Blastocyst transfer after extended culture of cryopreserved cleavage embryos improves in vitro fertilization cycle outcomes

Molecular profiling of human blastocysts reveals primitive endoderm defects among embryos of decreased implantation potential

Human embryo implantation is remarkably inefficient, and implantation failure remains among the greatest obstacles in treating infertility. Gene expression data from human embryos have accumulated rapidly in recent years; however, identification of the subset of genes that determine successful implantation remains a challenge. We leverage clinical morphologic grading-known for decades to correlate with implantation potential-and transcriptome analyses of matched embryonic and abembryonic samples to identify factors and pathways enriched and depleted in human blastocysts of good and poor morphology. Unexpectedly, we discovered that the greatest difference was in the state of extraembryonic primitive endoderm (PrE) development, with relative deficiencies in poor morphology blastocysts. Our results suggest that implantation success is most strongly influenced by the embryonic compartment and that deficient PrE development is common among embryos with decreased implantation potential. Our study provides a valuable resource for those investigating the markers and mechanisms of human embryo implantation.

The optimal frozen embryo transfer strategy for the recurrent implantation failure patient without blastocyst freezing: thawing day 3 embryos and culturing to day 5 blastocysts

This study aimed to investigate the optimal frozen embryo transfer (FET) strategy for recurrent implantation failure (RIF) patients with three consecutive failed cleaved embryo implantations and no blastocyst preservation. This retrospective analysis was divided into three groups based on the FET strategy: thawed day 3 embryo transfer (D3 FET group); and extended culture of frozen-thawed day 3 embryos to day 5 blastocysts transfer (D3-D5 FET group); thawed blastocyst transfer (D5 FET group). Transplant cycle data were compared between the three groups. In total, 43.8% of vitrified-thawed cleavage embryos developed into blastocysts. Analysis of the three transplantation strategies showed that, compared with the D3 FET group, D3-D5 had a significantly better hCG-positivity rate and live-birth rate (P < 0.05). Pregnancy outcomes in the D3-D5 FET group and D5 FET group were similar regarding hCG-positivity rate, implantation rate, clinical pregnancy rate, and live-birth rate. Our findings propose two potentially valuable transfer strategies for patients experiencing repeated implantation failures. The D3-D5 FET approach presents a greater potential for selecting promising embryos in cases without blastocyst preservation; however, this strategy does entail the risk of cycle cancellation. Conversely, in instances where blastocyst preservation is an option, prioritizing consideration of the D5 FET strategy is recommended.

Detecting Blastocyst Components by Artificial Intelligence for Human Embryological Analysis to Improve Success Rate of In Vitro Fertilization

Morphological attributes of human blastocyst components and their characteristics are highly correlated with the success rate of in vitro fertilization (IVF). Blastocyst component analysis aims to choose the most viable embryos to improve the success rate of IVF. The embryologist evaluates blastocyst viability by manual microscopic assessment of its components, such as zona pellucida (ZP), trophectoderm (TE), blastocoel (BL), and inner cell mass (ICM). With the success of deep learning in the medical diagnosis domain, semantic segmentation has the potential to detect crucial components of human blastocysts for computerized analysis. In this study, a sprint semantic segmentation network (SSS-Net) is proposed to accurately detect blastocyst components for embryological analysis. The proposed method is based on a fully convolutional semantic segmentation scheme that provides the pixel-wise classification of important blastocyst components that help to automatically check the morphologies of these elements. The proposed SSS-Net uses the sprint convolutional block (SCB), which uses asymmetric kernel convolutions in combination with depth-wise separable convolutions to reduce the overall cost of the network. SSS-Net is a shallow architecture with dense feature aggregation, which helps in better segmentation. The proposed SSS-Net consumes a smaller number of trainable parameters (4.04 million) compared to state-of-the-art methods. The SSS-Net was evaluated using a publicly available human blastocyst image dataset for component segmentation. The experimental results confirm that our proposal provides promising segmentation performance with a Jaccard Index of 82.88%, 77.40%, 88.39%, 84.94%, and 96.03% for ZP, TE, BL, ICM, and background, with residual connectivity, respectively. It is also provides a Jaccard Index of 84.51%, 78.15%, 88.68%, 84.50%, and 95.82% for ZP, TE, BL, ICM, and background, with dense connectivity, respectively. The proposed SSS-Net is providing a mean Jaccard Index (Mean JI) of 85.93% and 86.34% with residual and dense connectivity, respectively; this shows effective segmentation of blastocyst components for embryological analysis.