Can in vitro embryo production be estimated from semen variables in Senepol breed by using artificial intelligence?

Abstract

Thoroughly analyzing the sperm and exploring the information obtained using artificial intelligence (AI) could be the key to improving fertility estimation. Artificial neural networks have already been applied to calculate zootechnical indices in animals and predict fertility in humans. This method of estimating the results of reproductive biotechnologies, such as in vitro embryo production (IVEP) in cattle, could be valuable for livestock production. This study was developed to model IVEP estimates in Senepol animals based on various sperm attributes, through retrospective data from 290 IVEP routines performed using 38 commercial doses of semen from Senepol bulls. All sperm samples that had undergone the same procedure during sperm selection for in vitro fertilization were evaluated using a computer-assisted sperm analysis (CASA) system to define sperm subpopulations. Sperm morphology was also analyzed in a wet preparation, and the integrity of the plasma and acrosomal membranes, mitochondrial potential, oxidative status, and chromatin resistance were evaluated using flow cytometry. A previous study identified three sperm subpopulations in such samples and the information used in tandem with other sperm quality variables to perform an AI analysis. AI analysis generated models that estimated IVEP based on the season, donor, percentage of viable oocytes, and 18 other sperm predictor variables. The accuracy of the results obtained for the three best AI models for predicting the IVEP was 90.7, 75.3, and 79.6%, respectively. Therefore, applying this AI technique would enable the estimation of high or low embryo production for individual bulls based on the sperm analysis information.

Keywords: IVEP; artificial intelligence; bovine; fertility; sperm kinetics.

Figure 1

Confusion matrix, receiver operating characteristic (ROC), and area under the curve for ANN 1, 2, and 3 architectures. The y-axis in ROC represents the sensitivity, and the x-axis refers to 1-specificity. In the confusion matrix, the green frames present the number of data and percentage of correctly performed classifications. Gray frames show the ratio of correct and incorrect classifications in each row and column of the array. The blue frame shows the overall error and hit percentage of the ANN model for the blind test data.

Figure 2

Confusion matrix and ROC curve for architecture 3 when applying blind test data.