A neural computational model of ICSI outcomes from sperm source and other features

Abstract

Objective: With the success and increasing popularity of ICSI, accurately predicting outcomes from this artificial reproductive technique becomes highly desirable. We examined a large dataset of ICSI outcomes and associated clinical features such as sperm source, which technician performed the procedure, number of ova retrieved and maternal age, and modeled outcomes using neural computational techniques. Neural computation employs computer programs that implement mathematical models based loosely on the function of networks of biological neurons. Statistical feature extraction, allowing examination of the significance of individual features such as sperm source, was employed using Wilk’s Generalized Likelihood Ratio Test (GLRT). Design: Neural computational modeling of retrospective data. Materials/Methods: We modeled fertilization outcomes from ICSI from technician (7 different technicians performed the procedure,) sperm source (testis extraction, MESA, EEJ,) number of ova retrieved and maternal age using neUROn2++, a set of C++ programs we developed to implement neurocomputational algorithms, discriminant function and regression techniques using the Cygwin (Red Hat) GNU C++ port for Windows (Microsoft) and Visual C++ (Microsoft) distributed across Pentium (Intel) platforms. Wilk’s GLRT was implemented in neUROn2++ for statistical feature extraction. A dataset of 1070 unique ICSI outcomes was randomized into a training set of 1349 exemplars and 449 test exemplars. N1/N2 cross-validation was employed. Results: Overall model accuracy was ROC AUC 0.922 (sensitivity 86.9%, specificity 79.0%) in the training set, and 0.897 (sensitivity 82.8%, specificity 75.1%) in the test set. Feature extraction using Wilk’s GLRT in reverse regression is shown in the table. All features were significant to the model (all p <1e-5), however, with p <1e-106, technician was highly significant.TableRegression analysis of neural computational model to predict ICSI outcomes.Featurep-valueTechnician1.04e-107No. ova retrieved9.84e-45Testis extraction3.81e-21Maternal age7.70e-20MESA2.33e-10EEJ1.52e-06p-values indicate significance of the individual feature to the likelihood of fertilization with ICSI. Open table in a new tab p-values indicate significance of the individual feature to the likelihood of fertilization with ICSI. Conclusions: We modeled ICSI outcome with high accuracy (ROC AUC 0.897 in the test set) using a neural computational approach. In order to determine the significance of individual features to the model’s outcome, regression based on Wilk’s GLRT was performed. All features examined (sperm source, number of ova retrieved, maternal age and which technician performed the procedure) were statistically significant to the model’s outcome. Supported by: NIH P01 HD36289 to Dolores J Lamb, Larry I Lipshultz and Craig Niederberger.