Comparison of euploid blastocyst expansion with subgroups of single chromosome, multiple chromosome, and segmental aneuploids using an AI platform from donor egg embryos.

PURPOSE: This retrospective observational study compares how different classes of blastocyst genotypes from egg donor cycles differentially blastulate and expand using a standard assay. METHODS: Quantitative measurements of expansion utilized a customized neural network that segments all sequential time-lapse images during the first 10 h of expansion. RESULTS: Analyses were performed using two developmental time perspectives using time-lapse imaging. The first was the time to blastocyst formation (tB), which broadly reflects variations in developmental rate. Euploidy peaked at 100-115 h from fertilization. In contrast, aneuploidy peaks flanked this interval bi-modally. These distributions limit ploidy discrimination based upon traditional standard grading features when assessed in real time. In contrast, from the second perspective of progressive blastocyst expansion that is normalized to each individual blastocyst's tB time, euploidy was significantly increased at expansion values > 20,000µ2 across all tB intervals studied. A Cartesian coordinate plot graphically summarizes information useful to rank order blastocysts within cohorts for transfer. Defined aneuploidy subgroups, distinguished by the number and complexity of chromosomes involved, also showed distributive differences from both euploids and from each other. A small subset of clinically significant trisomies did not show discriminating features separating them from other euploids. CONCLUSION: A standard assay of blastocyst expansion normalized to each individual blastocyst's time of blastocyst formation more usefully discriminates euploidy from aneuploidy than real-time expansion comparisons using absolute developmental time from fertilization.

Deep learning neural network analysis of human blastocyst expansion from time-lapse image files.

RESEARCH QUESTION: Can artificial intelligence (AI) discriminate a blastocyst's cellular area from unedited time-lapse image files using semantic segmentation and a deep learning optimized U-Net architecture for use in selecting single blastocysts for transfer? DESIGN: This platform was retrospectively applied to time-lapse files from 101 sequentially transferred single blastocysts that were prospectively selected for transfer by their highest expansion ranking within cohorts using a 10 h expansion assay rather than standard grading. RESULTS: The AI platform provides expansion curves and raw data files to classify and compare blastocyst phenotypes within both cohorts and populations. Of 35 sequential unbiopsied single blastocyst transfers, 23 (65.7%) resulted in a live birth. Of 66 sequential single euploid blastocyst transfers, also selected for their most robust expansion, 49 (74.2%) resulted in live birth. The AI platform revealed that the averaged expansion rate was significantly (P = 0.007) greater in euploid blastocysts that resulted in live births compared with those resulting in failure to give a live birth. The platform further provides a framework to analyse fragmentation phenotypes that can test new hypotheses for developmental regulation during the preimplantation period. CONCLUSIONS: AI can be used to quantitatively describe blastocyst expansion from unedited time-lapse image files and can be used to quantitatively rank-order blastocysts for transfer. Early clinical results from such single blastocyst selection suggests that live birth rates without biopsy may be comparable to those found using single euploid blastocysts in younger, good responder patients.

Early blastocyst expansion in euploid and aneuploid human embryos: evidence for a non-invasive and quantitative marker for embryo selection.

RESEARCH QUESTION: How can the kinetics of human blastocyst expansion be used to evaluate an embryo's ploidy identified using preimplantation genetic testing for aneuploidy (PGT-A)? DESIGN: This was a retrospective observational study of 188 autologous blastocysts from 34 sequential treatment cycles using PGT-A and blastocyst biopsy. Using time-lapse imaging, blastocyst expansion was evaluated using a quantitative standardized expansion assay (qSEA). Trophectoderm cell division was examined in selected, unbiopsied embryos (n = 7) to evaluate the contribution of mitosis to the expansion rate. RESULTS: The averaged euploid blastocyst expansion rate was significantly (52.8%) faster than in aneuploid blastocysts (P = 0.0041). Scatterplots, representing 'expansion maps', revealed that both populations showed a similarly overlapping distribution of blastocyst formation times at 80-140 h from fertilization. Euploidy and aneuploidy were better distinguished in regions of higher and lower expansion, respectively, in expansion maps. Based upon the expansion slopes, rank-ordering of individual embryos within cohorts resulted in more than 90% euploid embryos in the first two ranks in patients less than 35 years of age. Additional detailed time-lapse image analysis provided evidence that rapid expansion was associated with robust, integrative cellular mitosis in trophectoderm cells. CONCLUSIONS: The kinetics of human blastocyst expansion are related to an embryo's ploidy. These preliminary observations describe a new quantitative, non-invasive approach to embryo assessment that may be useful to identify single blastocysts for transfer, particularly in younger patient groups. However, this approach may also be useful for euploid embryo selection after PGT-A. The results support the hypothesis that aneuploidy universally impairs general cellular processes, including cell division, in differentiated cells.