Impact of GnRH analogues on oocyte/embryo quality and embryo development in in vitro fertilization/intracytoplasmic sperm injection cycles: a case control study

<p>Abstract</p> <p>Background</p> <p>Despite the clinical outcomes of ovarian stimulation with either GnRH-agonist or GnRH-antagonist analogues for in vitro fertilization (IVF) being well analysed, the effect of analogues on oocyte/embryo quality and embryo development is still not known in detail. The aim of this case-control study was to compare the efficacy of a multiple-dose GnRH antagonist protocol with that of the GnRH agonist long protocol with a view to oocyte and embryo quality, embryo development and IVF treatment outcome.</p> <p>Methods</p> <p>Between October 2001 and December 2008, 100 patients were stimulated with human menopausal gonadotrophin (HMG) and GnRH antagonist in their first treatment cycle for IVF or intracytoplasmic sperm injection (ICSI). One hundred combined GnRH agonist + HMG (long protocol) cycles were matched to the GnRH antagonist + HMG cycles by age, BMI, baseline FSH levels and by cause of infertility. We determined the number and quality of retrieved oocytes, the rate of early-cleavage embryos, the morphology and development of embryos, as well as clinical pregnancy rates. Statistical analysis was performed using Wilcoxon's matched pairs rank sum test and McNemar's chi-square test. P < 0.05 was considered statistically significant.</p> <p>Results</p> <p>The rate of cytoplasmic abnormalities in retrieved oocytes was significantly higher with the use of GnRH antagonist than in GnRH agonist cycles (62.1% vs. 49.9%; P < 0.01). We observed lower rate of zygotes showing normal pronuclear morphology (49.3% vs. 58.0%; P < 0.01), and higher cell-number of preembryos on day 2 after fertilization (4.28 vs. 4.03; P < 0.01) with the use of GnRH antagonist analogues. The rate of mature oocytes, rate of presence of multinucleated blastomers, amount of fragmentation in embryos and rate of early-cleaved embryos was similar in the two groups. Clinical pregnancy rate per embryo transfer was lower in the antagonist group than in the agonist group (30.8% vs. 40.4%) although this difference did not reach statistical significance (P = 0.17).</p> <p>Conclusion</p> <p>Antagonist seemed to influence favourably some parameters of early embryo development dynamics, while other morphological parameters seemed not to be altered according to GnRH analogue used for ovarian stimulation in IVF cycles.</p

Impact of highly purified versus recombinant follicle stimulating hormone on oocyte quality and embryo development in intracytoplasmic sperm injection cycles

The quality of oocytes and developing embryos are the most relevant factors determining the success of an in vitro fertilization (IVF) treatment. However, there are very few studies analyzing the effects of different gonadotrophin preparations on oocyte and embryo quality. A retrospective secondary analysis of data collected from a prospective randomized study was performed to compare highly purified versus recombinant follicle stimulating hormone (HP-FSH vs. rFSH). The main outcome measures were quantity and quality of oocytes and embryos, dynamics of embryo development, cryopreservation, clinical pregnancy and live birth rate. The number of retrieved and of mature (MII) oocytes showed no significant differences. Fertilization rate was significantly higher in the HP-FSH group (68.9% vs. 59.9%, p = 0.01). We also found significantly higher rate of cryopreserved embryos per all retrieved oocytes (23.4% vs. 14.5%, p = 0.002) in the HP-FSH group. There were no significant differences in clinical pregnancy and in live birth rates. Oocytes obtained with HP-FSH stimulation showed higher fertilisability, whereas pregnancy and live birth rates did not differ between the groups. However, patients treated with HP-FSH may benefit from the higher rate of embryos capable for cryopreservation, suggesting that cumulative pregnancy rates might be higher in this group

Importance of cytoplasmic granularity of human oocytes in In vitro fertilization treatments

The aim of this study was to examine the effect of different stimulation protocols on oocyte granularity and to determine the influence of cytoplasmic granularity on further embryo development. A total of 2448 oocytes from 393 intracytoplasmic sperm injection (ICSI) cycles were analysed retrospectively. Oocytes were classified into 5 groups according to cytoplasmic granularity. (A) no granule or 1–2 small (5 μm); (D) refractile body; (E) dense centrally located granular area. Correlation between characteristics of hormonal stimulation, oocyte granularity and embryo development was analysed. The occurrence of cytoplasmic granularity was influenced by the patient’s age and characteristics of stimulation. The type of granulation had no effect on fertilization rate and zygote morphology. However, some type of granulation resulted in a lower cleavage rate and more fragmented embryos. Our results provided additional information on how hormonal stimulation affects oocyte quality. While cytoplasmic granularity seems not to have an effect on fertilization and embryo development, the presence of refractile body in the oocyte is associated with reduced cleavage rates and impaired embryo development

Correlation between first polar body morphology and further embryo development

First polar body (PB) morphology of human oocytes can indicate further embryo development and viability. However, controversial data have been published in this topic. Our retrospective study analyses the fertilization and further development of oocytes in relation to different morphological features of the first PB. The morphology of 3387 MII oocytes from 522 in vitro fertilization (IVF) treatments were assessed before intracytoplasmic sperm injection (ICSI). Oocytes were classified according to their first PB morphology. Assessment of fertilization and embryonic development (cell number, embryo grade, amount of anuclear fragmentation and presence of multinucleated blastomeres) was performed 16-20 and 42-48 hours after ICSI. Our results show that fertilization rate and embryo quality is influenced by PB morphology, while speed of development is not affected by the morphology of the first PB. Contrary to previous findings, our results suggest that oocytes with a fragmented PB had a higher developmental ability than those with an intact PB. However, we observed a lower viability of oocytes with a large PB. Since there are contradictions in this and previous observations, an extensive study is needed with standard hormonal stimulation protocol and oocyte evaluation criteria

O-285 Artificial intelligence algorithms reach expert-level accuracy in automated grading of blastocyst morphology assessment based on static embryo images and Gardner criteria

Abstract Study question Can artificial intelligence (AI) algorithms reach expert-level accuracy in blastocyst morphology assessment according to Gardner criteria? Summary answer The prediction accuracy of the best performing AI algorithm (Deit), outperformed human-level mean accuracies compared to an embryologist majority vote for all Gardner morphological criteria. What is known already Routinely, morphological grading of blastocysts is performed visually according to Gardner criteria, which suggest expansion (EXP), quality of inner cell mass (ICM), and trophectoderm (TE) as key parameters to predict treatment outcome. Consequently, blastocyst scoring is prone to inter-and intra-observer variability, which may lead to inconsistencies in selecting blastocysts for transfer. AI-based algorithms may help to improve treatment outcome predictability, as it has been suggested recently. In those studies, parameters such as blastocyst quality or stage were annotated by experts from static or time-lapse-derived blastocyst images, to train AI algorithms, e.g. XCeption or YOLO, and compare them to human annotators. Study design, size, duration This retrospective study involves 2,270 images from 837 patients collected over a period of four years in a university IVF clinic. Participants/materials, setting, methods All images were annotated by one senior embryologist and divided into a training and a balanced test set. Subsequently, eight embryologists labeled 300 test set images such that every single image was seen by at least four embryologists. Annotators diverging from the ensemble vote for more than one standard deviation were excluded (n = 2) to set the ground truth labels. Finally, three AI architectures (XCeption, Swin, Deit) were trained and evaluated on that particular ground truth. Main results and the role of chance Out of nine annotators, labelling accuracy of two embryologists diverged from the consensus vote for more than one standard deviation for at least one of the three Gardner criteria. The consensus vote was built from the remaining seven annotators (mean accuracy EXP 0.81, ICM 0.70, TE 0.67). The Swin architecture outperformed the mean expert accuracy for all three criteria (EXP 0.82, ICM 0.76, TE 0.68), while the Deit and the XCeption architecture outperformed the mean expert accuracy in ICM accuracy (Deit 0.72, XCeption 0.73), and performed equal or worse in EXP and TE accuracy (Deit EXP 0.77, ICM 0.73; XCeption EXP 0.77, TE 0.66). When compared to a recent study conducted on time-lapse imaging data using AI algorithms, all our models outperform the ICM accuracy and achieve comparable TE accuracy. To minimize the role of chance in calculating the models' prediction accuracies, the SWA-Gaussian (SWAG) algorithm was used. SWAG is a method to reflect and calibrate uncertainty representation in Bayesian deep learning. It is based on modelling a Gaussian distribution for each networks' weight and applying it as a posterior over all neural network weights to perform Bayesian model averaging. Limitations, reasons for caution To reflect a real IVF lab scenario, embryologists of different origins and levels of experience were involved and no scoring training was offered to the participants. These facts could have potentially negatively affected the degree of consensus, although we excluded two annotators diverging from the mean labeling accuracy. Wider implications of the findings In the past, AI algorithms proved to reliably differentiate between good and bad prognosis blastocysts but not necessarily between blastocysts of similar quality. Further AI-supported differentiation on the basis of expansion and cell lineages will facilitate the ranking of blastocysts and would bring automated scoring closer to clinical application. Trial registration number Not applicable. </jats:sec