The combination of pronuclear and blastocyst morphology: a strong prognostic tool for implantation potential

Braga, D.P.A.F.; Setti, Amanda Souza; Figueira, Rita de Cássia Sávio; Iaconelli, Assumpto; Borges, Edson

doi:10.1007/s10815-013-0073-3

Cited by 9 publications

(4 citation statements)

References 38 publications

(51 reference statements)

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“…Among these characteristics, that of the zygote quality seems to be of particular importance, since it was previously shown that zygote pronuclear morphology is related not only with IVF clinical outcomes ( 25 – 28 ) and the rate of embryo development to the blastocyst stage ( 27 ), but also with the normal ploidy of the resulting blastocysts ( 29 – 31 ). When used in combination with further evaluation of embryo morphology during subsequent stages of preimplantation development, the prognostic value of zygote morphology, as to the probability of establishing a normal pregnancy, was further enhanced ( 32 , 33 ).…”

Section: Discussionmentioning

confidence: 99%

The Effect of GH Administration on Oocyte and Zygote Quality in Young Women With Repeated Implantation Failure After IVF

Galán-Lázaro¹,

Conde-López²,

Chiara-Rapisarda

et al. 2020

Front. Endocrinol.

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Growth hormone (GH) has been shown to improve implantation and live birth rates in women of >40 years of age treated by in vitro fertilization (IVF). This effect was initially attributed to a GH effect on oocyte quality, but later studies showed that GH can also improve uterine receptivity for embryo implantation. As to younger women with previous failures of embryo implantation after IVF, data reported in the literature are ambiguous. This retrospective study focused on this latter category of women, comparing the numbers and morphological appearance of oocytes recovered from women with two previous IVF failures, aged between 30 and 39 years and treated with GH, with a comparable group of women without GH treatment. These results were complemented with the analysis of morphological markers of zygote and embryo quality and IVF clinical outcomes in both groups. The oocytes, zygotes and embryos from women treated with GH showed better morphological scores, and their uterine transfer resulted in more implantations, pregnancies and live births, as compared with the untreated group. It is concluded that the improvement of IVF outcomes in women with previous repeated IVF failures by exogenous GH administration is, at least partly, related to an increase in oocyte developmental potential. The statistically evident improvement of oocyte and embryo quality is the main finding of this study. Its weakness is its retrospective nature.

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Section: Discussionmentioning

confidence: 99%

The Effect of GH Administration on Oocyte and Zygote Quality in Young Women With Repeated Implantation Failure After IVF

Galán-Lázaro¹,

Conde-López²,

Chiara-Rapisarda

et al. 2020

Front. Endocrinol.

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“…Static morphological features are routinely used for evaluating the potential of embryos to implant [24][25][26][27]. The embryos were characterized by their two-cell and four-cell fragmentation percentage and blastomere size symmetry, as annotated in adherence to established criteria [28].…”

Section: Extraction Of Morphological Featuresmentioning

confidence: 99%

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

Amitai

Kan‐Tor

et al. 2022

J Assist Reprod Genet

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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.

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“…Extraction of morphological features: Static morphological features are routinely used for evaluating the potential of embryos to implant. [23][24][25][26] The embryos were characterized by their twocell and four-cell fragmentation percentage and blastomere size symmetry, as annotated in adherence to established criteria. 27 Additional morphological and geometrical features of the zona pellucida, ooplasm, pronuclei (PN'i) and nucleolus precursor bodies (NPB's) were semiautomatically extracted per the third frame prior to tPNf (~one hour) using a custom designed code (Python).…”

Section: Feature Extractionmentioning

confidence: 99%

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

Amitai

Kan‐Tor

Srebnik

et al. 2020

Preprint

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ObjectiveDevelop 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.DesignRetrospective study of a 4-year multi-center cohort of women undergoing intra-cytoplasmatic sperm injection (ICSI). 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.SettingData were recorded and obtained in hospital setting and research was performed in university setting.Patient(s)Data from 391 women who underwent fresh single or double embryo transfers were included.Intervention(s)None.Main Outcome Measure(s)A minimal subset of six non-redundant morphodynamic features were screened that maintain high prediction capacity. Using this feature subset, XGBoost and Random Forest models were trained following a 100-fold Monte-Carlo cross validation scheme. Feature importance was scored using the SHapley Additive exPlanations (SHAP) methodology. Miscarriage versus live-birth outcome prediction was evaluated using a non-contaminated balanced test set and quantified in terms of the area under the receiver operating characteristic (ROC) curve (AUC), precision-recall curve, positive predictive value (PPV), and confusion matrices.Result(s)Features that account for the distribution of the nucleolus precursor bodies within the small pronucleus and pronuclei dynamics were highly predictive of miscarriage outcome. AUC for miscarriage prediction of validation and test set embryos using both models was 0.68-to-0.69. Clinical utility was tested by setting two classification thresholds accounting for high sensitivity 0.73 with 0.6 specificity and high specificity 0.93 with 0.33 sensitivity.Conclusion(s)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 will improve live-birth rates and shorten time-to-pregnancy.CapsuleThe risk of first trimester miscarriage of cleavage stage embryos is predicted with AUC 68% by screening a minimal subset of six non-redundant morpho-dynamic features and training a machine-learning classifier.

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The combination of pronuclear and blastocyst morphology: a strong prognostic tool for implantation potential

Cited by 9 publications

References 38 publications

The Effect of GH Administration on Oocyte and Zygote Quality in Young Women With Repeated Implantation Failure After IVF

The Effect of GH Administration on Oocyte and Zygote Quality in Young Women With Repeated Implantation Failure After IVF

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

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

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