Infertility is defined as a failure of a couple to achieve a pregnancy after 12 or more months of unprotected intercourse. Males are found to be solely responsible for 20-30% of infertility cases but contribute to 50% of cases overall. The selection of sperm to microinject is completely subjective and there is high intra- and inter- observer variability. SiDTM v2.0 is an algorithm which analyses real-time seminal samples located at ICSI dishes. Particularly, it assesses morphology and several motility parameters of each sperm, and it assigns a categorical and numerical score to each one. Categorical scores are represented by colours: green colour for optimal sperm, yellow for good sperm, orange for medium-quality sperm and red for low-quality sperm. Numerical scores ranged from 0 to 100, with higher scores for those best-quality sperm. SiDTM v2.0 can reduce subjectivity of the sperm selection process to the maximum, selecting the optimal sperm in real time. In addition, it could help junior embryologists to perform this complex and tedious procedure, which is the sperm selection for ICSI. To carry out the study, we will conduct a prospective cohort study in a total of 100 couples. Therefore, the aim of this study is to validate SiDTM v2.0 as an useful Artificial Intelligence-tool for sperm selection; that means achieving , at least, same clinical results as sperm selection performed by the embryologist.