E-ISSN: 1309-0380
    Computational models for prediction of intrauterine insemination outcomes
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    Clinical Study
    VOLUME: 8 ISSUE: 3
    P: 302-307
    September 2007

    Computational models for prediction of intrauterine insemination outcomes

    J Turk Ger Gynecol Assoc 2007;8(3):302-307
    Moshe Wald 1
    Amy E.T Sparks 2
    Bradley J Van Voorhis 2
    Craig H Syrop 2
    Criag S Niederberger 3
    1. Department Of Urology,University Of Iowa, Iowa City, Iowa, Usa
    2. Department Of Obstetrics And Gynecology, Division Of Endocrinology And Infertility, University Of Iowa, Iowa City, Iowa, Usa
    3. Department Of Urology, University Of Illinois At Chicago, Chicago, Illinois, Usa
    No information available.
    No information available
    Received Date: 29.12.2006
    Accepted Date: 22.02.2007
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    ABSTRACT

    OBJECTIVE:

    Intrauterine insemination (IUI) using ejaculated sperm is a common option in the treatment of infertility of various etiologies. We sought to develop a computational model for the prediction of pregnancy following IUI.

    MATERIALS-METHODS:

    A dataset of 212 exemplars, derived from patients who underwent a first IUI cycle with ejaculated sperm, was divided into separate modeling and cross-validation sets, and retrospectively analyzed. The dataset contained input features of maternal age, type of medication used for ovulation induction, semen volume, sperm concentration, motility and morphology and output intra-uterine pregnancy, and was modeled using various mathematical methods, including linear and radial support vector machines, linear and quadratic discriminant function analysis, logistic regression, and neural computation. Various model architectures were used, in an attempt to achieve the highest model accuracy. A logistic regression model was found to have the highest accuracy, with a test set ROC area of 0.717.

    RESULTS:

    Forward regression of this model showed sperm morphology to be the most significant feature in predicting pregnancy (p=0.39), followed by maternal age (p=0.42), type of medication used for ovulation induction (p=0.6), sperm motility (p=0.61), semen volume (p=0.71) and sperm concentration (p=0.9). Reverse regression of the model revealed sperm motility to be the most significant feature in predicting pregnancy (p=0.37), followed by sperm morphology (p=0.39), maternal age (p=0.49), type of medication used for ovulation induction (0.61), sperm concentration (p=0.72) and semen volume (p=0.74).

    CONCLUSIONS:

    A logistic regression model of clinical relevance was developed, and is deployed on the World Wide Web for clinical use.

    Keywords: maternal age, ovulation induction, sperm concentration, motility, morphology, IUI, computational models

    References

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