecological Endocrinology in Bialystok. The subjects were treated with IVF ICSI/ET method from August 2005 to September 2008. Results: Classifying artificial neural network is described in the paper. Architecture of the network is three-layered perceptron consisting of 45 neurons in the input layer, 14 neurons in the hidden layer and a single output neuron. The source data for the network are 36 variables. 24 of them are nominal variables and the rest are quantitative variables. Among non-pregnancy cases only 59 prognosis of the network were incorrect. The results of treatment were correctly forecast in 68.5% of cases. The pregnancy was accurately confirmed in 49.1% of cases and lack of pregnancy in 86.5% of cases. Conclusions: Treatment of infertility with the use of in vitro fertilization methods continues to have too low efficiency per one treatment cycle. To improve this indicator, it is necessary to find dependencies, which describe the model of IVF treatment. The application of advanced methods of bioinformatics allows to predict the result of the treatment more effectively. With the help of artificial neural networks, we are able to forecast the failure of the treatment using IFV ICSI/ET procedure with almost 90% probability of certainty. These possibilities can be used to predict negative cases.

Abstract

Summary Objectives: Prognosis of pregnancy for patients treated with IVF ICSI/ET methods, using artificial neural networks. Material and methods: Retrospective study of 1007 cycles of infertility treatment of 899 patients of Department of Reproduction and Gynecological Endocrinology in Bialystok. The subjects were treated with IVF ICSI/ET method from August 2005 to September 2008. Results: Classifying artificial neural network is described in the paper. Architecture of the network is three-layered perceptron consisting of 45 neurons in the input layer, 14 neurons in the hidden layer and a single output neuron. The source data for the network are 36 variables. 24 of them are nominal variables and the rest are quantitative variables. Among non-pregnancy cases only 59 prognosis of the network were incorrect. The results of treatment were correctly forecast in 68.5% of cases. The pregnancy was accurately confirmed in 49.1% of cases and lack of pregnancy in 86.5% of cases. Conclusions: Treatment of infertility with the use of in vitro fertilization methods continues to have too low efficiency per one treatment cycle. To improve this indicator, it is necessary to find dependencies, which describe the model of IVF treatment. The application of advanced methods of bioinformatics allows to predict the result of the treatment more effectively. With the help of artificial neural networks, we are able to forecast the failure of the treatment using IFV ICSI/ET procedure with almost 90% probability of certainty. These possibilities can be used to predict negative cases.
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Keywords

infertility, treatment effectiveness, in vitro fertilization, neural networks

About this article
Title

Prognosis of the IVF ICSI/ET procedure efficiency with the use of artificial neural networks among patients of the Department of Reproduction and Gynecological Endocrinology

Journal

Ginekologia Polska

Issue

Vol 80, No 12 (2009)

Bibliographic record

Ginekol Pol 2009;80(12).

Keywords

infertility
treatment effectiveness
in vitro fertilization
neural networks

Authors

Sławomir Wołczyński
Jacek Szamatowicz
Anna Justyna Milewska
Jan Domitrz
Jacek Jamiołkowski
Robert Milewski

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