Use of imaging software for assessment of the associations among zona pellucida thickness variation, assisted hatching, and implantation of day 3 embryos

BlastAssist: a deep learning pipeline to measure interpretable features of human embryos

STUDY QUESTION

Can the BlastAssist deep learning pipeline perform comparably to or outperform human experts and embryologists at measuring interpretable, clinically relevant features of human embryos in IVF?

SUMMARY ANSWER

The BlastAssist pipeline can measure a comprehensive set of interpretable features of human embryos and either outperform or perform comparably to embryologists and human experts in measuring these features.

WHAT IS KNOWN ALREADY

Some studies have applied deep learning and developed 'black-box' algorithms to predict embryo viability directly from microscope images and videos but these lack interpretability and generalizability. Other studies have developed deep learning networks to measure individual features of embryos but fail to conduct careful comparisons to embryologists' performance, which are fundamental to demonstrate the network's effectiveness.

STUDY DESIGN, SIZE, DURATION

We applied the BlastAssist pipeline to 67 043 973 images (32 939 embryos) recorded in the IVF lab from 2012 to 2017 in Tel Aviv Sourasky Medical Center. We first compared the pipeline measurements of individual images/embryos to manual measurements by human experts for sets of features, including: (i) fertilization status (n = 207 embryos), (ii) cell symmetry (n = 109 embryos), (iii) degree of fragmentation (n = 6664 images), and (iv) developmental timing (n = 21 036 images). We then conducted detailed comparisons between pipeline outputs and annotations made by embryologists during routine treatments for features, including: (i) fertilization status (n = 18 922 embryos), (ii) pronuclei (PN) fade time (n = 13 781 embryos), (iii) degree of fragmentation on Day 2 (n = 11 582 embryos), and (iv) time of blastulation (n = 3266 embryos). In addition, we compared the pipeline outputs to the implantation results of 723 single embryo transfer (SET) cycles, and to the live birth results of 3421 embryos transferred in 1801 cycles.

PARTICIPANTS/MATERIALS, SETTING, METHODS

In addition to EmbryoScope™ image data, manual embryo grading and annotations, and electronic health record (EHR) data on treatment outcomes were also included. We integrated the deep learning networks we developed for individual features to construct the BlastAssist pipeline. Pearson's χ2 test was used to evaluate the statistical independence of individual features and implantation success. Bayesian statistics was used to evaluate the association of the probability of an embryo resulting in live birth to BlastAssist inputs.

MAIN RESULTS AND THE ROLE OF CHANCE

The BlastAssist pipeline integrates five deep learning networks and measures comprehensive, interpretable, and quantitative features in clinical IVF. The pipeline performs similarly or better than manual measurements. For fertilization status, the network performs with very good parameters of specificity and sensitivity (area under the receiver operating characteristics (AUROC) 0.84-0.94). For symmetry score, the pipeline performs comparably to the human expert at both 2-cell (r = 0.71 ± 0.06) and 4-cell stages (r = 0.77 ± 0.07). For degree of fragmentation, the pipeline (acc = 69.4%) slightly under-performs compared to human experts (acc = 73.8%). For developmental timing, the pipeline (acc = 90.0%) performs similarly to human experts (acc = 91.4%). There is also strong agreement between pipeline outputs and annotations made by embryologists during routine treatments. For fertilization status, the pipeline and embryologists strongly agree (acc = 79.6%), and there is strong correlation between the two measurements (r = 0.683). For degree of fragmentation, the pipeline and embryologists mostly agree (acc = 55.4%), and there is also strong correlation between the two measurements (r = 0.648). For both PN fade time (r = 0.787) and time of blastulation (r = 0.887), there's strong correlation between the pipeline and embryologists. For SET cycles, 2-cell time (P < 0.01) and 2-cell symmetry (P < 0.03) are significantly correlated with implantation success rate, while other features showed correlations with implantation success without statistical significance. In addition, 2-cell time (P < 5 × 10-11), PN fade time (P < 5 × 10-10), degree of fragmentation on Day 3 (P < 5 × 10-4), and 2-cell symmetry (P < 5 × 10-3) showed statistically significant correlation with the probability of the transferred embryo resulting in live birth.

LIMITATIONS, REASONS FOR CAUTION

We have not tested the BlastAssist pipeline on data from other clinics or other time-lapse microscopy (TLM) systems. The association study we conducted with live birth results do not take into account confounding variables, which will be necessary to construct an embryo selection algorithm. Randomized controlled trials (RCT) will be necessary to determine whether the pipeline can improve success rates in clinical IVF.

WIDER IMPLICATIONS OF THE FINDINGS

BlastAssist provides a comprehensive and holistic means of evaluating human embryos. Instead of using a black-box algorithm, BlastAssist outputs meaningful measurements of embryos that can be interpreted and corroborated by embryologists, which is crucial in clinical decision making. Furthermore, the unprecedentedly large dataset generated by BlastAssist measurements can be used as a powerful resource for further research in human embryology and IVF.

STUDY FUNDING/COMPETING INTEREST(S)

This work was supported by Harvard Quantitative Biology Initiative, the NSF-Simons Center for Mathematical and Statistical Analysis of Biology at Harvard (award number 1764269), the National Institute of Heath (award number R01HD104969), the Perelson Fund, and the Sagol fund for embryos and stem cells as part of the Sagol Network. The authors declare no competing interests.

TRIAL REGISTRATION NUMBER

Not applicable.

Laser-assisted hatching improves pregnancy outcomes in frozen-thawed embryo transfer cycles of cleavage-stage embryos: a large retrospective cohort study with propensity score matching

INTRODUCTION

Laser-assisted hatching (LAH) is a commonly used adjunct technique; however, its effectiveness has not been fully established.

OBJECTIVE

We evaluated the effects of LAH on pregnancy outcomes in frozen-thawed embryo transfer (FET) cycles of cleavage-stage embryos.

MATERIALS AND METHODS

This retrospective study involved 5779 FET cycles performed at the Reproductive and Genetic Center in the Affiliated Hospital of Shandong University of Traditional Chinese Medicine between January 2016 and December 2020. After propensity score matching, 3535 FET cycles were included, out of which 1238 were subjected to LAH while the remaining 2297 cycles were non-LAH (NLAH). The primary outcomes were clinical pregnancy rate (CPR) and live birth rate (LBR) while secondary outcomes included implantation rate (IR), biochemical pregnancy rate (BPR), ectopic pregnancy rate (EPR), pregnancy loss rate (PLR), multiple pregnancy rate (MPL), and monozygotic twinning rate (MTR). Logistic regression analysis was conducted to adjust for possible confounders. Subgroup analysis was also performed based on the endometrial preparation regimen.

RESULTS

The LAH group exhibited a higher LBR, compared to the NLAH group (34.9% vs. 31.4%, OR = 1.185, 95% CI = 1.023, 1.374, P = 0.024). Additionally, the LAH group showed a decreasing trend in PLR and EPR; however, differences were insignificant (P = 0.078, P = 0.063 respectively). Differences in IR (24.6% vs. 24.3%), BPR (41.8% vs. 40.4%), CPR (40.7% vs. 38.3%), MPR (14.1% vs. 17.3%), and MTR (1.4% vs. 1.1%) were insignificant. Subgroup analysis revealed that LAH may be more conducive for pregnancy outcomes in hormone replacement cycles.

CONCLUSIONS

In summary, LAH has an increased chance of achieving live births. However, further prospective studies should be performed to confirm our findings.

Assessment of the zona pellucida microdissection on its thickness in mammalian embryos

The zona pellucida (ZP) is a dynamically changing object that plays an important role during the preimplantation stage of embryogenesis. The ZP thickness may affect the implantation success and pregnancy rate, it is considered as a prognostic factor in a number of studies. The study was aimed to assess the dynamic changes in the mouse embryonic ZP thickness after laser assisted hatching (LAH) that involved breaching the ZP integrity at the blastocyst stage. Femtosecond laser pulses were used to perform the zona microsurgery. The zona thickness was measured both at the stage of blastocyst microsurgery (~Е3.5, i.e. 3.5 days of embryogenesis) and at the hatching stage (~Е5). Significant differences in the ZP thickness were revealed in the control group of embryos: from 6.21 µm (Е3.5) to 5.4 µm (Е5). The changes in thickness from 6.6 µm (Е3.5) to 6.2 µm (Е5) observed in the group subjected to LAH were non-significant. Tracing the ZP thickness of a particular embryo from the blastocyst stage to the hatching stage made it possible to estimate the thinning coefficients in the experimental and control groups. The findings that indicate lower tensile strength of the zona in case of LAH can provide the basis for further research on the ZP properties in case of using the embryo cryopreservation protocols.