Automated AI for real-time sperm selection in ICSI: reducing variability and studying the role of sperm in embryo development.

BACKGROUND: The application of Artificial Intelligence (AI) to sperm selection during Intracytoplasmic Sperm Injection (ICSI) procedures represents one of the most innovative advances in assisted reproductive technology (ART). Traditional sperm selection relies heavily on the subjective assessment of embryologists, which can lead to variability in outcomes. This study aimed to evaluate the performance of an AI-based software, Sperm ID (SiD™) v.1.0, for sperm selection during ICSI and to compare its outcomes with those obtained by experienced embryologists. Additionally, the study assessed the potential impact of sperm and oocyte quality, particularly in autologous versus donor oocyte cycles. METHODS: A single-center, blind, observational study was conducted involving 102 infertile couples-60 undergoing treatment with autologous oocytes and 42 using oocytes from a donation program. Semen samples were analyzed in real time with SiD™ v.1.0, a software that quantifies progressive motility parameters and assigns each sperm a categorical score ('Best,' 'Good,' 'Medium,' or 'low'). Spermatozoa and oocytes were individually tracked from injection to embryo development. Oocyte quality was retrospectively analyzed using another AI tool, Magenta IVF R3.0. The performance of the Artificial Intelligence Sperm Selection (AISS) system was compared with that of senior embryologists (> 300 ICSI cycles/year). Statistical analysis included descriptive statistics and inferential tests to compare fertilization and embryo development rates across sperm categories and between autologous and donor cycles. RESULTS: Biological outcomes-such as fertilization and blastocyst development-were generally similar across all sperm quality categories. However, in cycles with autologous oocytes, the use of top-quality sperm ('Best' category) was associated with a significantly higher blastocyst formation rate. In contrast, no significant differences were observed in donor oocyte cycles, regardless of sperm quality. The AISS system demonstrated comparable performance to that of senior embryologists, with similar fertilization and embryo development rates. CONCLUSIONS: The study highlights the promising role of AI-based tools in standardizing and enhancing sperm selection during ICSI. While AI-driven sperm selection showed limited impact in donor cycles, it may offer a distinct advantage in cases involving compromised oocyte quality. Furthermore, AISS may improve laboratory efficiency and support junior embryologists by reducing selection time and increasing procedural consistency.