Delineating the heterogeneity of embryo preimplantation development using automated and accurate morphokinetic annotation.

PURPOSE: Our objective was to design an automated deep learning model that extracts the morphokinetic events of embryos that were recorded by time-lapse incubators. Using automated annotation, we set out to characterize the temporal heterogeneity of preimplantation development across a large number of embryos. METHODS: To perform a retrospective study, we used a dataset of video files of 67,707 embryos from four IVF clinics. A convolutional neural network (CNN) model was trained to assess the developmental states that appear in single frames from 20,253 manually-annotated embryos. Probability-weighted superposition of multiple predicted states was permitted, thus accounting for visual uncertainties. Superimposed embryo states were collapsed onto discrete series of morphokinetic events via monotonic regression of whole-embryo profiles. Unsupervised K-means clustering was applied to define subpopulations of embryos of distinctive morphokinetic profiles. RESULTS: We perform automated assessment of single-frame embryo states with 97% accuracy and demonstrate whole-embryo morphokinetic annotation with R-square 0.994. High quality embryos that had been valid candidates for transfer were clustered into nine subpopulations, as characterized by distinctive developmental dynamics. Retrospective comparative analysis of transfer versus implantation rates reveals differences between embryo clusters as marked by poor synchronization of the third mitotic cell-cleavage cycle. CONCLUSIONS: By demonstrating fully automated, accurate, and standardized morphokinetic annotation of time-lapse embryo recordings from IVF clinics, we provide practical means to overcome current limitations that hinder the implementation of morphokinetic decision-support tools within clinical IVF settings due to inter-observer and intra-observer manual annotation variations and workload constrains. Furthermore, our work provides a platform to address embryo heterogeneity using dimensionality-reduced morphokinetic descriptions of preimplantation development.

Embryo classification beyond pregnancy: early prediction of first trimester miscarriage using machine learning.

PURPOSE: First trimester miscarriage is a major concern in IVF-ET treatments, accounting for one out of nine clinical pregnancies and for up to one out of three recognized pregnancies. To develop a machine learning classifier for predicting the risk of cleavage-stage embryos to undergo first trimester miscarriage based on time-lapse images of preimplantation development. METHODS: Retrospective study of a 4-year multi-center cohort of 391 women undergoing intra-cytoplasmatic sperm injection (ICSI) and fresh single or double embryo transfers. The study included embryos with positive indication of clinical implantation based on gestational sac visualization either with first trimester miscarriage or live-birth outcome. Miscarriage was determined based on negative fetal heartbeat indication during the first trimester. Data were recorded and obtained in hospital setting and research was performed in university setting. RESULTS: A minimal subset of six non-redundant morphodynamic features were screened that maintained high prediction capacity. Features that account for the distribution of the nucleolus precursor bodies within the small pronucleus and pronuclei dynamics were highly predictive of miscarriage outcome as evaluated using the SHapley Additive exPlanations (SHAP) methodology. Using this feature subset, XGBoost and random forest models were trained following a 100-fold Monte-Carlo cross validation scheme. Miscarriage was predicted with AUC 0.68 to 0.69. CONCLUSION: We report the development of a decision-support tool for identifying the embryos with high risk of miscarriage. Prioritizing embryos for transfer based on their predicted risk of miscarriage in combination with their predicted implantation potential is expected to improve live-birth rates and shorten time-to-pregnancy.

Prediction of embryo implantation rate using a sole parameter of timing of starting blastulation.

A time-lapse monitoring system provides a complete picture of the dynamic embryonic development process and simultaneously supplies extensive morphokinetic data. The objective of this study was to investigate whether the use of the morphokinetic parameter of time of starting blastulation (tSB) can improve the implantation rate of day-5 transferred blastocyst selected based on morphological parameters. In this retrospective study we analyzed the morphokinetics of 196 day-5 transferred blastocysts, selected solely based on morphological parameters. The interval time from intracytoplasmic sperm injection (ICSI) to time of starting blastocyst formation (tSB) was calculated for each embryo. The overall implantation rate of transferred blastocyst, selected based only on morphological parameters, was 49.2%. Implantation rate, determined retrospectively, was significantly higher (58.8% versus 42.6%, P = 0.02) for embryos with a short interval time to tSB (78-95.9 h) compared with embryos with a longer timeframe (96-114 h). Time of expanded blastocyst (tEB) post-ICSI was also significantly associated with implantation; however, this parameter was not available for all the embryos at time of transfer. When we tested only high ranked KIDScore day-3 sub-group embryos, the implantation rate was significantly higher in short interval time embryos compared with longer interval time embryos (62.2% vs. 45.5%, respectively, P = 0.02).These observations emphasize the importance of the timing of starting blastulation over blastocyst morphological parameters and may provide a preferable criterion for good morphology day-5 blastocyst selection.

Are morphokinetic parameters of embryo development associated with adverse perinatal outcomes following fresh blastocyst transfer?

RESEARCH QUESTION: Are obstetric and perinatal complications associated with morphokinetic parameters of embryo development? DESIGN: This proof-of-concept pilot study included a retrospective analysis of embryo morphokinetic parameters of 85 live births following day 5 single blastocyst transfer. Kinetic variables included time interval (hours) from time of pronuclei fading (tPNf) to: time of 2 cells (tPNf-t2), 9 cells (tPNf-t9), morula (tPNf-tM), start of blastulation (tPNf-tSB), full blastocyst (tPNf-tB) and expanded blastocyst (tPNf-tEB). Multivariable logistic models were used to calculate the risk of perinatal complications after adjustment for confounders. RESULTS: The mean interval of tPNf-tSB was significantly longer for newborns with congenital anomalies compared with healthy newborns (79.49 ± 5.78 versus 71.7 ± 6.3, respectively, P = 0.01) and for embryos of women who had gestational diabetes mellitus compared with normoglycemic women (76.56 ± 7.55 versus 71.5 ± 6.13, respectively, P = 0.015). The mean interval of tPNf-t9 was significantly longer for low-birthweight newborns compared with normal weight (49.25 ± 5.54 versus 45.47 ± 4.77, respectively, P = 0.01). Preterm delivery was associated with several longer intervals of cell divisions compared with delivery at term (tPNf-t5: 28.76 ± 3.13 versus 26.64 ± 2.40, respectively, P = 0.01; tPNf-t6: 30.10 ± 3.05 versus 27.68 ± 2.30, respectively, P < 0.001; tPNf-t7: 32.08 ± 4.11 versus 28.70 ± 2.67, respectively, P < 0.001; tPNf-t8: 34.75 ± 4.95 versus 30.70 ± 4.10, respectively, P < 0.001; tPNf-t9: 50.23 ± 5.87 versus 45.44 ± 4.67, respectively, P < 0.001). For each of the outcomes, the association remained significant after adjusting for confounders. CONCLUSION: This study indicates that there may be a possible association between adverse perinatal outcomes and morphokinetic parameters. Larger studies are needed to establish this association.

Arabinogalactan proteins 6 and 11 are required for stamen and pollen function in Arabidopsis.

Successful male reproductive function in plants is dependent on the correct development and functioning of stamens and pollen. AGP6 and AGP11 are two homologous Arabidopsis genes encoding cell wall-associated arabinogalactan glycoproteins (AGPs). Both genes were found to be specifically expressed in stamens, pollen grains and pollen tubes, suggesting that these genes may play a role in male organ development and function. RNAi lines with reduced AGP6 and AGP11 expression were generated. These, together with lines harboring point mutations in the coding region of AGP6, were used to show that loss of function in AGP6 and AGP11 led to reduced fertility, at least partly as a result of inhibition of pollen tube growth. Our results also suggest that AGP6 and AGP11 play an additional role in the release of pollen grains from the mature anther. Thus, our study demonstrates the involvement of specific AGPs in pollen tube growth and stamen function.