The application of neural networks in predicting the outcome of in-vitro fertilization.
Infertility affects one in six couples at some time in their lives, with 48% of these couples requiring assisted conception techniques in order to achieve a pregnancy. Whilst the overall clinical pregnancy rate per embryo transfer is 23%, this varies widely between clinics. The Human Fertilisation and Embryology Authority has attempted to analyse the results of all units, with weighting of different factors affecting assisted conception, and the published data have invariably led to comparisons between units. However, statistical models need to be developed to eliminate bias for valid comparisons. Neural networks offer a novel approach to pattern recognition. In some instances neural networks can identify a wider range of associations than other statistical techniques due in part to their ability to recognize highly non-linear associations. It was hoped that a neural network approach may be able to predict success for individual couples about to undergo in-vitro fertilization (IVF) treatment. A neural network was constructed using the variables of age, number of eggs recovered, number of embryos transferred and whether there was embryo freezing. Overall the network managed to achieve an accuracy of 59%.
Kaufmann
S J
SJ
Assisted Conception Unit, St James's University Hospital, Leeds, UK.
Eastaugh
J L
JL
Snowden
S
S
Smye
S W
SW
Sharma
V
V
D000328:Adult; D000367:Age Factors; D015925:Cryopreservation; D004624:Embryo Transfer; D005260:Female; D005307:Fertilization in Vitro; D006801:Humans; D007246:Infertility / Q000628:therapy; D008297:Male; D016571:Neural Networks, Computer; D009865:Oocytes / Q000166:cytology; D011247:Pregnancy; D016896:Treatment Outcome