M Murat Samli1, Ismet Dogan. 1. Department of Urology, School of Medicine, School of Veterinary Medicine, Afyon Kocatepe University, Afyon, Turkey. msamli@tr.net
Abstract
PURPOSE: We developed an artificial neural network (ANN) for predicting spermatozoa prior to testicular biopsy in men with nonobstructive azoospermia. The performance of this ANN was compared to that of the standard logistic regression (LR) model. MATERIALS AND METHODS: Data were retrospectively collected from the physical examination and laboratory records of 303 patients who presented with infertility due to nonobstructive azoospermia. Input factors were patient age, duration of infertility, serum follicle-stimulating hormone, luteinizing hormone, total testosterone and prolactin, and left and right testicular volume. The ANN and LR models were constructed based on data on cases in which spermatozoa were and were not detected on testicular sperm extraction. The ANN was trained and validated with the data in the training (200 cases) and validation (30 cases) sets, and the model was then used to predict findings in the test set (73 cases). The LR model was constructed using the same data on the 230 training and validation cases. The same 73 patients served as the test set. RESULTS: The sensitivity of the ANN model was significantly higher than that of the LR model (68% vs 28%, p < 0.0001). The neural network correctly predicted the outcome in 59 of the 73 test set patients (80.8%), whereas LR correctly predicted the outcome in 48 (65.7%, p = 0.07). CONCLUSIONS: This ANN model, which is based on age, duration of infertility, serum hormone levels and testicular volumes, has clinically acceptable sensitivity. It may be of value for predicting spermatozoa in men with nonobstructive azoospermia.
PURPOSE: We developed an artificial neural network (ANN) for predicting spermatozoa prior to testicular biopsy in men with nonobstructive azoospermia. The performance of this ANN was compared to that of the standard logistic regression (LR) model. MATERIALS AND METHODS: Data were retrospectively collected from the physical examination and laboratory records of 303 patients who presented with infertility due to nonobstructive azoospermia. Input factors were patient age, duration of infertility, serum follicle-stimulating hormone, luteinizing hormone, total testosterone and prolactin, and left and right testicular volume. The ANN and LR models were constructed based on data on cases in which spermatozoa were and were not detected on testicular sperm extraction. The ANN was trained and validated with the data in the training (200 cases) and validation (30 cases) sets, and the model was then used to predict findings in the test set (73 cases). The LR model was constructed using the same data on the 230 training and validation cases. The same 73 patients served as the test set. RESULTS: The sensitivity of the ANN model was significantly higher than that of the LR model (68% vs 28%, p < 0.0001). The neural network correctly predicted the outcome in 59 of the 73 test set patients (80.8%), whereas LR correctly predicted the outcome in 48 (65.7%, p = 0.07). CONCLUSIONS: This ANN model, which is based on age, duration of infertility, serum hormone levels and testicular volumes, has clinically acceptable sensitivity. It may be of value for predicting spermatozoa in men with nonobstructive azoospermia.