Clinical Usability of Embryo Development Using a Combined Qualitative and Quantitative Approach in a Single Vitrified-Warmed Blastocyst Transfer: Assessment of Pre-Vitrified Blastocyst Diameter and Post-Warmed Blastocyst Re-Expansion Speed

Time-lapse imaging of morula compaction for selecting high-quality blastocysts: a retrospective cohort study

PURPOSE

Before blastocyst development, embryos undergo morphological and metabolic changes crucial for their subsequent growth. This study aimed to investigate the relationship between morula compaction and blastocyst formation and the subsequent chromosomal status of the embryos.

METHODS

This retrospective cohort study evaluated embryo development (n = 371) using time-lapse imaging; 94 blastocysts underwent preimplantation genetic testing for aneuploidy (PGT-A).

RESULTS

The embryos were classified as fully (Group 1, n = 194) or partially (Group 2, n = 177) compacted. Group 1 had significantly higher proportions of good- and average-quality blastocysts than Group 2 (21.6% vs. 3.4%, p = 0.001; 47.9% vs. 26.6%, p = 0.001, respectively). The time from the morula stage to the beginning and completion of compaction and blastocyst formation was significantly shorter in Group 1 than in Group 2 (78.6 vs. 82.4 h, p = 0.001; 87.0 vs. 92.2 h, p = 0.001; 100.2 vs. 103.7 h, p = 0.017, respectively). Group 1 embryos had larger surface areas than Group 2 embryos at various time points following blastocyst formation. Group 1 blastocysts had significantly higher average expansion rates than Group 2 blastocysts (653.6 vs. 499.2 μm2/h, p = 0.001). PGT-A revealed a higher proportion of euploid embryos in Group 1 than in Group 2 (47.2% vs. 36.6%, p = 0.303).

CONCLUSION

Time-lapse microscopy uncovered a positive relationship between compaction and blastocyst quality and its association with embryo ploidy. Hence, compaction evaluation should be prioritized before blastocyst selection for transfer or cryopreservation.

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.

Using blastocyst re-expansion rate for deciding when to warm a new blastocyst for single vitrified-warmed blastocyst transfer

RESEARCH QUESTION

Can predictive post-warm parameters that support the decision to transfer a warmed blastocyst or to warm another blastocyst be identified in women with multiple frozen-vitrified blastocysts?

DESIGN

Retrospective single-centre observational cohort analysis. A total of 1092 single vitrified-warmed blastocyst transfers (SVBT) with known Gardner score, maternal age and live birth were used to develop live birth prediction models based on logistic regression, including post-warm re-expansion parameters. Time-lapse incubation was used for pre-vitrification and post-warm embryo culture. A dataset of 558 SVBT with the same inclusion criteria was used to validate the model, but with known clinical pregnancy outcome instead of live birth outcome.

RESULTS

Three different logistic regression models were developed for predicting live birth based on post-warm blastocyst re-expansion. Different post-warm assessment times indicated that a 2-h post-warm culture period was optimal for live birth prediction (model 1). Adjusting for pre-vitrification Gardner score (model 2) and in combination with maternal age (model 3) further increased predictability (area under the curve [AUC] = 0.623, 0.633, 0.666, respectively). Model validation gave an AUC of 0.617, 0.609 and 0.624, respectively. The false negative rate and true negative rate for model 3 were 2.0 and 10.1 in the development dataset and 3.5 and 8.0 in the validation dataset.

CONCLUSIONS

Clinical application of a simple model based on 2 h of post-warm re-expansion data, pre-vitrification Gardner score and maternal age can support a standardized approach for deciding if warming another blastocyst may increase the likelihood of live birth in SVBT.

Development and validation of a nomogram for predicting ongoing pregnancy in single vitrified-warmed blastocyst embryo transfer cycles

Introduction

The global adoption of the "freeze-all strategy" has led to a continuous increase in utilization of single vitrified-warmed blastocyst embryo transfer (SVBT) owing to its clinical effectiveness. Accurate prediction of clinical pregnancy is crucial from a patient-centered perspective. However, this remains challenging, with inherent limitations due to the absence of precise and user-friendly prediction tools. Thus, this study primarily aimed to develop and assess a nomogram based on quantitative clinical data to optimize the efficacy of personalized prognosis assessment.

Materials and methods

We conducted a retrospective cohort analysis of ongoing pregnancy data from 658 patients with infertility who underwent SVBT at our center between October 17, 2017, and December 18, 2021. Patients were randomly assigned to the training (n=461) or validation (n=197) cohort for nomogram development and testing, respectively. A nomogram was constructed using the results of the multivariable logistic regression (MLR), which included clinical covariates that were assessed for their association with ongoing pregnancy.

Results

The MLR identified eight significant variables that independently predicted ongoing pregnancy outcomes in the study population. These predictors encompassed maternal physiology, including maternal age at oocyte retrieval and serum anti-Müllerian hormone levels; uterine factors, such as adenomyosis; and various embryo assessment parameters, including the number of fertilized embryos, blastocyst morphology, blastulation day, blastocyst re-expansion speed, and presence of embryo string. The area under the receiver operating characteristic curve in our prediction model was 0.675 (95% confidence interval [CI], 0.622-0.729) and 0.656 (95% CI, 0.573-0.739) in the training and validation cohorts, respectively, indicating good discrimination performance in both cohorts.

Conclusions

Our individualized nomogram is a practical and user-friendly tool that can provide accurate and useful SVBT information for patients and clinicians. By offering this model to patients, clinical stakeholders can alleviate uncertainty and confusion about fertility treatment options and enhance patients' confidence in making informed decisions.

Time-lapse observation of mouse preimplantation embryos using a simple closed glass capillary method

Time-lapse observation is a popular method for analyzing mammalian preimplantation embryos, but it often requires expensive equipment and skilled techniques. We previously developed a simply and costly embryo-culture system in a sealed tube that does not require a CO2 incubator. In the present study, we developed a new time-lapse observation system using our previous culture method and a glass capillary. Zygotes were placed in a glass capillary and sunk in oil for observation under a stereomicroscope. Warming the capillary using a thermoplate enabled most of the zygotes to develop into blastocysts and produce healthy offspring. This time-lapse observation system captured images every 30 min for up to 5 days, which confirmed that the developmental speed and quality of the embryos were not affected, even with fluorescence. Overall, this new system is a simple time-lapse observation method for preimplantation embryos that does not require dedicated machines and advanced techniques.

Embryo selection through artificial intelligence versus embryologists: a systematic review

STUDY QUESTION

What is the present performance of artificial intelligence (AI) decision support during embryo selection compared to the standard embryo selection by embryologists?

SUMMARY ANSWER

AI consistently outperformed the clinical teams in all the studies focused on embryo morphology and clinical outcome prediction during embryo selection assessment.

WHAT IS KNOWN ALREADY

The ART success rate is ∼30%, with a worrying trend of increasing female age correlating with considerably worse results. As such, there have been ongoing efforts to address this low success rate through the development of new technologies. With the advent of AI, there is potential for machine learning to be applied in such a manner that areas limited by human subjectivity, such as embryo selection, can be enhanced through increased objectivity. Given the potential of AI to improve IVF success rates, it remains crucial to review the performance between AI and embryologists during embryo selection.

STUDY DESIGN SIZE DURATION

The search was done across PubMed, EMBASE, Ovid Medline, and IEEE Xplore from 1 June 2005 up to and including 7 January 2022. Included articles were also restricted to those written in English. Search terms utilized across all databases for the study were: ('Artificial intelligence' OR 'Machine Learning' OR 'Deep learning' OR 'Neural network') AND ('IVF' OR 'in vitro fertili*' OR 'assisted reproductive techn*' OR 'embryo'), where the character '*' refers the search engine to include any auto completion of the search term.

PARTICIPANTS/MATERIALS SETTING METHODS

A literature search was conducted for literature relating to AI applications to IVF. Primary outcomes of interest were accuracy, sensitivity, and specificity of the embryo morphology grade assessments and the likelihood of clinical outcomes, such as clinical pregnancy after IVF treatments. Risk of bias was assessed using the Modified Down and Black Checklist.

MAIN RESULTS AND THE ROLE OF CHANCE

Twenty articles were included in this review. There was no specific embryo assessment day across the studies-Day 1 until Day 5/6 of embryo development was investigated. The types of input for training AI algorithms were images and time-lapse (10/20), clinical information (6/20), and both images and clinical information (4/20). Each AI model demonstrated promise when compared to an embryologist's visual assessment. On average, the models predicted the likelihood of successful clinical pregnancy with greater accuracy than clinical embryologists, signifying greater reliability when compared to human prediction. The AI models performed at a median accuracy of 75.5% (range 59-94%) on predicting embryo morphology grade. The correct prediction (Ground Truth) was defined through the use of embryo images according to post embryologists' assessment following local respective guidelines. Using blind test datasets, the embryologists' accuracy prediction was 65.4% (range 47-75%) with the same ground truth provided by the original local respective assessment. Similarly, AI models had a median accuracy of 77.8% (range 68-90%) in predicting clinical pregnancy through the use of patient clinical treatment information compared to 64% (range 58-76%) when performed by embryologists. When both images/time-lapse and clinical information inputs were combined, the median accuracy by the AI models was higher at 81.5% (range 67-98%), while clinical embryologists had a median accuracy of 51% (range 43-59%).

LIMITATIONS REASONS FOR CAUTION

The findings of this review are based on studies that have not been prospectively evaluated in a clinical setting. Additionally, a fair comparison of all the studies were deemed unfeasible owing to the heterogeneity of the studies, development of the AI models, database employed and the study design and quality.

WIDER IMPLICATIONS OF THE FINDINGS

AI provides considerable promise to the IVF field and embryo selection. However, there needs to be a shift in developers' perception of the clinical outcome from successful implantation towards ongoing pregnancy or live birth. Additionally, existing models focus on locally generated databases and many lack external validation.

STUDY FUNDING/COMPETING INTERESTS

This study was funded by Monash Data Future Institute. All authors have no conflicts of interest to declare.

REGISTRATION NUMBER

CRD42021256333.