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].
abstract: OBJECTIVE
  Prognosis of pregnancy for patients treated with IVF ICSI/ET methods, using artificial neural networks.
  
  
  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.
authors.affiliation: Zakład Statystyki i Informatyki Medycznej, Uniwersytet Medyczny w Białymstoku. milewski@umwb.edu.pl
authors.forename: Robert
authors.initials: R
authors.lastname: Milewski
authors.forename: Jacek
authors.initials: J
authors.lastname: Jamiołkowski
authors.forename: Justyna
authors.initials: J
authors.lastname: Milewska Anna
authors.forename: Jan
authors.initials: J
authors.lastname: Domitrz
authors.forename: Jacek
authors.initials: J
authors.lastname: Szamatowicz
authors.forename: Sławomir
authors.initials: S
authors.lastname: Wołczyński
mesh_terms: D000328:Adult; D004624:Embryo Transfer / Q000706:statistics & numerical data*; D005260:Female; D005306:Fertilization; D006801:Humans; D007246:Infertility / Q000453:epidemiology* / Q000628:therapy*; D007247:Infertility, Female / Q000453:epidemiology / Q000628:therapy; D007248:Infertility, Male / Q000453:epidemiology / Q000628:therapy; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D017060:Patient Satisfaction / Q000706:statistics & numerical data; D011044:Poland; D016742:Preconception Care / Q000458:organization & administration; D011247:Pregnancy; D011256:Pregnancy Outcome / Q000453:epidemiology*; D011379:Prognosis; D012189:Retrospective Studies; D020554:Sperm Injections, Intracytoplasmic / Q000706:statistics & numerical data*; D016896:Treatment Outcome