Computational models for prediction of IVF/ICSI outcomes with surgically retrieved spermatozoa

Abstract

IVF/intracytoplasmic sperm injection (ICSI) using surgically retrieved spermatozoa (SRS) is a key option in the treatment of severe male infertility. It was aimed to develop a computational model for the prediction of this modality's outcome. A dataset of 113 exemplars, derived from patients who underwent IVF/ICSI with SRS, was retrospectively analysed. The dataset, containing input features maternal age, sperm retrieval technique, type of spermatozoa used, type of male factor and output intrauterine pregnancy, was randomized into a modelling (‘training’) set of 83 and cross-validation (‘test’) set of 30. neUROn++, a set of C++ programs, was used to model the dataset using linear and quadratic discriminant function analysis, logistic regression, and neural computation. A 4-hidden node neural network was found to have the highest accuracy, with a test set receiver operator characteristic (ROC) curve area of 0.783. Reverse regression of this neural network showed maternal age to be the most significant feature in predicting pregnancy ( P = 0.025), followed by sperm type ( P = 0.076). Type of male factor ( P = 0.47) and sperm retrieval technique ( P = 0.88) did not predict outcome. In summary, a neural network of clinical relevance was found to be superior in terms of IVF/ICSI outcome prediction. Future media deployment is planned.