Sperm Selection

This study aims to assess the effect of age of the male partner and the reproductive ability of sperm prepared via sperm selection devices (Zymot) compared to routine embryologist selected sperm after density gradient centrifugation (DGC) preparation for intracytoplasmic sperm injection (ICSI) in patients undergoing in vitro fertilization treatment (IVF) of their infertility.

Infertility is a growing global health problem affecting millions of couples worldwide, with male infertility accounting for approximately half of all cases. In the physiological environment, sperm go through an exhaustive selection process in the female reproductive tract before reaching the oocyte. During this journey, progressive mobility and morphology are key parameters for achieving fertilisation. Therefore, before starting an assisted reproduction treatment, it is essential to analyse and process the semen sample to assess the fertile potential, select the most optimal sperm and determine the most appropriate treatment. Conventional methods of semen processing, such as density gradient centrifugation (DGC) and Swim-up washing of motile sperm, have significant limitations. These include interobserver and interlaboratory subjectivity, as well as damage to sperm DNA caused by centrifugation. Alternatively, microfluidics, which simulates natural selection, allows higher counts of morphologically normal, progressive motile sperm to be obtained. On the other hand, the CASA (computer-assisted sperm analysis) system has improved the standardisation and quality of semen analysis. Furthermore, the incorporation of Artificial Intelligence (AI) into semen quality analysis represents a promising opportunity, as it improves efficiency, accuracy and standardisation, and has the potential to increase success rates in assisted reproduction treatments. This project aims to develop an innovative AI-based diagnostic tool to address male infertility. The tool will integrate microfluidic technology and the CASA system to analyse semen quality, calculate fertilisation potential and recommend personalised treatments with an estimate of success. Trained with large volumes of biological and clinical data, it will provide a comprehensive and patient-specific diagnosis by identifying complex relationships between multiple variables. Finally, a comparative study will be conducted to evaluate laboratory indicators and clinical outcomes of cycles using this tool versus those using conventional methods.

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.