P-277 An artificial intelligence (AI) deselection model for top-quality blastocysts: algorithmic analysis of morphokinetic features for aneuploidy may increase implantation rates

Abstract

Abstract Study question Can an AI deselection model identify distinct morphokinetic patterns in top-quality blastocyst with unknown ploidy that fail to implant? Summary answer An AI based deselection model was able to predict implantation failure based on morphokinetic features previously found to associate with aneuploidy. What is known already Aneuploidy is the most common explanation for implantation failure of high-quality blastocysts. Yet, high-quality blastocysts with unknown ploidy that fail to implant are often morphologically indistinguishable from blastocysts that succeed to implant. Our previously published results (ESHRE 2021) demonstrated that aneuploid blastocysts were more likely to reach development events (t2-t8) later, and that the timing between each event was statistically longer (p < 0.001), when compared to euploid embryos. Given that delayed morphokinetic rates are tightly linked to ploidy, we investigated whether similar known morphokinetic features were associated with implantation failure in top-graded embryos. Study design, size, duration Time-lapse sequences of 3,259 top-quality blastocysts from fresh single embryo transfer cycles with known implantation outcomes were analyzed using an AI-based algorithm. The algorithm utilized convolutional neural network extracted temporal features based on multiple morphokinetic parameters known to associate with ploidy. Participants/materials, setting, methods time-lapse sequences and morphokinetic events were algorithmically analyzed to measure the rate of mitotic division events and compare the number of embryos in each category (implanted/nonimplanted) that reached each developmental event at least one standard deviation (SD) later than the mean for implanted embryos. Main results and the role of chance Results showed statistical differences in the following morphokinetic features between the two categories: t2, t3, t4, and t3-t4 (p < 0.05). Implanted top-graded blastocysts were likely to reach t2, t3, and t4 after 25.23 ± 3.8 SD, 36.06 ± 3.4 SD, and 37.14 hours ±3.6 SD, respectively. The time gap between t3 and t4 was found to be 12.25 hours ± 5.31 SD. Given this, we followed the methodology described above to propose cutoff values (in hours) that differentiated between non-implanted and implanted top-graded blastocysts based on their morphokinetic profiles. Implantation failure was found to be associated with the likelihood of reaching t2 after 28.61 hours (OR = 2.36, CI 0.96-5.77), t3 after 39.46 (OR = 3.48, CI 1.62-7.47), and t4 after 40.79 hours (OR = 2.23, CI 1.09- 4.53). A time gap between t3 and t4 of more than 17.56 hours was also associated with implantation failure (OR = 2.48, CI 1.12-5.48), indicating perturbed mitotic activity. The cutoff values proposed here were incorporated into the algorithm for optimized deselection of morphologically similar top-quality blastocysts with delayed morphokinetic profiles. Limitations, reasons for caution This study needs to be validated on a larger, multi-centric dataset that takes into account more morphokinetic features associated with ploidy in order to increase the robustness of our algorithm. Wider implications of the findings For the first time, our algorithmic model proposed here demonstrates the utility of an AI tool to deselect top-graded blastocysts that would otherwise be selected for transfer based on conventional morphologic assessment alone. Trial registration number Not Applicable