Does conventional morphological evaluation still play a role in predicting blastocyst formation?

Jiang, Xiaoming; Cai, Jiali; Liu, Lanlan; Liu, Zhenfang; Wang, Wenjie; Chen, Jinhua; Yang, Chao; Geng, Jie; Ma, Caihui; Ren, Jianzhi

doi:10.1186/s12958-022-00945-y

Cited by 4 publications

(3 citation statements)

References 42 publications

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“…Currently, the main methods for assessing embrynic potential are morphological assessment and genetic testing for ploidy status. However, these criteria are not able to accurately identify embryos with a high chance of implantation [47]. Therefore, it is of utmost importance to find a noninvasive, reliable, and fast method for assessing embryos.…”

Section: Omics Analysis Of the Scm To Evaluate The Developmental Pote...mentioning

confidence: 99%

Noninvasive testing of preimplantation embryos in assisted reproductive technology

Zhou,

Wang

2024

Obstetrics and Gynecology

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One approach to improving the success of assisted reproductive technology (ART) is the careful selection of embryos prior to implantation. Although preimplantation genetic testing (PGT) is widely employed for embryo selection, it needs embryo biopsy and is detrimental to embryos. Thus, noninvasive testing of preimplantation embryos offers new possibilities for evaluating embryo quality. Here, we reviewed current progression of noninvasive embryo testing technologies, including the use of microscopy images combined with artificial intelligence (AI) to select embryos based on morphology, minimally invasive and noninvasive PGT of blastocoel fluid and spent embryo culture medium, and omics analysis of molecules in the culture medium to assess the developmental potential of embryos. More importantly, using the AI technology based on various type of data of each embryo will greatly improve the noninvasive embryo assessments. Thus, these cutting-edge technologies offer fresh insights into noninvasive testing of preimplantation embryos and have the potential to enhance the quality and efficiency of ART procedures.

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Section: Omics Analysis Of the Scm To Evaluate The Developmental Pote...mentioning

confidence: 99%

Noninvasive testing of preimplantation embryos in assisted reproductive technology

Zhou,

Wang

2024

Obstetrics and Gynecology

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“…Routine IVF protocol in our center was carried out (Jiang et al, 2022). Cumulus-oocyte complexes were co-cultured with approximate 1.5-3 X 10 5 progressively motile spermatozoa in pre-equilibrated fertilization culture medium (K-SIFM, Cook) under mineral oil in traditional incubators (C200, Labotect) at 37 °C, 6% CO2 and 5% O2 in a humidified atmosphere.…”

Section: Treatment Protocol and Fertilization Checkmentioning

confidence: 99%

Predicting the unexpected total fertilization failure in conventional in vitro fertilization cycles: What is the role of semen quality?

Jiang

Cai²,

Liu³

et al. 2023

Front. Cell Dev. Biol.

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Background: Male and female gametes factors might contribute to the total fertilization failure (TFF). In first in vitro fertilization (IVF) cycles, decision-making of insemination protocol was mainly based on semen quality for the contribution of female clinical characteristics to TFF remained obscure. The purpose of the study was to evaluate the role of semen quality in predicting unexpected TFF.Methods: A single-center retrospective cohort analysis was performed on 19539 cycles between 2013 and 2021. Two algorithms, a Least Absolute Shrinkage and Selection Operator (LASSO) and an Extreme Gradient Boosting (Xgboost) were used to create models with cycle characteristics parameters. By including semen parameters or not, the contribution of semen parameters to the performance of the models was evaluated. The area under the curve (AUC), the calibration, and the net reclassification index (NRI) were used to evaluate the performance of the models.Results: The prevalence of TFF were .07 (95%CI:0.07-0.08), and .08 (95%CI:0.07-0.09) respectively in the development and validation group. Including all characteristics, with the models of LASSO and Xgboost, TFF was predicted with the AUCs of .74 (95%CI:0.72-0.77) and .75 (95%CI:0.72-0.77) in the validation group. The AUCs with models of LASSO and Xgboost without semen parameters were .72 (95%CI:0.69-0.74) and .73 (95%CI:0.7-0.75). The models of LASSO and Xgboost with semen parameters only gave the AUCs of .58 (95%CI:0.55-0.61) and .57 (95%CI:0.55-0.6). For the overall validation cohort, the event NRI values were −5.20 for the LASSO model and −.71 for the Xgboost while the non-event NRI values were 10.40 for LASSO model and 0.64 for Xgboost. In the subgroup of poor responders, the prevalence was .21 (95%CI:0.18-0.24). With refitted models of LASSO and Xgboost, the AUCs were .72 (95%CI:0.67-0.77) and .69 (95%CI:0.65-0.74) respectively.Conclusion: In unselected patients, semen parameters contribute to limited value in predicting TFF. However, oocyte yield is an important predictor for TFF and the prevalence of TFF in poor responders was high. Because reasonable predicting power for TFF could be achieved in poor responders, it may warrant further study to prevent TFF in these patients.

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“…Morphological evaluation is routinely used in IVF-ET laboratories. However, morphology alone cannot accurately screen embryos with high implantation potential because of the subjective nature of the screening criteria [ 3 ]. Preimplantation genetic screening (PGS), which uses chromosomal diploid as a criterion, has been developed recently.…”

Section: Introductionmentioning

confidence: 99%

Prediction model for day 3 embryo implantation potential based on metabolites in spent embryo culture medium

Liang

Duan

et al. 2023

BMC Pregnancy Childbirth

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Background Metabolites in spent embryo culture medium correlate with the embryo’s viability. However, there is no widely accepted method using metabolite dada to predict successful implantation. We sought to combine metabolomic profiling of spent embryo culture medium and clinical variables to create an implantation prediction model as an adjunct to morphological screening of day 3 embryos. Methods This investigation was a prospective, nested case-control study. Forty-two day 3 embryos from 34 patients were transferred, and the spent embryo culture medium was collected. Twenty-two embryos implanted successfully, and the others failed. Metabolites in the medium relevant to implantation were detected and measured by Liquid Chromatography-Mass Spectrometry. Clinical signatures relevant to embryo implantation were subjected to univariate analysis to select candidates for a prediction model. Multivariate logistical regression of the clinical and metabolomic candidates was used to construct a prediction model for embryo implantation potential. Results The levels of 13 metabolites were significantly different between the successful and failed groups, among which five were most relevant and interpretable selected by Least Absolute Shrinkage and Selection Operator regression analysis. None of the clinical variables significantly affected day 3 embryo implantation. The most relevant and interpretable set of metabolites was used to construct a prediction model for day 3 embryo implantation potential with an accuracy of 0.88. Conclusions Day 3 embryos’implantation potential could be noninvasively predicted by the spent embryo culture medium’s metabolites measured by LC-MS. This approach may become a useful adjunct to morphological evaluation of day 3 embryos.

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Does conventional morphological evaluation still play a role in predicting blastocyst formation?

Cited by 4 publications

References 42 publications

Noninvasive testing of preimplantation embryos in assisted reproductive technology

Noninvasive testing of preimplantation embryos in assisted reproductive technology

Predicting the unexpected total fertilization failure in conventional in vitro fertilization cycles: What is the role of semen quality?

Prediction model for day 3 embryo implantation potential based on metabolites in spent embryo culture medium

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