P-150 Differential contributions of sperm and oocyte to fertilization and embryo development: Insights from a recent AI-driven study

Abstract Study question Do sperm and oocytes have equivalent roles in fertilization and embryo development, according to Artificial Intelligence algorithms? Summary answer Female gamete plays a more significant role in the fertilization process than sperm; but differential effect on the formation of the blastocyst remains unclear. What is known already The contributions of the oocyte and sperm to fertilization and embryo development are distinct yet complementary, influencing early embryogenesis through their unique genetic and cytoplasmic components. Recent advancements in AI have enabled real-time assessment of gamete quality, providing unprecedented insights into their roles. AI-driven algorithms analyze key parameters such as sperm motility, sperm morphology or oocyte morphology, with unparalleled precision, allowing for the identification of subtle patterns associated with successful fertilization and optimal embryo development. This integration of AI-technology into reproductive medicine offers a transformative approach to improve outcomes in assisted reproduction. Study design, size, duration Single-centre, non-interventional and blind study including 165 ICSI procedures. Real-time semen analysis was performed using SiDTM v2.0 (IVF2.0, Ltd, Mexico), providing categorical and numerical individual scores. Oocytes were retrospectively assessed by Magenta IVF R3.0 (FutureFertility, Canada), giving also a numerical score to each one. Oocyte-sperm pairs were individually followed up to assess fertilisation status, blastocyst formation, and embryo quality according to ASEBIR criteria and embryo scoring AI-algorithms, over a period of ten months. Participants/materials, setting, methods 1023 oocyte-sperm pairs were studied. ICSI procedures were recorded using a digitizer attached to an optical microscope. Numerical SiD scores ranged from 0 to 700, with lower scores indicating better quality. Oocytes’ images were retrospectively taken from time-lapse incubators (0h after microinjection). Two groups were formed based on whether the microinjected sperm score was below or above 100. Additionally, two more groups were created based on the average oocyte score (<5 or > 5). Main results and the role of chance Outcomes in patients with poor oocyte quality shown slightly higher fertilization (FR) and blastocyst rates (BR) per MII, in oocytes microinjected with good-quality sperm (SiD score<100) compared to poor-quality sperm (SiD score>100) (FR = 73.33% vs 69.83%; BR = 46.67% vs 41.32%); whereas usable blastocyst rates (UBR) were similar (33.33% vs 33.47%). Embryo quality evaluated using ASEBIR criteria and AI-based scoring, showed modestly better results with the microinjection of good-quality sperm [top-quality embryos(A+B)=60.71% vs 47.00%; KIDScore = 5.27 vs 4.92; IDAScore = 5.13 vs 4.65; Embryoaid = 5.22 vs 4.92]. However, these differences were not statistically significant (p > 0.05). In patients with good oocyte quality, outcomes were almost identical regardless of sperm quality (SiD score < or > 100) [FR = 84.58% vs 82.73%; BR = 55.60% vs 57.55%; UBR = 43.45% vs 45.08%; top-quality embryos (A+B) = 59.66% vs 62.55%; KIDScore = 4.51 vs 4.96; IDAScore = 4.60 vs 4.77; Embryoaid = 5.46 vs 5.44] (p > 0.05). Differences on FR achieve by good quality sperm (SiD score<100) on poor and good oocyte quality were significantly higher in the last ones (73.33 % vs 84.58%; p < 0.05). BR and UBR were approximately 10% higher in good-quality oocytes, but these differences were not statistically significant (p > 0.05). Limitations, reasons for caution This study relies on AI-based tools, which, while promising, require further validation across larger and more diverse cohorts. SiDTM v2.0 do not assess sperm morphology, which is a key factor for selecting best sperm to microinject. Finally, the sample size and retrospective nature may introduce biases. Wider implications of the findings These findings highlight the critical role of oocyte quality in assisted reproduction outcomes, emphasizing the need to prioritize female gamete assessment to evaluate male factor contribution. Integrating AI tools can refine embryo selection processes and improve success rates. Trial registration number No

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