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%.

Objectives Current guidelines recommend that less than 20% of treatments in colposcopy clinics should be under general anaesthetic. The objective of this study was to increase the evidence base for guidelines by establishing the proportion of women receiving general anaesthesia for treatment, determining the predictors of and reasons recorded for general anaesthetic use.Design Retrospective analysis of routinely collected data. General anaesthetic was more likely to be used when the woman required loop excision (OR = 3.63, 95% CI 2.11-6.24) and less likely when directed biopsy was performed (OR = 0.11, 95% CI 0.01-0.80), when the patient appointment date was after introduction of new guidelines (OR = 0.37, 95% CI 0.24-0.56) or when the assessment visit was with a nonconsultant status doctor rather than nurse or consultant (OR = 0.70, 95% CI 0.50-0.97). General anaesthetic use varied between colposcopists ranging from 0 to 16.5% of new patients seen. Woman's choice was the most commonly specified reason for the use of general anaesthetic. ConclusionsThe proportion of colposcopy patients treated under general anaesthetic is 20%, within guideline limits. Substantial variation in general anaesthetic rates between colposcopists was observed, and further investigation is required to discover the reason for this.

Objective To assess the role of neural networks in predicting the likelihood of malignancy in women Design Retrospective case study.Setting University Department of Obstetrics and Gynaecology, St James's Hospital, Leeds.Methods Information from 217 cases with histologically proven benign, borderline or malignant tumours was extracted for study. Four variables (age, ultrasound findings with and without colour Doppler imaging and CA125) were entered in the neural network classifier. The neural network results were compared with logistic regression analysis.Results When used in the neural network the variables of age, CA125 and ultrasound score produced the best result with a sensitivity of 95% and a corresponding specificity of 78% in predicting malignancy. Logistic regression gave a sensitivity or 82% for a specificity of 51%.The neural network is a good method of combining diagnostic variables and may be a useful predictor of malignancy in women presenting with ovarian turnours. A comparison of the performance of the neural network with conventional diagnostic methods would be warranted prior to use in clinical practice.presenting with ovarian turnours. Conclusion

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