Morphological and morphokinetic associations with aneuploidy: a systematic review and meta-analysis

Prediction of embryo euploidy and pregnancy outcome by blastocyst morphology and development speed for women receiving single embryo transfer

BACKGROUND

Selection of high-quality blastocysts is the most important factor determining the success of assisted reproductive technology. The objective of this study is to assess the values of blastocyst morphological quality and development speed for predicting euploidy and clinical pregnancy outcome.

METHODS

A total of 155 preimplantation genetic testing cycles including 959 blastocysts and 154 euploid blastocyst transfer cycles conducted between January 2018 and December 2019 were retrospectively analysed. The associations of blastocyst morphological quality and development speed (D) with chromosomal status, clinical pregnancy rate, early miscarriage rate, and ongoing pregnancy rate were evaluated by univariate and multivariate regression.

RESULTS

The euploidy rate of development speed D5 blastocysts was significantly greater than that of D6 blastocysts (61.4% vs. 38.1%, P < 0.001), and the euploid rate of morphologically high-grade blastocysts was significantly greater than that of non-high-grade blastocysts. Development speed D5 (OR = 1.6, 95% CI 1.2-2.2, P = 0.02) and high-grade morphology (OR = 2.1, 95% CI 1.5-2.9, P = 0.01) were independent predictors of euploidy. The ongoing pregnancy rate of D5 blastocysts was significantly higher than that of D6 blastocysts (62.3% vs. 43.8%, P = 0.04). Transfer of euploid blastocysts with high-grade morphology resulted in a greater ongoing pregnancy rate than transfer of non-high-grade euploid blastocysts (60.7% vs. 43.2%, P = 0.049). Alternatively, D6 development speed was an independent risk factor for early pregnancy loss after euploid blastocyst transfer. Multivariate regression analysis adjusting for confounding factors identified maternal age, blastocyst development speed, and blastocyst morphological grade as independent predictors of euploidy but not of clinical pregnancy.

CONCLUSION

The recommended sequence of embryo transfer based on the present study is D5 high-grade > D6 high-grade > D5 non-high-grade > D6 non-high-grade.

Embryo multinucleation: detection, possible origins, and implications for treatment

Cell cycle regulation is crucial to assure expansion of a cell population, while preserving genome integrity. This notion is especially relevant to fertilization and early embryo development, a time when the cell cycle transforms from meiotic into mitotic cycles. Zygote-to-embryo transition is acutely error-prone, causing major developmental perturbations, including cleavage delays, tri- and multi-chotomous cleavages, and cell fragmentation. Another such alteration is bi- and multinucleation, consisting of the simultaneous formation of two or more nuclei at interphase. Indeed, multinucleation affects a large proportion of early human embryos, typically at the two-cell stage. Mechanistically, several factors, including spindle dysfunction, failed cleavage, and cell fusion, may generate this cell anomaly. In assisted reproduction treatment, multinucleation is associated with reduced developmental rates and lower implantation rates in Days 2-3 embryo transfers. However, many multinucleated embryos can develop to the blastocyst stage. In blastocyst transfers, the current evidence does not suggest a major impact of a previous history of multinucleation on the odds of euploidy or successful treatment outcomes. Human embryo multinucleation remains a not-fully-understood but developmentally relevant and intriguing phenomenon which requires further research of its generative mechanisms and clinical implications.

Optimizing single blastocyst selection: the role of day 3 embryo morphology in vitrified-warmed blastocyst transfer cycles

RESEARCH QUESTION

Can day 3 embryo morphology serve as an independent criterion for optimal single blastocyst selection?

DESIGN

This retrospective, single-centre cohort study included 1517 single vitrified-warmed blastocyst transfer (SVBT) cycles conducted between October 2019 and July 2022. The live birth rate (LBR) and other clinical outcomes of SVBT cycles were evaluated, considering both good-quality and non-good-quality day 3 embryos. The associations of day 3 morphological characteristics, encompassing number of blastomeres and embryo grade, were assessed. Multivariable analyses were undertaken using multiple models adjusted for day of blastocyst development and blastocyst grade.

RESULTS

Blastocysts from good-quality day 3 embryos had significantly higher LBR compared with those from non-good-quality embryos for both day 5 (51.5% versus 42.9%; P = 0.013) and day 6 (25.1% versus 17.6%; P = 0.018) blastocysts. LBR did not differ significantly with number of blastomeres on day 3, regardless of day of blastocyst development (day 5/6) or blastocyst grade. LBR varied significantly by day 3 embryo grade for both day 5 (48.0%, 51.5%, 46.6% and 32.7% for grades I, II, III and IV-V; P = 0.005) and day 6 (41.5%, 23.6%, 15.9% and 16.1% for grades I, II, III and IV-V; P = 0.001) blastocysts. Multivariable logistic regression revealed that non-good-quality embryos and lower morphological grade (IV-V) on day 3 were significantly and negatively correlated with LBR, while the number of blastomeres on day 3 was not an independent factor.

CONCLUSIONS

When selecting blastocysts of equal quality for SVBT cycles, those with higher day 3 morphological scores are preferred. Day 3 morphological evaluation is a valuable supplement to conventional selection methods.

Non-invasively predicting euploidy in human blastocysts via quantitative 3D morphology measurement: a retrospective cohort study

BACKGROUND

Blastocyst morphology has been demonstrated to be associated with ploidy status. Existing artificial intelligence models use manual grading or 2D images as the input for euploidy prediction, which suffer from subjectivity from observers and information loss due to incomplete features from 2D images. Here we aim to predict euploidy in human blastocysts using quantitative morphological parameters obtained by 3D morphology measurement.

METHODS

Multi-view images of 226 blastocysts on Day 6 were captured by manually rotating blastocysts during the preparation stage of trophectoderm biopsy. Quantitative morphological parameters were obtained by 3D morphology measurement. Six machine learning models were trained using 3D morphological parameters as the input and PGT-A results as the ground truth outcome. Model performance, including sensitivity, specificity, precision, accuracy and AUC, was evaluated on an additional test dataset. Model interpretation was conducted on the best-performing model.

RESULTS

All the 3D morphological parameters were significantly different between euploid and non-euploid blastocysts. Multivariate analysis revealed that three of the five parameters including trophectoderm cell number, trophectoderm cell size variance and inner cell mass area maintained statistical significance (P < 0.001, aOR = 1.054, 95% CI 1.034-1.073; P = 0.003, aOR = 0.994, 95% CI 0.991-0.998; P = 0.010, aOR = 1.003, 95% CI 1.001-1.006). The accuracy of euploidy prediction by the six machine learning models ranged from 80 to 95.6%, and the AUCs ranged from 0.881 to 0.984. Particularly, the decision tree model achieved the highest accuracy of 95.6% (95% CI 84.9-99.5%) with the AUC of 0.978 (95% CI 0.882-0.999), and the extreme gradient boosting model achieved the highest AUC of 0.984 (95% CI 0.892-1.000) with the accuracy of 93.3% (95% CI 81.7-98.6%). No significant difference was found between different age groups using either decision tree or extreme gradient boosting to predict euploid blastocysts. The quantitative criteria extracted from the decision tree imply that euploid blastocysts have a higher number of trophectoderm cells, larger inner cell mass area, and smaller trophectoderm cell size variance compared to non-euploid blastocysts.

CONCLUSIONS

Using quantitative morphological parameters obtained by 3D morphology measurement, the decision tree-based machine learning model achieved an accuracy of 95.6% and AUC of 0.978 for predicting euploidy in Day 6 human blastocysts.

TRIAL REGISTRATION

N/A.

The density of the inner cell mass is a new indicator of the quality of a human blastocyst: a valid supplement to the Gardner scoring system

STUDY QUESTION

Can the density of the inner cell mass (ICM) be a new indicator of the quality of the human blastocyst?

SUMMARY ANSWER

The densification index (DI) developed in this study can quantify ICM density and provide positive guidance for ploidy, pregnancy, and live birth.

WHAT IS KNOWN ALREADY

In evaluating the quality of ICM, reproductive care clinics still use size indicators without further evaluation. The main disadvantage of this current method is that the evaluation of blastocyst ICM is relatively rough and cannot meet the needs of clinical embryologists, especially when multiple blastocysts have the same ICM score, which makes them difficult to evaluate further.

STUDY DESIGN, SIZE, DURATION

This observational study included data from 2272 blastocysts in 1991 frozen-thawed embryo transfer (FET) cycles between January 2018 to November 2021 and 1105 blastocysts in 430 preimplantation genetic testing cycles between January 2019 and February 2023.

PARTICIPANTS/MATERIALS, SETTING, METHODS

FET, ICSI, blastocyst culture, trophectoderm biopsy, time-lapse (TL) monitoring, and next-generation sequencing were performed. After preliminary sample size selection, the 11 focal plane images captured by the TL system were normalized and the spatial frequency was used to construct the DI of the ICM.

MAIN RESULTS AND THE ROLE OF CHANCE

This study successfully constructed a quantitative indicator DI that can reflect the degree of ICM density in terms of fusion and texture features. The higher the DI value, the better the density of the blastocyst ICM, and the higher the chances that the blastocyst was euploid (P < 0.001) and that pregnancy (P < 0.001) and live birth (P = 0.005) were reached. In blastocysts with ICM graded B and blastocysts graded 4BB, DI was also positively associated with ploidy, pregnancy, and live birth (P < 0.05). ROC analysis showed that combining the Gardner scoring system with DI can more effectively predict pregnancy and live births, when compared to using the Gardner scoring system alone.

LIMITATIONS, REASONS FOR CAUTION

Accurate calculation of the DI value places high demands on image quality, requiring manual selection of the clearest focal plane and exposure control. Images with the ICM not completely within the field of view cannot be used. The association between the density of ICM and chromosomal mosaicism was not evaluated. The associations between the density of ICM and different assisted reproductive technologies and different culture conditions in embryo laboratories were also not evaluated. Prospective studies are needed to further investigate the impact of ICM density on clinical outcomes.

WIDER IMPLICATIONS OF THE FINDINGS

ICM density assessment is a new direction in blastocyst assessment. This study explores new ways of assessing blastocyst ICM density and develops quantitative indicators and a corresponding qualitative evaluation scheme for ICM density. The DI of the blastocyst ICM developed in this study is easy to calculate and requires only TL equipment and image processing, providing positive guidance for clinical outcomes. The qualitative evaluation scheme of ICM density can assist embryologists without TL equipment to manually evaluate ICM density. ICM density is a simple indicator that can be used in practice and is a good complement to the blastocyst scoring systems currently used in most centers.

STUDY FUNDING/COMPETING INTEREST(S)

This work was supported by the National Key Research & Development Program of China (2021YFC2700603). The authors report no financial or commercial conflicts of interest.

TRIAL REGISTRATION NUMBER

N/A.

Beyond black-box models: explainable AI for embryo ploidy prediction and patient-centric consultation

PURPOSE

To determine if an explainable artificial intelligence (XAI) model enhances the accuracy and transparency of predicting embryo ploidy status based on embryonic characteristics and clinical data.

METHODS

This retrospective study utilized a dataset of 1908 blastocyst embryos. The dataset includes ploidy status, morphokinetic features, morphology grades, and 11 clinical variables. Six machine learning (ML) models including Random Forest (RF), Linear Discriminant Analysis (LDA), Logistic Regression (LR), Support Vector Machine (SVM), AdaBoost (ADA), and Light Gradient-Boosting Machine (LGBM) were trained to predict ploidy status probabilities across three distinct datasets: high-grade embryos (HGE, n = 1107), low-grade embryos (LGE, n = 364), and all-grade embryos (AGE, n = 1471). The model's performance was interpreted using XAI, including SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) techniques.

RESULTS

The mean maternal age was 38.5 ± 3.85 years. The Random Forest (RF) model exhibited superior performance compared to the other five ML models, achieving an accuracy of 0.749 and an AUC of 0.808 for AGE. In the external test set, the RF model achieved an accuracy of 0.714 and an AUC of 0.750 (95% CI, 0.702-0.796). SHAP's feature impact analysis highlighted that maternal age, paternal age, time to blastocyst (tB), and day 5 morphology grade significantly impacted the predictive model. In addition, LIME offered specific case-ploidy prediction probabilities, revealing the model's assigned values for each variable within a finite range.

CONCLUSION

The model highlights the potential of using XAI algorithms to enhance ploidy prediction, optimize embryo selection as patient-centric consultation, and provides reliability and transparent insights into the decision-making process.

Preimplantation genetic testing for aneuploidy in unexplained recurrent pregnancy loss: a systematic review and meta-analysis

IMPORTANCE

Preimplantation genetic testing for aneuploidy (PGT-A) to deselect aneuploid embryos in assisted reproductive technology (ART) treatment cycles may hold promise by augmenting pregnancy rates per transfer and reducing pregnancy loss rates for patients with unexplained recurrent pregnancy loss (RPL).

OBJECTIVE

To explore effectiveness of PGT-A in managing unexplained RPL by evaluating several key aspects: the likelihood of live birth in a subsequent spontaneous pregnancy, whether women with unexplained RPL have a higher rate of aneuploidy, whether euploid blastocysts offer comparable live birth rate (LBR) in patients with unexplained RPL, whether the endometrium is less selective in unexplained RPL loss, and whether PGT-A increases the LBR or reduces pregnancy losses until delivery.

DATA SOURCES

PubMed and Cochrane Library databases were searched from inception until June 2024.

STUDY SELECTION AND SYNTHESIS

Studies involving patients with ≥2 unexplained RPL who underwent ART with or without PGT-A or expectant management were included.

MAIN OUTCOME MEASURES

The primary outcome measure was the LBR. Secondary outcome measures were aneuploidy rate, clinical pregnancy rate, and clinical pregnancy loss rate.

RESULTS

Whether couples with unexplained RPL have higher embryo aneuploidy rates remains equivocal. Euploid blastocyst transfers yielded comparable clinical pregnancy loss rate (odds ratio [OR], 1.10; 95% confidence interval [CI], 0.57-2.13) and LBR (OR, 1.04; 95% CI, 0.74-1.44) in patients with and without unexplained RPL. Comprehensive chromosome analysis of products of conception shows similar aneuploidy rates between patients with and without RPL and does not support the less selective endometrium hypothesis. Preimplantation genetic testing for aneuploidy decreased clinical pregnancy loss rate (OR, 0.42; 95% CI, 0.27-0.67) and enhanced LBR per transfer (OR, 2.17; 95% CI, 1.77-2.65) and LBR per patient (OR, 1.85; 95% CI, 1.18-2.91) in patients with unexplained RPL.

CONCLUSION AND RELEVANCE

Current low-quality evidence suggests that PGT-A enhances LBR per transfer and per patient in unexplained RPL. Well-designed randomized controlled trials comparing ART with PGT-A vs. expectant management for unexplained RPL are warranted.

CLINICAL TRIAL REGISTRATION NUMBER

CRD42021291546.

The synergy of morphokinetic parameters and sHLA-G in cleavage embryo enhancing implantation rates

Objective: This study aimed to assess the relationship between implantation and soluble HLA-G (sHLA-G) expression in cleavage embryo culture medium (ECM) in conjunction with early developmental kinetics determined by time-lapse imaging (TLI). Methods: A retrospective, single-center study was conducted involving 238 embryos from 165 patients who underwent Frozen-thawed embryo transfer (FET) using autologous oocytes, with either single or double embryo transfer. TLI morphokinetic parameters (t2, t3, t4, t5, t6, t7, t8, cc2, s2, cc3, s3) of embryos were analyzed, and sHLA-G levels in D3 ECM were measured using an enzyme-linked immunosorbent assay (ELISA). A hierarchical classification model was developed to categorize embryos into five groups (A, B, C, D, E). The correlation between sHLA-G levels, TLI classification of embryos, and embryo implantation was investigated to establish a non-invasive method for evaluating implantation potential. Multivariate logistic regression analysis was performed to identify potential influencing factors, and receiver operating characteristic (ROC) curves were used to evaluate the predictive value for implantation. Results: Multivariate unconditional logistic regression analysis indicated that TLI parameters t5 and s3 and sHLA-G level in ECM were independent risk factors affecting embryo implantation. The implantation rate decreased from TLI classification A to E. The proposed classification model effectively assessed the implantation potential of embryos. The implantation rate was higher in the sHLA-G positive group compared to the sHLA-G negative group (p < 0.001). The expression of sHLA-G in D3 ECM, combined with the TLI classification model, accurately evaluated the implantation potential of embryos with an AUC of 0.876. Conclusion: The integration of cleavage kinetics and embryonic sHLA-G expression could reliably identify embryos with a high likelihood of successful implantation.

Multiple collapses of blastocysts after full blastocyst formation is an independent risk factor for aneuploidy - a study based on AI and manual validation

BACKGROUND

The occurrence of blastocyst collapse may become an indicator of preimplantation embryo quality assessment. It has been reported that collapsing blastocysts can lead to higher rates of aneuploidy and poorer clinical outcomes, but more large-scale studies are needed to explore this relationship. This study explored the characteristics of blastocyst collapse identified and quantified by artificial intelligence and explored the associations between blastocyst collapse and embryo ploidy, morphological quality, and clinical outcomes.

METHODS

This observational study included data from 3288 biopsied blastocysts in 1071 time-lapse preimplantation genetic testing cycles performed between January 2019 and February 2023 at a single academic fertility center. All transferred blastocysts are euploid blastocysts. The artificial intelligence recognized blastocyst collapse in time-lapse microscopy videos and then registered the collapsing times, and the start time, the recovery duration, the shrinkage percentage of each collapse. The effects of blastocyst collapse and embryo ploidy, pregnancy, live birth, miscarriage, and embryo quality were studied using available data from 1196 euploid embryos and 1300 aneuploid embryos.

RESULTS

5.6% of blastocysts collapsed at least once only before the full blastocyst formation (tB), 19.4% collapsed at least once only after tB, and 3.1% collapsed both before and after tB. Multiple collapses of blastocysts after tB (times ≥ 2) are associated with higher aneuploid rates (54.6%, P > 0.05; 70.5%, P < 0.001; 72.5%, P = 0.004; and 71.4%, P = 0.049 in blastocysts collapsed 1, 2, 3 or ≥ 4 times), which remained significant after adjustment for confounders (OR = 2.597, 95% CI 1.464-4.607, P = 0.001). Analysis of the aneuploid embryos showed a higher ratio of collapses and multiple collapses after tB in monosomies and embryos with subchromosomal deletion of segmental nature (P < 0.001). Blastocyst collapse was associated with delayed embryonic development and declined blastocyst quality. There is no significant difference in pregnancy and live birth rates between collapsing and non-collapsing blastocysts.

CONCLUSIONS

Blastocyst collapse is common during blastocyst development. This study underlined that multiple blastocyst collapses after tB may be an independent risk factor for aneuploidy which should be taken into account by clinicians and embryologists when selecting blastocysts for transfer.

The potential of self- supervised learning in embryo selection for IVF success

How to select the "best" embryo for transfer is a long-standing question in clinical in vitro fertilization (IVF). Wang et al. proposed a multi-modal self-supervised learning framework for human embryo selection with a high accuracy and generalization ability.

Total duration of spontaneous blastocyst collapse during the expansion stage is an independent predictor of euploidy and live birth rates

RESEARCH QUESTION

Is the total duration of spontaneous blastocyst collapse to re-expansion before biopsy related to ploidy and live birth rates after single euploid blastocyst transfer?

DESIGN

This was a retrospective cohort study of 600 preimplantation genetic testing cycles for aneuploidy (PGT-A) cycles, involving 2203 biopsied blastocysts, at a large reproductive medicine centre. Features of spontaneous blastocyst collapse from full to expanded stage, before biopsy, were observed using an embryoscope viewer for embryos cultured in a time-lapse incubator. In total, 568 cycles of frozen blastocyst transfers, either single euploid or mosaic, were performed. Correlations between collapse features and PGT-A outcomes were evaluated, as well as live birth rate, following euploid embryo transfer.

RESULTS

Blastocysts with lower morphological quality or delayed development had significantly higher rates of collapse, multiple collapses, and a longer duration of collapse to re-expansion. After controlling for confounders, such as oocyte age, morphological quality of blastocyst, and day of biopsy, multivariate logistic regression revealed that the total duration of collapse to re-expansion was an independent predictor of lower euploidy rate; the multivariate OR was 0.85 (95% CI 0.77-0.95; P = 0.00). Furthermore, even with euploid embryo transfer, the probability of a live birth decreased as the total duration of collapse to re-expansion increased; the multivariate OR was 0.79 (95% CI 0.64-0.98; P = 0.033).

CONCLUSION

The total duration of blastocyst collapse to re-expansion could be used as a predictor of lower euploidy and live birth rate. When developing blastocyst algorithms for pregnancy prediction, the duration of spontaneous blastocyst collapse should be included as a significant variable.

Two types of cleavage, from zygote to three cells, result in different clinical outcomes and should be treated differently

Research Question

What is the utilization rate of embryos that exert inadequate zygote cleavage into three daughter cells?

Design

This study used a retrospective dataset from a single IVF Unit. A total of 3,060 embryos from 1,811 fresh IVF cycles were analyzed. The cleavage pattern, morphokinetics, and outcome were recorded. Only 2pn embryos, fertilized by ejaculated sperm, and cultured in a time-lapse system for at least 5 days were included. We generated three study groups according to the embryo's cleavage pattern: (I) Control, normal cleavage (n = 551); (II) fast cleavage, zygote to three cells within 5 h (n = 1,587); and (III) instant direct tripolar cleavage (IDC) from zygote to three cells (n = 922).

Results

The rate of usable fast cleavage blastocysts was 108/1,587 (6.81%) and usable control blastocysts was 180/551 (32.67%). The time of PN fading and from fading to first cleavage differed significantly between the three groups. Although the pregnancy rate of control and fast cleavage blastocysts were comparable (40.35% and 42.55%, respectively), the amount of instant direct cleavage embryos that reached blastocyst stage was neglectable (only four embryos out of 922 analyzed IDC embryos) and unsuitable for statistical comparison of pregnancy rates.

Conclusion

Our results indicate the need to culture instant direct cleavage embryos for 5 days, up to the blastocyst stage, and avoid transfer of embryos that are fated to arrest even when their morphological grade on day 3 is acceptable, whereas fast cleavage embryos could be transferred on day 3 when there is no alternative.

Enhancing clinical utility: deep learning-based embryo scoring model for non-invasive aneuploidy prediction

BACKGROUND

The best method for selecting embryos ploidy is preimplantation genetic testing for aneuploidies (PGT-A). However, it takes more labour, money, and experience. As such, more approachable, non- invasive techniques were still needed. Analyses driven by artificial intelligence have been presented recently to automate and objectify picture assessments.

METHODS

In present retrospective study, a total of 3448 biopsied blastocysts from 979 Time-lapse (TL)-PGT cycles were retrospectively analyzed. The "intelligent data analysis (iDA) Score" as a deep learning algorithm was used in TL incubators and assigned each blastocyst with a score between 1.0 and 9.9.

RESULTS

Significant differences were observed in iDAScore among blastocysts with different ploidy. Additionally, multivariate logistic regression analysis showed that higher scores were significantly correlated with euploidy (p < 0.001). The Area Under the Curve (AUC) of iDAScore alone for predicting euploidy embryo is 0.612, but rose to 0.688 by adding clinical and embryonic characteristics.

CONCLUSIONS

This study provided additional information to strengthen the clinical applicability of iDAScore. This may provide a non-invasive and inexpensive alternative for patients who have no available blastocyst for biopsy or who are economically disadvantaged. However, the accuracy of embryo ploidy is still dependent on the results of next-generation sequencing technology (NGS) analysis.

A novel non-invasive embryo evaluation method (NICS-Timelapse) with enhanced predictive precision and clinical impact

The selection of the finest possible embryo in in-vitro fertilization (IVF) was crucial and revolutionary, particularly when just one embryo is transplanted to lessen the possibility of multiple pregnancies. However, practical usefulness of currently used methodologies may be constrained. Here, we established a novel non-invasive embryo evaluation method that combines non-invasive chromosomal screening (NICS) and Timelapse system along with artificial intelligence algorithms. With an area under the curve (AUC) of 0.94 and an accuracy of 0.88, the NICS-Timelapse model was able to predict blastocyst euploidy. The performance of the model was further evaluated using 75 patients in various clinical settings. The clinical pregnancy and live birth rates of embryos predicted by the NICS-Timelapse model, showing that embryos with higher euploid probabilities were associated with higher clinical pregnancy and live birth rates. These results demonstrated the NICS-Timelapse model's significantly wider application in clinical IVF due to its excellent accuracy and noninvasiveness.

Effect of cytoplasmic fragmentation on embryo development, quality, and pregnancy outcome: a systematic review of the literature

The role of cytoplasmic fragmentation in human embryo development and reproductive potential is widely recognized, albeit without standard definition nor agreed upon implication. While fragmentation is best understood to be a natural process across species, the origin of fragmentation remains incompletely understood and likely multifactorial. Several factors including embryo culture condition, gamete quality, aneuploidy, and abnormal cytokinesis seem to have important role in the etiology of cytoplasmic fragmentation. Fragmentation reduces the volume of cytoplasm and depletes embryo of essential organelles and regulatory proteins, compromising the developmental potential of the embryo. While it has been shown that degree of fragmentation and embryo implantation potential are inversely proportional, the degree, pattern, and distribution of fragmentation as it relates to pregnancy outcome is debated in the literature. This review highlights some of the challenges in analysis of fragmentation, while revealing trends in our evolving knowledge of how fragmentation may relate to functional development of the human embryos, implantation, and pregnancy outcome.

How should the best human embryo in vitro be? Current and future challenges for embryo selection

In-vitro fertilization (IVF) aims at overcoming the causes of infertility and lead to a healthy live birth. To maximize IVF efficiency, it is critical to identify and transfer the most competent embryo within a cohort produced by a couple during a cycle. Conventional static embryo morphological assessment involves sequential observations under a light microscope at specific timepoints. The introduction of time-lapse technology enhanced morphological evaluation via the continuous monitoring of embryo preimplantation in vitro development, thereby unveiling features otherwise undetectable via multiple static assessments. Although an association exists, blastocyst morphology poorly predicts chromosomal competence. In fact, the only reliable approach currently available to diagnose the embryonic karyotype is trophectoderm biopsy and comprehensive chromosome testing to assess non-mosaic aneuploidies, namely preimplantation genetic testing for aneuploidies (PGT-A). Lately, the focus is shifting towards the fine-tuning of non-invasive technologies, such as "omic" analyses of waste products of IVF (e.g., spent culture media) and/or artificial intelligence-powered morphologic/morphodynamic evaluations. This review summarizes the main tools currently available to assess (or predict) embryo developmental, chromosomal, and reproductive competence, their strengths, the limitations, and the most probable future challenges.

Mature oocyte dysmorphisms may be associated with progesterone levels, mitochondrial DNA content, and vitality in luteal granulosa cells

PURPOSE

To identify whether follicular environment parameters are associated with mature oocyte quality, embryological and clinical outcomes.

METHODS

This retrospective study examined 303 mature oocytes from 51 infertile women undergoing ICSI cycles between May 2018 and June 2021. Exclusion criteria consisted of advanced maternal age (> 36 years old), premature ovarian failure, obesity in women, or use of frozen gametes. Luteal granulosa cells (LGCs) were analyzed for mitochondrial DNA/genomic (g) DNA ratio and vitality. The relationships between hormone levels in the follicular fluid and oocyte features were assessed. Quantitative morphometric measurements of mature oocytes were assessed, and the association of LGC parameters and oocyte features on live birth rate after single embryo transfer was examined.

RESULTS

Results indicated an inverse correlation between the mtDNA/gDNA ratio of LGCs and the size of polar body I (PBI). A 4.0% decrease in PBI size was observed with each one-unit increase in the ratio (p = 0.04). Furthermore, a 1% increase in LGC vitality was linked to a 1.3% decrease in fragmented PBI (p = 0.03), and a 1 ng/mL increase in progesterone levels was associated with a 0.1% rise in oocytes with small inclusions (p = 0.015). Associations were drawn among LGC characteristics, perivitelline space (PVS) debris, cytoplasmic inclusions, PBI integrity, and progesterone levels. Certain dysmorphisms in mature oocytes were associated with embryo morphokinetics; however, live birth rates were not associated with follicular parameters and oocyte quality characteristics.

CONCLUSION

Follicular markers may be associated with mature oocyte quality features.

When the Embryo Meets the Endometrium: Identifying the Features Required for Successful Embryo Implantation

Evaluation of the optimal number of embryos, their quality, and the precise timing for transfer are critical determinants in reproductive success, although still remaining one of the main challenges in assisted reproduction technologies (ART). Indeed, the success of in vitro fertilization (IVF) treatments relies on a multitude of events and factors involving both the endometrium and the embryo. Despite concerted efforts on both fronts, the overall success rates of IVF techniques continue to range between 25% and 30%. The role of the endometrium in implantation has been recently recognized, leading to the hypothesis that both the "soil" and the "seed" play a central role in a successful pregnancy. In this respect, identification of the molecular signature of endometrial receptivity together with the selection of the best embryo for transfer become crucial in ART. Currently, efforts have been made to develop accurate, predictive, and personalized tests to identify the window of implantation and the best quality embryo. However, the value of these tests is still debated, as conflicting results are reported in the literature. The purpose of this review is to summarize and critically report the available criteria to optimize the success of embryo transfer and to better understand current limitations and potential areas for improvement.

The Association between Embryo Development and Chromosomal Results from PGT-A in Women of Advanced Age: A Prospective Cohort Study

Embryo morphology and morphokinetics have been studied for their association with euploid embryos. However, the results are controversial, especially in the advanced-aged women group, when the risk of aneuploidy increases significantly. This prospective cohort study evaluated the association between embryo development between day-3 cleavage and day-5 blastocyst stages and euploidy rates, determined using preimplantation genetic testing for aneuploidy (PGT-A). Embryos from women aged 35 years and above who underwent intracytoplasmic sperm injections and PGT-A were studied. Day-3 cleavage-stage embryos were evaluated for their cell number, and day-5 blastocyst-stage embryos were evaluated for their morphological grade. Embryo development from day 3 to day 5 was categorized as either good or poor development and evaluated for its association with the PGT-A results. We evaluated 325 embryos from 101 infertile couples. It was found that 55.17% of blastocysts with good development and 29.83% with poor development were euploid. A significant association was found between embryo development and euploidy rates in advanced-aged women (p < 0.001). Also, there were significantly higher rates of euploid embryos with good blastocyst morphological grades, especially blastocyst expansion grades and trophectoderm grades. In conclusion, embryo morphokinetics shows promising results in predicting euploidy in advanced female age.

A temporal extracellular transcriptome atlas of human pre-implantation development

Non-invasively evaluating gene expression products in human pre-implantation embryos remains a significant challenge. Here, we develop a non-invasive method for comprehensive characterization of the extracellular RNAs (exRNAs) in a single droplet of spent media that was used to culture human in vitro fertilization embryos. We generate the temporal extracellular transcriptome atlas (TETA) of human pre-implantation development. TETA consists of 245 exRNA sequencing datasets for five developmental stages. These data reveal approximately 4,000 exRNAs at each stage. The exRNAs of the developmentally arrested embryos are enriched with the genes involved in negative regulation of the cell cycle, revealing an exRNA signature of developmental arrest. Furthermore, a machine-learning model can approximate the morphology-based rating of embryo quality based on the exRNA levels. These data reveal the widespread presence of coding gene-derived exRNAs at every stage of human pre-implantation development, and these exRNAs provide rich information on the physiology of the embryo.

A comparison of morphokinetic models and morphological selection for prioritizing euploid embryos: a multicentre cohort study

STUDY QUESTION

Are morphokinetic models better at prioritizing a euploid embryo for transfer over morphological selection by an embryologist?

SUMMARY ANSWER

Morphokinetic algorithms lead to an improved prioritization of euploid embryos when compared to embryologist selection.

WHAT IS KNOWN ALREADY

PREFER (predicting euploidy for embryos in reproductive medicine) is a previously published morphokinetic model associated with live birth and miscarriage. The second model uses live birth as the target outcome (LB model).

STUDY DESIGN, SIZE, DURATION

Data for this cohort study were obtained from 1958 biopsied blastocysts at nine IVF clinics across the UK from January 2021 to December 2022.

PARTICIPANTS/MATERIALS, SETTING, METHODS

The ability of the PREFER and LB models to prioritize a euploid embryo was compared against arbitrary selection and the prediction of four embryologists using the timelapse video, blinded to the morphokinetic time stamp. The comparisons were made using calculated percentages and normalized discounted cumulative gain (NDCG), whereby an NDCG score of 1 would equate to all euploid embryos being ranked first. In arbitrary selection, the ploidy status was randomly assigned within each cycle and the NDGC calculated, and this was then repeated 100 times and the mean obtained.

MAIN RESULTS AND THE ROLE OF CHANCE

Arbitrary embryo selection would rank a euploid embryo first 37% of the time, embryologist selection 39%, and the LB and PREFER ploidy morphokinetic models 46% and 47% of the time, respectively. The AUC for LB and PREFER model was 0.62 and 0.63, respectively. Morphological selection did not significantly improve the performance of both morphokinetic models when used in combination. There was a significant difference between the NDGC metric of the PREFER model versus embryologist selection at 0.96 and 0.87, respectively (t = 14.1, P < 0.001). Similarly, there was a significant difference between the LB model and embryologist selection with an NDGC metric of 0.95 and 0.87, respectively (t = 12.0, P < 0.001). All four embryologists ranked embryos similarly, with an intraclass coefficient of 0.91 (95% CI 0.82-0.95, P < 0.001).

LIMITATIONS, REASONS FOR CAUTION

Aside from the retrospective study design, limitations include allowing the embryologist to watch the time lapse video, potentially providing more information than a truly static morphological assessment. Furthermore, the embryologists at the participating centres were familiar with the significant variables in time lapse, which could bias the results.

WIDER IMPLICATIONS OF THE FINDINGS

The present study shows that the use of morphokinetic models, namely PREFER and LB, translates into improved euploid embryo selection.

STUDY FUNDING/COMPETING INTEREST(S)

This study received no specific grant funding from any funding agency in the public, commercial or not-for-profit sectors. Dr Alison Campbell is minor share holder of Care Fertility. All other authors have no conflicts of interest to declare. Time lapse is a technology for which patients are charged extra at participating centres.

TRIAL REGISTRATION NUMBER

N/A.

Developmental competence and neonatal outcomes of nonpronuclear zygotes following single vitrified-warmed blastocyst transfers using propensity score matching analysis

PURPOSE

To investigate developmental competence and neonatal outcomes of nonpronuclear (0PN) zygotes following single vitrified-warmed blastocyst transfers (VBT).

METHODS

The clinical, laboratorial and neonatal data of 996 patients with ≤ 38 years who underwent blastocyst culture and single VBT were retrospectively analyzed. The pregnancy and neonatal outcomes of VBT were compared between 0PN and 2PN blastocysts using propensity score matching (PSM). Moreover, Day 3 (D3) embryo development and blastocyst formation were compared between 0PN and 2PN zygotes.

RESULTS

There were no significant differences in clinical pregnancy rate (CPR), live birth rate (LBR) and neonatal outcomes of VBT between the 0PN and 2PN blastocysts irrespectively of whether PSM was used. However, early abortion rate (EAR) was higher in blastocysts from 0PN D3 embryos > 10 cells (p < 0.05) before PSM. Moreover, the early developmental competence of 0PN zygotes was different from that of 2PN zygotes presenting higher percentages of D3 embryos ≤ 6 cells (p < 0.01) and > 10 cells (p < 0.01), lower available blastocyst formation rate (ABFR) (p < 0.01) and good-quality blastocyst formation rate (GBFR) (p < 0.01) in D3 embryos with 4-6 cells. ABFR and GBFR increased with cell number when compared among embryos with 4-6 cells, 7-10 cells and > 10 cells, irrespectively of 0PN or 2PN embryos.

CONCLUSION

The early developmental competence of 0PN zygotes was different from that of 2PN zygotes, but did not influence pregnancy and neonatal outcomes following VBT. ABFR and GBFR increased with cell number, irrespectively of 0PN or 2PN embryos.

Considerations for future modification of The Association for the Study of Reproductive Biology embryo grading system incorporating time-lapse observations

The Association for the Study of Reproductive Biology (ASEBIR) Interest Group in Embryology (in Spanish 'Grupo de Interés de Embriología') reviewed key morphokinetic parameters to assess the contribution of time-lapse technology (TLT) to the ASEBIR grading system. Embryo grading based on morphological characteristics is the most widely used method in human assisted reproduction laboratories. The introduction and implementation of TLT has provided a large amount of information that can be used as a complementary tool for morphological embryo evaluation and selection. As part of IVF treatments, embryologists grade embryos to decide which embryos to transfer or freeze. At the present, the embryo grading system developed by ASEBIR does not consider dynamic events observed through TLT. Laboratories that are using TLT consider those parameters as complementary data for embryo selection. The aim of this review was to evaluate review time-specific morphological changes during embryo development that are not included in the ASEBIR scoring system, and to consider them as candidates to add to the scoring system.

Novel embryo selection strategies-finding the right balance

The use of novel technologies in the selection of embryos during in vitro fertilisation (IVF) has the potential to improve the chances of pregnancy and birth of a healthy child. However, it is important to be aware of the potential risks and unintended consequences that may arise from the premature implementation of these technologies. This article discusses the ethical considerations surrounding the use of novel embryo selection technologies in IVF, including the growing uptake of genetic testing and others, and argues that prioritising embryos for transfer using these technologies is acceptable, but discarding embryos based on unproven advances is not. Several historical examples are provided, which demonstrate possible harms, where the overall chance of pregnancy may have been reduced, and some patients may have missed out on biological parenthood altogether. We emphasise the need for caution and a balanced approach to ensure that the benefits of these technologies outweigh any potential harm. We also highlight the primacy of patients' autonomy in reproductive decision-making, especially when information gained by utilising novel technologies is imprecise.

Diabetes mellitus and prostate cancer risk: A mendelian randomization analysis

BACKGROUND

Some studies have directed towards an association between diabetes mellitus (DM) and prostate cancer (PCa); however, this specific relationship remains inconclusive. In recent years, Mendelian randomization (MR) has become a widely used analytical method for inferring epidemiological causes.

AIM

To investigated the potential relationship between DM and PCa using MR.

METHODS

We downloaded relevant data on "diabetes" and "PCa" from the IEU OpenGWAS project database, performed three different methods to conduct MR, and carried out sensitivity analysis for verification.

RESULTS

The results indicated that DM was an independent risk factor for PCa. The odds ratio (OR) values obtained using the inverse variance weighted method in this study were as follows: OR = 1.018 (95% confidence interval: 1.004-1.032), P = 0.014.

CONCLUSION

We found that DM could increase the incidence rate of PCa.

Atypical initial cleavage patterns minimally impact rhesus macaque in vitro embryo morphokinetics and embryo outgrowth development†

Embryo morphokinetic analysis through time-lapse embryo imaging is envisioned as a method to improve selection of developmentally competent embryos. Morphokinetic analysis could be utilized to evaluate the effects of experimental manipulation on pre-implantation embryo development. The objectives of this study were to establish a normative morphokinetic database for in vitro fertilized rhesus macaque embryos and to assess the impact of atypical initial cleavage patterns on subsequent embryo development and formation of embryo outgrowths. The cleavage pattern and the timing of embryo developmental events were annotated retrospectively for unmanipulated in vitro fertilized rhesus macaque blastocysts produced over four breeding seasons. Approximately 50% of the blastocysts analyzed had an abnormal early cleavage event. The time to the initiation of embryo compaction and the time to completion of hatching was significantly delayed in blastocysts with an abnormal early cleavage event compared to blastocysts that had cleaved normally. Embryo hatching, attachment to an extracellular matrix, and growth during the implantation stage in vitro was not impacted by the initial cleavage pattern. These data establish normative morphokinetic parameters for in vitro fertilized rhesus macaque embryos and suggest that cleavage anomalies may not impact embryo implantation rates following embryo transfer.

Predicting risk of blastocyst aneuploidy among women with previous aneuploid pregnancy loss: a multicenter-data-based multivariable model

STUDY QUESTION

Can blastocyst aneuploidy be predicted for patients with previous aneuploid pregnancy loss (PAPL) and receiving preimplantation genetic testing for aneuploidy (PGT-A)?

SUMMARY ANSWER

Multivariable logistic regression models were established to predict high risk of blastocyst aneuploidy using four identified factors, presenting good predictive performance.

WHAT IS KNOWN ALREADY

Aneuploidy is the most common embryonic chromosomal abnormality leading to pregnancy loss. Several studies have demonstrated a higher embryo aneuploidy rate in patients with PAPL, which has suggested that PGT-A should have benefits in PAPL patients intending to improve their pregnancy outcomes. However, recent studies have failed to demonstrate the efficacy of PGT-A for PAPL patients. One possible way to improve the efficacy is to predict the risk of blastocyst aneuploidy risk in order to identify the specific PAPL population who may benefit from PGT-A.

STUDY DESIGN, SIZE, DURATION

We conducted a multicenter retrospective cohort study based on data analysis of 1119 patients receiving PGT-A in three reproductive medical centers of university affiliated teaching hospitals during January 2014 to June 2020. A cohort of 550 patients who had one to three PAPL(s) were included in the PAPL group. In addition, 569 patients with monogenic diseases without pregnancy loss were taken as the non-PAPL group.

PARTICIPANTS/MATERIALS, SETTING, METHODS

PGT-A was conducted using single nucleotide polymorphism microarrays and next-generation sequencing. Aneuploidy rates in Day 5 blastocysts of each patient were calculated and high-risk aneuploidy was defined as a rate of ≥50%. Candidate risk factors for high-risk aneuploidy were selected using the Akaike information criterion and were subsequently included in multivariable logistic regression models. Overall predictive accuracy was assessed using the confusion matrix, discrimination by area under the receiver operating characteristic curve (AUC), and calibration by plotting the predicted probabilities versus the observed probabilities. Statistical significance was set at P < 0.05.

MAIN RESULTS AND THE ROLE OF CHANCE

Blastocyst aneuploidy rates were 30 ± 25% and 21 ± 19% for PAPL and non-PAPL groups, respectively. Maternal age (odds ratio (OR) = 1.31, 95% CI 1.24-1.39, P < 0.001), number of PAPLs (OR = 1.40, 95% CI 1.05-1.86, P = 0.02), estradiol level on the ovulation trigger day (OR = 0.47, 95% CI 0.30-0.73, P < 0.001), and blastocyst formation rate (OR = 0.13, 95% CI 0.03-0.50, P = 0.003) were associated with high-risk of blastocyst aneuploidy. The predictive model based on the above four variables yielded AUCs of 0.80 using the training dataset and 0.83 using the test dataset, with average and maximal discrepancies of 2.89% and 12.76% for the training dataset, and 0.98% and 5.49% for the test dataset, respectively.

LIMITATIONS, REASONS FOR CAUTION

Our conclusions might not be compatible with those having fewer than four biopsied blastocysts and diminished ovarian reserves, since all of the included patients had four or more biopsied blastocysts and had exhibited good ovarian reserves.

WIDER IMPLICATIONS OF THE FINDINGS

The developed predictive model is critical for counseling PAPL patients before PGT-A by considering maternal age, number of PAPLs, estradiol levels on the ovulation trigger day, and the blastocyst formation rate. This prediction model achieves good risk stratification and so may be useful for identifying PAPL patients who may have higher risk of blastocyst aneuploidy and can therefore acquire better pregnancy outcomes by PGT-A.

STUDY FUNDING/COMPETING INTEREST(S)

This work was supported by the National Natural Science Foundation of China under Grant (81871159). No competing interest existed in the study.

TRIAL REGISTRATION NUMBER

N/A.

Aneuploidy in oocytes from women of advanced maternal age: analysis of the causal meiotic errors and impact on embryo development

STUDY QUESTION

In oocytes of advanced maternal age (AMA) women, what are the mechanisms leading to aneuploidy and what is the association of aneuploidy with embryo development?

SUMMARY ANSWER

Known chromosome segregation errors such as precocious separation of sister chromatids explained 90.4% of abnormal chromosome copy numbers in polar bodies (PBs), underlying impaired embryo development.

WHAT IS KNOWN ALREADY

Meiotic chromosomal aneuploidies in oocytes correlate with AMA (>35 years) and can affect over half of oocytes in this age group. This underlies the rationale for PB biopsy as a form of early preimplantation genetic testing for aneuploidy (PGT-A), as performed in the 'ESHRE STudy into the Evaluation of oocyte Euploidy by Microarray analysis' (ESTEEM) randomized controlled trial (RCT). So far, chromosome analysis of oocytes and PBs has shown that precocious separation of sister chromatids (PSSC), Meiosis II (MII) non-disjunction (ND), and reverse segregation (RS) are the main mechanisms leading to aneuploidy in oocytes.

STUDY DESIGN, SIZE, DURATION

Data were sourced from the ESTEEM study, a multicentre RCT from seven European centres to assess the clinical utility of PGT-A on PBs using array comparative genomic hybridization (aCGH) in patients of AMA (36-40 years). This included data on the chromosome complement in PB pairs (PGT-A group), and on embryo morphology in a subset of embryos, up to Day 6 post-insemination, from both the intervention (PB biopsy and PGT-A) and control groups.

PARTICIPANTS/MATERIALS, SETTING, METHODS

ESTEEM recruited 396 AMA patients: 205 in the intervention group and 191 in the control group. Complete genetic data from 693 PB pairs were analysed. Additionally, the morphology from 1034 embryos generated from fertilized oocytes (two pronuclei) in the PB biopsy group and 1082 in the control group were used for statistical analysis.

MAIN RESULTS AND THE ROLE OF CHANCE

Overall, 461/693 PB pairs showed abnormal segregation in 1162/10 810 chromosomes. The main observed abnormal segregations were compatible with PSSC in Meiosis I (MI) (n = 568/1162; 48.9%), ND of chromatids in MII or RS (n = 417/1162; 35.9%), and less frequently ND in MI (n = 65/1162; 5.6%). For 112 chromosomes (112/1162; 9.6%), we observed a chromosome copy number in the first PB (PB1) and second PB (PB2) that is not explained by any of the known mechanisms causing aneuploidy in oocytes. We observed that embryos in the PGT-A arm of the RCT did not have a significantly different morphology between 2 and 6 days post-insemination compared to the control group, indicating that PB biopsy did not affect embryo quality. Following age-adjusted multilevel mixed-effect ordinal logistic regression models performed for each embryo evaluation day, aneuploidy was associated with a decrease in embryo quality on Day 3 (adjusted odds ratio (aOR) 0.62, 95% CI 0.43-0.90), Day 4 (aOR 0.15, 95% CI 0.06-0.39), and Day 5 (aOR 0.28, 95% CI 0.14-0.58).

LIMITATIONS, REASON FOR CAUTION

RS cannot be distinguished from normal segregation or MII ND using aCGH. The observed segregations were based on the detected copy number of PB1 and PB2 only and were not confirmed by the analysis of embryos. The embryo morphology assessment was static and single observer.

WIDER IMPLICATIONS OF THE FINDINGS

Our finding of frequent unexplained chromosome copy numbers in PBs indicates that our knowledge of the mechanisms causing aneuploidy in oocytes is incomplete. It challenges the dogma that aneuploidy in oocytes is exclusively caused by mis-segregation of chromosomes during MI and MII.

STUDY FUNDING/COMPETING INTEREST(S)

Data were mined from a study funded by ESHRE. Illumina provided microarrays and other consumables necessary for aCGH testing of PBs. None of the authors have competing interests.

TRIAL REGISTRATION NUMBER

Data were mined from the ESTEEM study (ClinicalTrials.gov Identifier NCT01532284).

Genetic causes of sporadic and recurrent miscarriage

Approximately 80% of miscarriages happen within the first 12 weeks of gestation. More than half of early losses result from genetic defects, usually presenting as abnormal chromosome numbers or gene rearrangements in the embryo. However, the impact of genetics on pregnancy loss goes well beyond embryonic aneuploidy. For example, the use of big data has recently led to the discovery of specific gene mutations that may be implicated in sporadic and recurrent miscarriages. Further, emerging data suggest that genetic factors play a role in conditions for which there is a causative association with recurrent pregnancy loss. Here, we summarize the evidence on the genetics of miscarriage and provide an overview of the diagnosis and prevention of genetic causes associated with sporadic and recurrent pregnancy loss.

Association between a morphokinetic ploidy prediction model risk score and miscarriage and live birth: a multicentre cohort study

OBJECTIVE

To determine whether the aneuploidy risk score from a morphokinetic ploidy prediction model, Predicting Euploidy for Embryos in Reproductive Medicine (PREFER), is associated with miscarriage and live birth outcomes.

DESIGN

Multicentre cohort study.

SETTING

Nine in vitro fertilization clinics in the United Kingdom.

PATIENTS

Data were obtained from the treatment of patients from 2016-2019. A total of 3587 fresh single embryo transfers were included; preimplantation genetic testing for aneuploidy) cycles were excluded.

INTERVENTION

PREFER is a model developed using 8,147 biopsied blastocyst specimens to predict ploidy status using morphokinetic and clinical biodata. A second model using only morphokinetic (MK) predictors was developed, P PREFER-MK. The models will categorize embryos into the following three risk score categories for aneuploidy: "high risk," "medium risk," and "low risk."

MAIN OUTCOME MEASURES

The primary outcomes are miscarriage and live birth. Secondary outcomes include biochemical clinical pregnancy per single embryo transfer.

RESULTS

When applying PREFER, the miscarriage rates were 12%, 14%, and 22% in the "low risk," "moderate risk," and "high risk" categories, respectively. Those embryos deemed "high risk" had a significantly higher egg provider age compared with "low risk," and there was little variation in risk categories in patients of the same age. The trend in miscarriage rate was not seen when using PREFER-MK; however, there was an association with live birth, increasing from 38%-49% and 50% in the "high risk," "moderate risk," and "low risk" groups, respectively. An adjusted logistic regression analysis demonstrated that PREFER-MK was not associated with miscarriage when comparing "high risk" to "moderate risk" embryos (odds ratio [OR], 0.87; 95% confidence interval [CI], 0.63-1.63) or "high risk" to "low risk" embryos (OR, 1.07; 95% CI, 0.79-1.46). An embryo deemed "low risk" by PREFER-MK was significantly more likely to result in a live birth than those embryos graded "high risk" (OR, 1.95; 95% CI, 1.65-2.25).

CONCLUSION

The PREFER model's risk scores were significantly associated with live births and miscarriages. Importantly, this study also found that this model applied too much weight to clinical factors, such that it could no longer rank a patient's embryos effectively. Therefore, a model including only MKs would be preferred; this was similarly associated with live birth but not miscarriage.

Application of artificial intelligence in gametes and embryos selection

Gamete and embryo quality are critical to the success rate of Assisted Reproductive Technology (ART) cycles, but there remains a lack of methods to accurately measure the quality of sperm, oocytes and embryos. The ability of Artificial Intelligence (AI) technology to analyze large amounts of data, especially video and images, is particularly useful in gamete and embryo assessment and selection. The well-trained model has fast calculation speed and high accuracy, which can help embryologists to perform more objective gamete and embryo selection. Various artificial intelligence models have been developed for gamete and embryo assessment, some of which exhibit good performance. In this review, we summarize the latest applications of AI technology in semen analysis, as well as selection for sperm, oocyte and embryo, and discuss the existing problems and development directions of artificial intelligence in this field.

Opening the black box: why do euploid blastocysts fail to implant? A systematic review and meta-analysis

BACKGROUND

A normal chromosomal constitution defined through PGT-A assessing all chromosomes on trophectoderm (TE) biopsies represents the strongest predictor of embryo implantation. Yet, its positive predictive value is not higher than 50-60%. This gap of knowledge on the causes of euploid blastocysts' reproductive failure is known as 'the black box of implantation'.

OBJECTIVE AND RATIONALE

Several embryonic, maternal, paternal, clinical, and IVF laboratory features were scrutinized for their putative association with reproductive success or implantation failure of euploid blastocysts.

SEARCH METHODS

A systematic bibliographical search was conducted without temporal limits up to August 2021. The keywords were '(blastocyst OR day5 embryo OR day6 embryo OR day7 embryo) AND (euploid OR chromosomally normal OR preimplantation genetic testing) AND (implantation OR implantation failure OR miscarriage OR abortion OR live birth OR biochemical pregnancy OR recurrent implantation failure)'. Overall, 1608 items were identified and screened. We included all prospective or retrospective clinical studies and randomized-controlled-trials (RCTs) that assessed any feature associated with live-birth rates (LBR) and/or miscarriage rates (MR) among non-mosaic euploid blastocyst transfer after TE biopsy and PGT-A. In total, 41 reviews and 372 papers were selected, clustered according to a common focus, and thoroughly reviewed. The PRISMA guideline was followed, the PICO model was adopted, and ROBINS-I and ROB 2.0 scoring were used to assess putative bias. Bias across studies regarding the LBR was also assessed using visual inspection of funnel plots and the trim and fill method. Categorical data were combined with a pooled-OR. The random-effect model was used to conduct the meta-analysis. Between-study heterogeneity was addressed using I2. Whenever not suitable for the meta-analysis, the included studies were simply described for their results. The study protocol was registered at http://www.crd.york.ac.uk/PROSPERO/ (registration number CRD42021275329).

OUTCOMES

We included 372 original papers (335 retrospective studies, 30 prospective studies and 7 RCTs) and 41 reviews. However, most of the studies were retrospective, or characterized by small sample sizes, thus prone to bias, which reduces the quality of the evidence to low or very low. Reduced inner cell mass (7 studies, OR: 0.37, 95% CI: 0.27-0.52, I2 = 53%), or TE quality (9 studies, OR: 0.53, 95% CI: 0.43-0.67, I2 = 70%), overall blastocyst quality worse than Gardner's BB-grade (8 studies, OR: 0.40, 95% CI: 0.24-0.67, I2 = 83%), developmental delay (18 studies, OR: 0.56, 95% CI: 0.49-0.63, I2 = 47%), and (by qualitative analysis) some morphodynamic abnormalities pinpointed through time-lapse microscopy (abnormal cleavage patterns, spontaneous blastocyst collapse, longer time of morula formation I, time of blastulation (tB), and duration of blastulation) were all associated with poorer reproductive outcomes. Slightly lower LBR, even in the context of PGT-A, was reported among women ≥38 years (7 studies, OR: 0.87, 95% CI: 0.75-1.00, I2 = 31%), while obesity was associated with both lower LBR (2 studies, OR: 0.66, 95% CI: 0.55-0.79, I2 = 0%) and higher MR (2 studies, OR: 1.8, 95% CI: 1.08-2.99, I2 = 52%). The experience of previous repeated implantation failures (RIF) was also associated with lower LBR (3 studies, OR: 0.72, 95% CI: 0.55-0.93, I2 = 0%). By qualitative analysis, among hormonal assessments, only abnormal progesterone levels prior to transfer were associated with LBR and MR after PGT-A. Among the clinical protocols used, vitrified-warmed embryo transfer was more effective than fresh transfer (2 studies, OR: 1.56, 95% CI: 1.05-2.33, I2 = 23%) after PGT-A. Lastly, multiple vitrification-warming cycles (2 studies, OR: 0.41, 95% CI: 0.22-0.77, I2 = 50%) or (by qualitative analysis) a high number of cells biopsied may slightly reduce the LBR, while simultaneous zona-pellucida opening and TE biopsy allowed better results than the Day 3 hatching-based protocol (3 studies, OR: 1.41, 95% CI: 1.18-1.69, I2 = 0%).

WIDER IMPLICATIONS

Embryo selection aims at shortening the time-to-pregnancy, while minimizing the reproductive risks. Knowing which features are associated with the reproductive competence of euploid blastocysts is therefore critical to define, implement, and validate safer and more efficient clinical workflows. Future research should be directed towards: (i) systematic investigations of the mechanisms involved in reproductive aging beyond de novo chromosomal abnormalities, and how lifestyle and nutrition may accelerate or exacerbate their consequences; (ii) improved evaluation of the uterine and blastocyst-endometrial dialogue, both of which represent black boxes themselves; (iii) standardization/automation of embryo assessment and IVF protocols; (iv) additional invasive or preferably non-invasive tools for embryo selection. Only by filling these gaps we may finally crack the riddle behind 'the black box of implantation'.

The embryo non-invasive pre-implantation diagnosis era: how far are we?

Advancements in assisted reproduction (AR) methodologies have allowed significant improvements in live birth rates of women who otherwise would not be able to conceive. One of the tools that allowed this improvement is the possibility of embryo selection based on genetic status, performed via preimplantation genetic testing (PGT). Even though the widespread use of PGT from TE biopsy helped to decrease the interval from the beginning of the AR intervention to pregnancy, especially in older patients, in AR, there are still many concerns about the application of this invasive methodology in all cycles. Therefore, recently, researchers started to study the use of cell free DNA (cfDNA) released by the blastocyst in its culture medium to perform PGT, in a method called non-invasive PGT (niPGT). The development of a niPGT would bring the diagnostics power of conventional PGT, but with the advantage of being potentially less harmful to the embryo. Its implementation in clinical practice, however, is under heavy discussion since there are many unknowns about the technique, such as the origin of the cfDNA or if this genetic material is a true representative of the actual ploidy status of the embryo. Available data indicates that there is high correspondence between results observed in TE biopsies and the ones observed from cfDNA, but these results are still contradictory and highly debatable. In the present review, the advantages and disadvantages of niPGT are presented and discussed in relation to tradition TE biopsy-based PGT. Furthermore, there are also presented some other possible non-invasive tools that could be applied in the selection of the best embryo, such as quantification of other molecules as quality biomarkers, or the use artificial intelligence (AI) to identify the best embryos based on morphological and/or morphokitetic parameters.

Making and selecting the best embryo in the laboratory

Over the past 4 decades our ability to maintain a viable human embryo in vitro has improved dramatically, leading to higher implantation rates. This has led to a notable shift to single blastocyst transfer and the ensuing elimination of high order multiple gestations. Future improvements to embryo culture systems will not only come from new improved innovative media formulations (such as the inclusion of antioxidants), but plausibly by moving away from static culture to more dynamic perfusion-based systems now made a reality owing to the breakthroughs in three-dimensional printing technology and micro fabrication. Such an approach has already made it feasible to create high resolution devices for intracytoplasmic sperm injection, culture, and cryopreservation, paving the way not only for improvements in outcomes but also automation of assisted reproductive technology. Although improvements in culture systems can lead to further increases in pregnancy outcomes, the ability to quantitate biomarkers of embryo health and viability will reduce time to pregnancy and decrease pregnancy loss. Currently artificial intelligence is being used to assess embryo development through image analysis, but we predict its power will be realized through the creation of selection algorithms based on the integration of information related to metabolic functions, cell-free DNA, and morphokinetics, thereby using vast amounts of different data types obtained for each embryo to predict outcomes. All of this will not only make assisted reproductive technology more effective, but it will also make it more cost effective, thereby increasing patient access to infertility treatment worldwide.

Deep learning system for classification of ploidy status using time-lapse videos

OBJECTIVE

To develop a spatiotemporal model for de prediction of euploid and aneuploid embryos using time-lapse videos from 10-115 hours after insemination (hpi).

DESIGN

Retrospective study.

MAIN OUTCOME MEASURES

The research used an end-to-end approach to develop an automated artificial intelligence system capable of extracting features from images and classifying them, considering spatiotemporal dependencies. A convolutional neural network extracted the most relevant features from each video frame. A bidirectional long short-term memory layer received this information and analyzed the temporal dependencies, obtaining a low-dimensional feature vector that characterized each video. A multilayer perceptron classified them into 2 groups, euploid and noneuploid.

RESULTS

The model performance in accuracy fell between 0.6170 and 0.7308. A multi-input model with a gate recurrent unit module performed better than others; the precision (or positive predictive value) is 0.8205 for predicting euploidy. Sensitivity, specificity, F1-Score and accuracy are 0.6957, 0.7813, 0.7042, and 0.7308, respectively.

CONCLUSIONS

This article proposes an artificial intelligence solution for prioritizing euploid embryo transfer. We can highlight the identification of a noninvasive method for chromosomal status diagnosis using a deep learning approach that analyzes raw data provided by time-lapse incubators. This method demonstrated potential automation of the evaluation process, allowing spatial and temporal information to encode.

ESHRE good practice recommendations on recurrent implantation failure

STUDY QUESTION

How should recurrent implantation failure (RIF) in patients undergoing ART be defined and managed?

SUMMARY ANSWER

This is the first ESHRE good practice recommendations paper providing a definition for RIF together with recommendations on how to investigate causes and contributing factors, and how to improve the chances of a pregnancy.

WHAT IS KNOWN ALREADY

RIF is a challenge in the ART clinic, with a multitude of investigations and interventions offered and applied in clinical practice, often without biological rationale or with unequivocal evidence of benefit.

STUDY DESIGN SIZE DURATION

This document was developed according to a predefined methodology for ESHRE good practice recommendations. Recommendations are supported by data from the literature, if available, and the results of a previously published survey on clinical practice in RIF and the expertise of the working group. A literature search was performed in PubMed and Cochrane focussing on 'recurrent reproductive failure', 'recurrent implantation failure', and 'repeated implantation failure'.

PARTICIPANTS/MATERIALS SETTING METHODS

The ESHRE Working Group on Recurrent Implantation Failure included eight members representing the ESHRE Special Interest Groups for Implantation and Early Pregnancy, Reproductive Endocrinology, and Embryology, with an independent chair and an expert in statistics. The recommendations for clinical practice were formulated based on the expert opinion of the working group, while taking into consideration the published data and results of the survey on uptake in clinical practice. The draft document was then open to ESHRE members for online peer review and was revised in light of the comments received.

MAIN RESULTS AND THE ROLE OF CHANCE

The working group recommends considering RIF as a secondary phenomenon of ART, as it can only be observed in patients undergoing IVF, and that the following description of RIF be adopted: 'RIF describes the scenario in which the transfer of embryos considered to be viable has failed to result in a positive pregnancy test sufficiently often in a specific patient to warrant consideration of further investigations and/or interventions'. It was agreed that the recommended threshold for the cumulative predicted chance of implantation to identify RIF for the purposes of initiating further investigation is 60%. When a couple have not had a successful implantation by a certain number of embryo transfers and the cumulative predicted chance of implantation associated with that number is greater than 60%, then they should be counselled on further investigation and/or treatment options. This term defines clinical RIF for which further actions should be considered. Nineteen recommendations were formulated on investigations when RIF is suspected, and 13 on interventions. Recommendations were colour-coded based on whether the investigations/interventions were recommended (green), to be considered (orange), or not recommended, i.e. not to be offered routinely (red).

LIMITATIONS REASONS FOR CAUTION

While awaiting the results of further studies and trials, the ESHRE Working Group on Recurrent Implantation Failure recommends identifying RIF based on the chance of successful implantation for the individual patient or couple and to restrict investigations and treatments to those supported by a clear rationale and data indicating their likely benefit.

WIDER IMPLICATIONS OF THE FINDINGS

This article provides not only good practice advice but also highlights the investigations and interventions that need further research. This research, when well-conducted, will be key to making progress in the clinical management of RIF.

STUDY FUNDING/COMPETING INTERESTS

The meetings and technical support for this project were funded by ESHRE. N.M. declared consulting fees from ArtPRED (The Netherlands) and Freya Biosciences (Denmark); Honoraria for lectures from Gedeon Richter, Merck, Abbott, and IBSA; being co-founder of Verso Biosense. He is Co-Chief Editor of Reproductive Biomedicine Online (RBMO). D.C. declared being an Associate Editor of Human Reproduction Update, and declared honoraria for lectures from Merck, Organon, IBSA, and Fairtility; support for attending meetings from Cooper Surgical, Fujifilm Irvine Scientific. G.G. declared that he or his institution received financial or non-financial support for research, lectures, workshops, advisory roles, or travelling from Ferring, Merck, Gedeon-Richter, PregLem, Abbott, Vifor, Organon, MSD, Coopersurgical, ObsEVA, and ReprodWissen. He is an Editor of the journals Archives of Obstetrics and Gynecology and Reproductive Biomedicine Online, and Editor in Chief of Journal Gynäkologische Endokrinologie. He is involved in guideline developments and quality control on national and international level. G.L. declared he or his institution received honoraria for lectures from Merck, Ferring, Vianex/Organon, and MSD. He is an Associate Editor of Human Reproduction Update, immediate past Coordinator of Special Interest Group for Reproductive Endocrinology of ESHRE and has been involved in Guideline Development Groups of ESHRE and national fertility authorities. D.J.M. declared being an Associate Editor for Human Reproduction Open and statistical Advisor for Reproductive Biomedicine Online. B.T. declared being shareholder of Reprognostics and she or her institution received financial or non-financial support for research, clinical trials, lectures, workshops, advisory roles or travelling from support for attending meetings from Ferring, MSD, Exeltis, Merck Serono, Bayer, Teva, Theramex and Novartis, Astropharm, Ferring. The other authors had nothing to disclose.

DISCLAIMER

This Good Practice Recommendations (GPR) document represents the views of ESHRE, which are the result of consensus between the relevant ESHRE stakeholders and are based on the scientific evidence available at the time of preparation. ESHRE GPRs should be used for information and educational purposes. They should not be interpreted as setting a standard of care or be deemed inclusive of all proper methods of care, or be exclusive of other methods of care reasonably directed to obtaining the same results. They do not replace the need for application of clinical judgement to each individual presentation, or variations based on locality and facility type. Furthermore, ESHRE GPRs do not constitute or imply the endorsement, or favouring, of any of the included technologies by ESHRE.

Towards Automation in IVF: Pre-Clinical Validation of a Deep Learning-Based Embryo Grading System during PGT-A Cycles

Preimplantation genetic testing for aneuploidies (PGT-A) is arguably the most effective embryo selection strategy. Nevertheless, it requires greater workload, costs, and expertise. Therefore, a quest towards user-friendly, non-invasive strategies is ongoing. Although insufficient to replace PGT-A, embryo morphological evaluation is significantly associated with embryonic competence, but scarcely reproducible. Recently, artificial intelligence-powered analyses have been proposed to objectify and automate image evaluations. iDAScore v1.0 is a deep-learning model based on a 3D convolutional neural network trained on time-lapse videos from implanted and non-implanted blastocysts. It is a decision support system for ranking blastocysts without manual input. This retrospective, pre-clinical, external validation included 3604 blastocysts and 808 euploid transfers from 1232 cycles. All blastocysts were retrospectively assessed through the iDAScore v1.0; therefore, it did not influence embryologists' decision-making process. iDAScore v1.0 was significantly associated with embryo morphology and competence, although AUCs for euploidy and live-birth prediction were 0.60 and 0.66, respectively, which is rather comparable to embryologists' performance. Nevertheless, iDAScore v1.0 is objective and reproducible, while embryologists' evaluations are not. In a retrospective simulation, iDAScore v1.0 would have ranked euploid blastocysts as top quality in 63% of cases with one or more euploid and aneuploid blastocysts, and it would have questioned embryologists' ranking in 48% of cases with two or more euploid blastocysts and one or more live birth. Therefore, iDAScore v1.0 may objectify embryologists' evaluations, but randomized controlled trials are required to assess its clinical value.

Does embryo categorization by existing artificial intelligence, morphokinetic or morphological embryo selection models correlate with blastocyst euploidy rates?

RESEARCH QUESTION

Does embryo categorization by existing artificial intelligence (AI), morphokinetic or morphological embryo selection models correlate with blastocyst euploidy?

DESIGN

A total of 834 patients (mean maternal age 40.5 ± 3.4 years) who underwent preimplantation genetic testing for aneuploidies (PGT-A) on a total of 3573 tested blastocysts were included in this retrospective study. The cycles were stratified into five maternal age groups according to the Society for Assisted Reproductive Technology age groups (<35, 35-37, 38-40, 41-42 and >42 years). The main outcome of this study was the correlation of euploidy rates in stratified maternal age groups and an automated AI model (iDAScore® v1.0), a morphokinetic embryo selection model (KIDScore Day 5 ver 3, KS-D5) and a traditional morphological grading model (Gardner criteria), respectively.

RESULTS

Euploidy rates were significantly correlated with iDAScore (P = 0.0035 to <0.001) in all age groups, and expect for the youngest age group, with KS-D5 and Gardner criteria (all P < 0.0001). Additionally, multivariate logistic regression analysis showed that for all models, higher scores were significantly correlated with euploidy (all P < 0.0001).

CONCLUSION

These results show that existing blastocyst scoring models correlate with ploidy status. However, as these models were developed to indicate implantation potential, they cannot accurately diagnose if an embryo is euploid or aneuploid. Instead, they may be used to support the decision of how many and which blastocysts to biopsy, thus potentially reducing patient costs.