Artificial intelligence-driven oocyte assessment for predicting blastulation and high-quality blastocyst formation in severe male factor infertility.

Abstract

Objective: To study whether artificial intelligence (AI)-driven oocyte evaluation is associated with blastocyst development and quality in couples with severe male factor infertility (SMF) undergoing intracytoplasmic sperm injection (ICSI) cycles.

Design: Cohort study.

Subjects: Fourteen thousand six hundred two oocyte images from 2,156 ICSI cycles performed between January 2020 and May 2024 in a private, university-affiliated in vitro fertilization center. Cycles were categorized into the following two groups: SMF (n = 200 cycles, 1,478 embryos) and non-SMF (n = 1,956 cycles, 13,124 embryos). Severe male factor infertility was defined as <5 million sperm in the ejaculate.

Exposure: Oocyte images were captured before ICSI and scored using the AI tool MAGENTA. The predictive value of Magenta Scores (MS) on embryonic development was assessed. The association between MS and oocyte fertilization and blastocyst formation was analyzed.

Main outcome measures: Oocyte fertilization, blastulation rate, and blastocyst quality.

Results: Magenta scores were significantly lower in oocytes that failed to fertilize compared with those that successfully fertilized (5.00 ± 0.04 vs. 6.44 ± 0.03). Blastulation rate was lower in the SMF group (46.61% vs. 50.80%), and blastocysts exhibited higher MS than nonblastocysts (5.12 ± 0.3 vs. 6.69 ± 0.3). The top-quality blastocyst rate was lower in SMF (56.6% vs. 65.2%), and high-quality blastocysts had higher MS than lower-quality ones (7.2 ± 0.6 vs. 6.8 ± 0.5). Among SMF cycles, MS were lower in oocytes that failed to fertilize (4.91 ± 0.12 vs. 6.34 ± 0.10). Magenta scores also differed between embryos that reached the blastocyst stage and those that did not (6.70 ± 0.11 vs. 4.96 ± 0.10). Top-quality blastocysts had significantly higher MS than others (7.00 ± 0.21 vs. 6.39 ± 0.19). Paternal age negatively correlated with fertilization, blastulation, and blastocyst quality; however, differences remained significant after adjusting for paternal age.

Conclusion: Artificial intelligence-based oocyte evaluation is associated with fertilization, blastulation, and blastocyst quality in SMF couples undergoing ICSI cycles. Magenta score values were consistently higher for blastocysts than nonblastocysts, demonstrating the AI tool's utility in identifying oocytes with greater developmental potential, regardless of male infertility factors. However, the absence of sperm-specific factors in the MAGENTA algorithm may limit its ability to fully account for male infertility.