Embryo morphokinetic characteristics and euploidy

Enhancing predictive models for egg donation: time to blastocyst hatching and machine learning insights

BACKGROUND

Data sciences and artificial intelligence are becoming encouraging tools in assisted reproduction, favored by time-lapse technology incubators. Our objective is to analyze, compare and identify the most predictive machine learning algorithm developed using a known implantation database of embryos transferred in our egg donation program, including morphokinetic and morphological variables, and recognize the most predictive embryo parameters in order to enhance IVF treatments clinical outcomes.

METHODS

Multicenter retrospective cohort study carried out in 378 egg donor recipients who performed a fresh single embryo transfer during 2021. All treatments were performed by Intracytoplasmic Sperm Injection, using fresh or frozen oocytes. The embryos were cultured in Geri® time-lapse incubators until transfer on day 5. The embryonic morphokinetic events of 378 blastocysts with known implantation and live birth were analyzed. Classical statistical analysis (binary logistic regression) and 10 machine learning algorithms were applied including Multi-Layer Perceptron, Support Vector Machines, k-Nearest Neighbor, Cart and C0.5 Classification Trees, Random Forest (RF), AdaBoost Classification Trees, Stochastic Gradient boost, Bagged CART and eXtrem Gradient Boosting. These algorithms were developed and optimized by maximizing the area under the curve.

RESULTS

The Random Forest emerged as the most predictive algorithm for implantation (area under the curve, AUC = 0.725, IC 95% [0.6232-0826]). Overall, implantation and miscarriage rates stood at 56.08% and 18.39%, respectively. Overall live birth rate was 41.26%. Significant disparities were observed regarding time to hatching out of the zona pellucida (p = 0.039). The Random Forest algorithm demonstrated good predictive capabilities for live birth (AUC = 0.689, IC 95% [0.5821-0.7921]), but the AdaBoost classification trees proved to be the most predictive model for live birth (AUC = 0.749, IC 95% [0.6522-0.8452]). Other important variables with substantial predictive weight for implantation and live birth were duration of visible pronuclei (DESAPPN-APPN), synchronization of cleavage patterns (T8-T5), duration of compaction (TM-TiCOM), duration of compaction until first sign of cavitation (TiCAV-TM) and time to early compaction (TiCOM).

CONCLUSIONS

This study highlights Random Forest and AdaBoost as the most effective machine learning models in our Known Implantation and Live Birth Database from our egg donation program. Notably, time to blastocyst hatching out of the zona pellucida emerged as a highly reliable parameter significantly influencing our implantation machine learning predictive models. Processes involving syngamy, genomic imprinting during embryo cleavage, and embryo compaction are also influential and could be crucial for implantation and live birth outcomes.

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.

Embryo ploidy status classification through computer-assisted morphology assessment

BACKGROUND

Preimplantation genetic testing for aneuploidy has been proven to be effective in determining the embryo's chromosomal or ploidy status. The test requires a biopsy of embryonic cells on day 3, 5, or 6 from which complete information on the chromosomes would be obtained. The main drawbacks of preimplantation genetic testing for aneuploidy include its relatively invasive approach and the lack of research studies on the long-term effects of preimplantation genetic testing for aneuploidy.

OBJECTIVE

Computer-assisted predictive modeling through machine learning and deep learning algorithms has been proposed to minimize the use of invasive preimplantation genetic testing for aneuploidy. The capability to predict morphologic characteristics of embryo ploidy status creates a meaningful support system for decision-making before further treatment.

STUDY DESIGN

Image processing is a component in developing a predictive model specialized in image classification through which a model is able to differentiate images based on unique features. Image processing is obtained through image augmentation to capture segmented embryos and perform feature extraction. Furthermore, multiple machine learning and deep learning algorithms were used to create prediction-based modeling, and all of the prediction models undergo similar model performance assessments to determine the best model prediction algorithm.

RESULTS

An efficient artificial intelligence model that can predict embryo ploidy status was developed using image processing through a histogram of oriented gradient and then followed by principal component analysis. The gradient boosting algorithm showed an advantage against other algorithms and yielded an accuracy of 0.74, an aneuploid precision of 0.83, and an aneuploid predictive value (recall) of 0.84.

CONCLUSION

This research study proved that machine-assisted technology perceives the embryo differently than human observation and determined that further research on in vitro fertilization is needed. The study finding serves as a basis for developing a better computer-assisted prediction model.

Embryo morphokinetics derived from fresh and vitrified bovine oocytes predict blastocyst development and nuclear abnormalities

Embryo development is a dynamic process and critical stages may go unnoticed with the use of traditional morphologic assessments, especially the timing of embryonic divisions and aberrant zygotic cleavage patterns. Bovine embryo development is impaired after oocyte vitrification, but little is known about the underlying morphokinetic behavior. Here, bovine zygotes from fresh (n = 708) and vitrified oocytes (n = 182) were monitored by time-lapse imaging and the timing and nature of early blastomere divisions were modeled to find associations with blastocyst development at day 8. The predictive potential of morphokinetic parameters was analyzed by logistic regression and receiver operating characteristic curve analysis to determine optimal cut-off values. Lag-phase was highly correlated with embryo development. Remarkably, 100% of zygotes that reached the blastocyst stage showed a lag-phase. Fast first cleavage increased the chance of blastocyst development to 30% with a cut-off of 32 h and 22 min. Aberrant zygotic cleavage events, including multipolar division, unequal blastomere sizes, and membrane ruffling resulted in decreased blastocyst development. Multipolar division leads to uneven blastomeres, which was associated with anuclear and multinuclear blastomeres, indicating genome segregation errors. Moreover, we described for the first time morphokinetics of embryos derived from vitrified bovine oocytes. Vitrification severely affected blastocyst development, although lower cryoprotectant concentration in equilibration solutions seems to be less detrimental for embryo yield. Impaired development was linked to slow cleavages, lower lag-phase incidence, and increased early embryonic arrest. Typically, less than 15% of the embryos produced from vitrified oocytes reached more than eight cells. Interestingly, the rate of abnormal first cleavage events was not affected by oocyte vitrification. In conclusion, time to first cleavage, the presence of a lag-phase, and the absence of aberrant zygotic cleavage were the best predictors of bovine blastocyst development for both fresh and vitrified oocytes.

Microfluidic sperm sorting improves ICSI outcomes in patients with increased values of Double-Strand Breaks in sperm DNA

BACKGROUND

Delays in embryo kinetics, implantation failures in ICSI treatments and recurrent miscarriages have been associated with high values of Double-Strand Breaks (DSB) in sperm DNA. While conventional methods for semen preparation have been shown to be inefficient reducing DSB values, Microfluidic Sperm Sorting (MSS) devices are promising tools to reduce this damage.

OBJECTIVE

To study the clinical utility of an MSS device in ICSI treatments when the male partner presents increased DSB values, as compared to the use of conventional methods based on sperm motility.

METHODS

This retrospective cohort study included 28 infertile couples undergoing ICSI treatments. Only couples where the male partner presented increased values of DSB were included. DSB values were evaluated in semen samples by the Neutral Comet assay. Couples performed a first ICSI cycle using conventional methods for semen preparation (Density Gradients and Swim-up) and a second ICSI cycle using the ZyMōt™ICSI (formerly named FertileChip®) microfluidic device. Embryology and clinical outcomes were compared between ICSI cycles.

RESULTS

Semen parameters and the number of obtained and fertilized oocytes did not show differences between ICSI rounds. Clinical outcomes were statistically better when MSS was used: the biochemical pregnancy rate increased 28.31%; the clinical pregnancy rate increased 35.56% and the number of live births increased 35.29%, as compared to the first ICSI cycle in this group of patients.

CONCLUSIONS

The ZyMōt™ICSI microfluidic device improved the reproductive outcomes in couples where the male partner presented increased DSB values, when compared to the use of conventional semen preparation techniques.

Marginal differences in preimplantation morphokinetics between conventional IVF and ICSI in patients with preimplantation genetic testing for aneuploidy (PGT-A): A sibling oocyte study

Objective

This study aimed to analyze the morphokinetic behaviour between conventional IVF and ICSI, in cycles with preimplantation genetic testing for aneuploidies (PGT-A).

Materials

A randomized controlled trial (NCT03708991) was conducted in a private fertility center. Thirty couples with non-male factor infertility were recruited between November 2018 and April 2019. A total of 568 sibling cumulus oocyte complexes were randomly inseminated with conventional IVF and ICSI and cultured in an Embryoscope time-lapse system. The morphokinetic behaviour of IVF/ICSI sibling oocytes was analysed as primary endpoint. As secondary endpoints, morphokinetic parameters that predict blastocysts that will be biopsied, the day of biopsy, gender and euploid outcome was assessed.

Results

When comparing IVF to ICSI, only the time to reach the 2-cell stage (t2) was significantly delayed for IVF embryos: OR: 1.282 [1.020–1.612], p = 0.033. After standardizing for tPNf (ct parameters), only Blast(tStartBlastulation-t2) remained significant: OR: 0.803 [0.648–0.994], p = 0.044. For the analysis of zygotes that will be biopsied on day 5/6 versus zygotes without biopsy, only early morphokinetic parameters were considered. All parameters were different in the multivariate model: ct2: OR: 0.840 [0.709–0.996], p = 0.045; ct6: OR: 0.943 [0.890–0.998], p = 0.043; cc2(t3-t2): OR: 1.148 [1.044–1.263], p = 0.004; cc3(t5-t3): OR: 1.177 [1.107–1.251], p<0.0001. When comparing the development between blastocysts biopsied on day 5 versus day 6, only three morphokinetic parameters were significant: cc2(t3-t2): OR: 1.394 [1.010–1.926], p = 0.044; ctBlastocyst: OR: 0.613 [0.489–0.768], p<0.0001 and ctExpandedBlastocyst: OR: 0.913 [0.868–0.960], p = 0.0004. Multivariate analysis of gender and ploidy did not reveal differences in morphokinetic behaviour.

Conclusion

Minor morphokinetic differences are observed between IVF and ICSI. Early in the development, distinct cleavage patterns are observed between embryos that will be biopsied or not.

The Association of Kinetic Variables with Blastocyst Development and Ploidy Status

Background:

Despite a plethora of studies conducted so far, a debate is still unresolved as to whether TLM can identify predictive kinetic biomarkers or algorithms universally applicable. Therefore, this study aimed to elucidate if there is a relationship between kinetic variables and ploidy status of human embryos or blastocyst developmental potential.

Methods:

For conducting this retrospective cohort study, the normal distribution of data was verified using Kolmogorov-Smirnov test with the Lilliefors’ amendment and the Shapiro-Wilk test. Kinetic variables were expressed as median and quartiles (Q1, Q2, Q3, Q4). Mann-Whitney U-test was used to compare the median values of parameters. Univariate and multiple logistic regression models were used to assess relationship between blastocyst developmental potential or ploidy status and kinetics. Several confounding factors were also assessed.

Results:

Blastocyst developmental potential was positively correlated with the t4-t3 interval (s2) (OR=1.417, 95% CI of 1.288–1.560). s2 median value was significantly different between high- and low-quality blastocysts (0.50 and 1.33 hours post-insemination, hpi, respectively; p=0.003). In addition, timing of pronuclear appearance (tPNa) (OR=1.287; 95% CI of 1.131–1.463) had a significant relationship with ploidy changes. The median value of tPNa was statistically different (p=0.03) between euploid and aneuploid blastocysts (Euploid blastocysts=8.9 hpi; aneuploid blastocysts=10.3 hpi).

Conclusion:

The present findings are in line with the study hypothesis that kinetic analysis may reveal associations between cleavage patterns and embryo development to the blastocyst stage and ploidy status.

Artificial intelligence in the embryology laboratory: a review

The goal of an IVF cycle is a healthy live-born baby. Despite the many advances in the field of assisted reproductive technologies, accurately predicting the outcome of an IVF cycle has yet to be achieved. One reason for this is the method of selecting an embryo for transfer. Morphological assessment of embryos is the traditional method of evaluating embryo quality and selecting which embryo to transfer. However, this subjective method of assessing embryos leads to inter- and intra-observer variability, resulting in less than optimal IVF success rates. To overcome this, it is common practice to transfer more than one embryo, potentially resulting in high-risk multiple pregnancies. Although time-lapse incubators and preimplantation genetic testing for aneuploidy have been introduced to help increase the chances of live birth, the outcomes remain less than ideal. Utilization of artificial intelligence (AI) has become increasingly popular in the medical field and is increasingly being leveraged in the embryology laboratory to help improve IVF outcomes. Many studies have been published investigating the use of AI as an unbiased, automated approach to embryo assessment. This review summarizes recent AI advancements in the embryology laboratory.

Artificial intelligence in human in vitro fertilization and embryology

Embryo evaluation and selection embody the aggregate manifestation of the entire in vitro fertilization (IVF) process. It aims to choose the "best" embryos from the larger cohort of fertilized oocytes, the majority of which will be determined to be not viable either as a result of abnormal development or due to chromosomal imbalances. Indeed, it is generally acknowledged that even after embryo selection based on morphology, time-lapse microscopic photography, or embryo biopsy with preimplantation genetic testing, implantation rates in the human are difficult to predict. Our pursuit of enhancing embryo evaluation and selection, as well as increasing live birth rates, will require the adoption of novel technologies. Recently, several artificial intelligence (AI)-based methods have emerged as objective, standardized, and efficient tools for evaluating human embryos. Moreover, AI-based methods can be implemented for other clinical aspects of IVF, such as assessing patient reproductive potential and individualizing gonadotropin stimulation protocols. As AI has the capability to analyze "big" data, the ultimate goal will be to apply AI tools to the analysis of all embryological, clinical, and genetic data in an effort to provide patient-tailored treatments. In this chapter, we present an overview of existing AI technologies in reproductive medicine and envision their potential future applications in the field.

When embryology meets genetics: the definition of developmentally incompetent preimplantation embryos (DIPE)-the consensus of two Italian scientific societies

A clear definition of developmentally incompetent preimplantation embryo (DIPE) in literature is still missing, while several scientific societies are discussing this challenging topic. From both a clinical and scientific perspective, the identification of embryos unfit for reproductive purpose is crucial. This aim should be pursued in light of all diagnostic technologies for embryo evaluation, encompassing also genetic analyses, of recent implementation in IVF. The Italian context is characterized by an unusual scenario: embryos can be discarded only if not viable and cannot be used for research purposes either. Therefore, thousands of embryos, diagnosed as affected and/or aneuploid as resulting from preimplantation genetic testing (PGT) and clinically not utilizable, are cryopreserved and stored indefinitely, with important psychological, legal, and financial implications. With the aim of updating the definition of DIPE, also on the basis of the embryo genetic status, the Italian Society of Embryology, Reproduction and Research (SIERR) and the Italian Society of Human Genetic (SIGU) reviewed the literature on this topic, found a consensus, and produced a list of relevant criteria.

The clinical use of time-lapse in human-assisted reproduction

A major challenge in the assisted reproduction laboratory is to set up reproducible and efficient criteria to identify the embryo with the highest developmental potential. Over the years, several methods have been used worldwide with this purpose. Initially, standard morphology assessment was the only available strategy. It is now universally recognized that besides being a very subjective embryo selection strategy, morphology evaluation alone has a very poor prognostic value. More recently, the availability of time-lapse incubators allowed a continuous monitoring of human embryo development. This technology has spread quickly and many fertility clinics over the world produced a remarkable amount of data. To date, however, a general consensus on which variables, or combination of variables, should play a central role in embryo selection is still lacking. Many confounding factors, concerning both patient features and clinical and biological procedures, have been observed to influence embryo development. In addition, several studies have reported unexpected positive outcomes, even in the presence of abnormal developmental criteria. While it does not seem that time-lapse technology is ready to entirely replace the more invasive preimplantation genetic testing in identifying the embryo with the highest implantation potential, it is certainly true that its application is rapidly growing, becoming progressively more accurate. Studies involving artificial intelligence and deep-learning models as well as combining morphokinetic with other non-invasive markers of embryo development, are currently ongoing, raising hopes for its successful applicability for clinical purpose in the near future. The present review mainly focuses on data published starting from the first decade of 2000, when time-lapse technology was introduced as a routine clinical practice in the infertility centers.

Time of morulation and trophectoderm quality are predictors of a live birth after euploid blastocyst transfer: a multicenter study

OBJECTIVE

To investigate whether the morphodynamic characterization of a euploid blastocyst's development allows a higher prediction of a live birth after single-embryo-transfer (SET).

DESIGN

Observational cohort study conducted in two phases: training and validation.

SETTING

Private in vitro fertilization centers.

PATIENT(S)

Euploid blastocysts: 511 and 319 first vitrified-warmed SETs from 868 and 546 patients undergoing preimplantation genetic testing for aneuploidies (PGT-A) in the training and validation phase, respectively.

INTERVENTION(S)

Data collected from time of polar body extrusion to time of starting blastulation, and trophectoderm and inner-cell-mass static morphology in all embryos cultured in a specific time-lapse incubator with a continuous medium. Logistic regressions conducted to outline the variables showing a statistically significant association with live birth. In the validation phase, these variables were tested in an independent data set.

MAIN OUTCOME MEASURE(S)

Live births per SET.

RESULT(S)

The average live birth rate (LBR) in the training set was 40% (N = 207/511). Only time of morulation (tM) and trophectoderm quality were outlined as putative predictors of live birth at two IVF centers. In the validation set, the euploid blastocysts characterized by tM <80 hours and high-quality trophectoderm resulted in a LBR of 55.2% (n = 37/67), while those with tM ≥ 80 hours and a low-quality trophectoderm resulted in a LBR of 25.5% (N = 13/51).

CONCLUSION(S)

Time of morulation and trophectoderm quality are better predictors of a euploid blastocyst's reproductive competence. Our evidence was reproducible across different centers under specific culture conditions. These data support the crucial role of morulation for embryo development, a stage that involves massive morphologic, cellular, and molecular changes and deserves more investigation.

Does polycystic ovary syndrome affect morphokinetics or abnormalities in early embryonic development?

OBJECTIVES

This study aimed to investigate whether oocyte retrieval from PCOS patients affects the morphokinetics or the incidence of abnormalities in early embryonic development, using time-lapse imaging analysis.

METHODS

This was a prospective study in total of 39 PCOS patients and 67 women with normal ovarian function, underwent a GnRH antagonist protocol of controlled ovarian stimulation and fertilization by ICSI. 402 zygotes from the PCOS group and 449 zygotes from the control group were observed by time-lapse monitoring for 48 h following sperm injection.

RESULTS

Patients with PCOS showed a significantly higher number of retrieved oocytes, and a significantly higher number of metaphase II oocytes per cycle than that of the non-PCOS group (18.85 ± 9.41 vs. 11.48 ± 5.51 and 14.97 ± 7.43 vs. 9.51 ± 4.7, respectively). However, oocyte maturation rate and morphologically assessed embryo quality did not differ between two groups. After 48 h of the embryo culture using time-lapse video recording, most of the embryos in both groups had reached the 4-cell stage (353/449 vs. 314/402 embryos). There were no significant differences between the control and PCOS group regarding the time-points of the successive events in early embryonic development from the appearance of 2 polar bodies to the 6-cell stage. However, the percentage of t2 stages which fell in "optimal range" (>24 h and ≤28 h) was significantly lower in the PCOS group than in the control group (37.8% vs. 48.1%, P = 0.004). The proportion of embryos manifesting abnormal fertility, multinucleation, direct cleavage and reverse cleavage were not significantly different between the two groups.

CONCLUSION

Our data indicated that PCOS does not affect the morphokinetics or incidence of abnormalities in early embryonic development.

An integrated investigation of oocyte developmental competence: expression of key genes in human cumulus cells, morphokinetics of early divisions, blastulation, and euploidy

PURPOSE

To investigate the association of cumulus cell (CC)-related expression of a selected cluster of key genes (PTGS2, CAMK1D, HAS2, STC1, and EFNB2) with embryo development to blastocyst.

METHODS

Exploratory study at a private clinic. Eighteen advanced maternal age patients were enrolled (37.3 ± 4.0 years). Seventy-five cumuli were collected, whose oocytes resulted in either developmental arrest (N = 33) or blastocyst formation (N = 42). The noninvasive CC gene expression was combined with time-lapse morphokinetic parameters and, for blastocysts, with qPCR-based aneuploidy testing on trophectoderm biopsies.

RESULTS

The detection rate was 100% for all transcripts, but STC1 (96%) and CAMK1D (89%). Among amplified assays, CC mean expression levels of CAMK1D, PTGS2, and HAS2 were higher from oocytes that developed to blastocyst. No difference in CC key gene expression was reported between euploid (N = 21) and aneuploid (N = 21) blastocysts. Some timings of early embryo development were faster in embryos developing to blastocyst (time of pronuclei appearance and fading, division to two- and four-cells, first and second cell cycles). However, the generalized linear models outlined increasing CAMK1D expression levels as the strongest parameter associated with oocytes' developmental potential from both a general (AUC = 0.78 among amplified samples) and an intrapatient perspectives (AUC = 0.9 among patients obtaining ≥ 2 zygotes from the cohort with different developmental outcomes).

CONCLUSIONS

CAMK1D level of expression in CCs associated with blastocyst development. If confirmed from larger studies in wider populations of patients, the investigation of CC key gene expression might suit IVF clinics not adopting blastocyst culture. Future investigations should clarify the role of CAMK1D in ovarian physiology and could provide novel insights on how oocytes gain competence during folliculogenesis.