Mathematical methodology to obtain and compare different embryo scores

Novel image analyser-assisted morphometric methodology offer unique opportunity for selection of embryos with potential for implantation

BACKGROUND

Previous studies looked into the connections between pregnancy and the Zona Pellucida (ZP) thickness and Zona Pellucida Thickness Variation (ZPTV), as well as the embryo's radius, circumference, perimeter and global symmetry. However, no research has linked embryo implantation and pregnancy to the percentage of ZP thinning, the reduction in ooplasm volume, and the increase in perivitelline space (PVS) volume. Our objective is to correlate the percentage of ZP thinning, the percentage of ooplasm volume shrinkage and the percentage of PVS increase to the implantation. These data will be used for embryo selection as well as it can be put into a software that will assist embryo selection.

MATERIALS AND METHODS

Retrospective study included 281 patients, all of them had 2 embryos transferred, 149 patients got pregnant with two gestation sacs and 132 patients did not get pregnant. All of the transferred embryos had the ZP thickness measured several times from time of ICSI till Embryo Transfer (ET), the ooplasm volume was calculated from time of ICSI till two Pronuclei (2PN) fading and the PVS was calculated from the ICSI time till the 2PN fading.

RESULTS

The first characteristic is the change in the average ZP thickness that decreased by 32.7% + 5.3% at 70 h for the implanted embryos (Group 1) versus 23.6% + 4.8% for non-implanted embryos (Group 2) p = 0.000. The second characteristic is the average reduction in the volume of the ooplasm which is 20.5% + 4.3% in Group 1 versus 15.1% + 5.2% in Group 2, p = 0.000. The third characteristic is the increase in the volume of the PVS which was 38.1% + 7.6% in Group 1 versus 31.6% + 9.7% in Group 2 p = 0.000.

CONCLUSION

The implanted embryos showed higher percent of ZP thinning, higher percent of ooplasm reduction and higher percent of PVS increase.

Modeling three sources of uncertainty in assisted reproductive technologies with probabilistic graphical models

Embryo selection is a critical step in assisted reproduction: good selection criteria are expected to increase the probability of inducing a pregnancy. Machine learning techniques have been applied for implantation prediction or embryo quality assessment, which embryologists can use to make a decision about embryo selection. However, this is a highly uncertain real-world problem, and current proposals do not model always all the sources of uncertainty. We present a novel probabilistic graphical model that accounts for three different sources of uncertainty, the standard embryo and cycle viability, and a third one that represents any unknown factor that can drive a treatment to a failure in otherwise perfect conditions. We derive a parametric learning method based on the Expectation-Maximization strategy, which accounts for uncertainty issues. We empirically analyze the model within a real database consisting of 604 cycles (3125 embryos) carried out at Hospital Donostia (Spain). Embryologists followed the protocol of the Spanish Association for Reproduction Biology Studies (ASEBIR), based on morphological features, for embryo selection. Our model predictions are correlated with the ASEBIR protocol, which validates our model. The benefits of accounting for the different sources of uncertainty and the importance of the cycle characteristics are shown. Considering only transferred embryos, our model does not further discriminate them as implanted or failed, suggesting that the ASEBIR protocol could be understood as a thorough summary of the available morphological features.

Multivariate analysis of the factors associated with live births during in vitro fertilisation in Southeast Asia: a cross-sectional study of 104,015 in vitro fertilisation records in Taiwan

PURPOSE

To investigate the factors associated with live births and the interaction between age and the number of embryos transferred after in vitro fertilisation (IVF) treatment.

METHODS

This study analyses data from a population-based-assisted reproductive database of all registered artificial reproduction institutions (n = 80) from 2010 to 2016 in Taiwan. The probability of a live birth in correlation with the treatment parameters was measured with multivariate logistic regression analyses using the generalised additive model (GAM) and Pearson's chi-square exact test.

RESULTS

A total of 104,015 IVF treatments performed between 2010 and 2016 were included in our analysis. From these treatments, 31,467 (30.3%) were successfully delivered, and 40,565 test-tube babies were born. Pearson's chi-square exact test indicated that parents' age, cause of infertility, type of ovarian stimulation, additional assisted reproductive technology techniques, donated egg or sperm, fresh or frozen embryo, presence or absence of ovarian hyperstimulation syndrome, and day of embryo transfer were significantly associated with live births after an IVF cycle (p < 0.05). Multiple logistic regression analysis with the GAM revealed that the odds of a live birth with IVF treatment in patients < 34 years of age were 2.55 times higher than that in patients ≥ 45 years of age (odds ratio = 2.55, 95% confidence interval = 1.69-2.90) for patients who underwent a single-embryo transfer (SET); a similar pattern was observed when two or more embryos were transferred. Egg donation, the assisted hatching technique, oral ovarian stimulation agents, and implantation of frozen embryos during SET were shown to improve the chance of a live birth by 29-90%. Implantation of the embryo after the 5th day of culture yielded the highest odds of a live birth. The interaction plot revealed that maternal age, especially < 40 years, was associated with the probability of a live birth. SET and double-embryo transfer showed similar associations with the probability of a live birth across age groups. Transferring more than two embryos might reduce the probability of a live birth during IVF treatment for women ≥ 40 years of age.

CONCLUSIONS

Implanting a greater number of embryos did not improve the age-related decrease in fertility for patients undergoing IVF. Therefore, we suggested that ≤ 2 blastocysts could be transferred during IVF treatments for women ≥ 40 years. Transferring a blastocyst on day 5 of culture was associated with a significant increase in the odds of a live birth resulting from IVF.

Individualized embryo selection strategy developed by stacking machine learning model for better in vitro fertilization outcomes: an application study

BACKGROUND

To minimize the rate of in vitro fertilization (IVF)- associated multiple-embryo gestation, significant efforts have been made. Previous studies related to machine learning in IVF mainly focused on selecting the top-quality embryos to improve outcomes, however, in patients with sub-optimal prognosis or with medium- or inferior-quality embryos, the selection between SET and DET could be perplexing.

METHODS

This was an application study including 9211 patients with 10,076 embryos treated during 2016 to 2018, in Tongji Hospital, Wuhan, China. A hierarchical model was established using the machine learning system XGBoost, to learn embryo implantation potential and the impact of double embryos transfer (DET) simultaneously. The performance of the model was evaluated with the AUC of the ROC curve. Multiple regression analyses were also conducted on the 19 selected features to demonstrate the differences between feature importance for prediction and statistical relationship with outcomes.

RESULTS

For a single embryo transfer (SET) pregnancy, the following variables remained significant: age, attempts at IVF, estradiol level on hCG day, and endometrial thickness. For DET pregnancy, age, attempts at IVF, endometrial thickness, and the newly added P1 + P2 remained significant. For DET twin risk, age, attempts at IVF, 2PN/ MII, and P1 × P2 remained significant. The algorithm was repeated 30 times, and averaged AUC of 0.7945, 0.8385, and 0.7229 were achieved for SET pregnancy, DET pregnancy, and DET twin risk, respectively. The trend of predictive and observed rates both in pregnancy and twin risk was basically identical. XGBoost outperformed the other two algorithms: logistic regression and classification and regression tree.

CONCLUSION

Artificial intelligence based on determinant-weighting analysis could offer an individualized embryo selection strategy for any given patient, and predict clinical pregnancy rate and twin risk, therefore optimizing clinical outcomes.

Effect of GnRH agonist before IVF on outcomes in infertile endometriosis patients: a randomized controlled trial

RESEARCH QUESTION

Does 3-months of gonadotrophin releasing hormone agonist (GnRHa) treatment before IVF improve clinical pregnancy rate in infertile patients with endometriosis?

DESIGN

Single-blind, placebo-controlled clinical trial of 200 infertile women with endometriosis assigned to use GnRHa (study group) or placebo (control group) for 3 months before IVF. Clinical, embryological outcomes and stimulation parameters were analysed. Clinical pregnancy rate was the primary endpoint. In a subgroup of 40 patients, follicular fluid levels of oestradiol, testosterone and androstendione were measured. Gene expression profile of CYP19A1 was analysed in cumulus and mural granulosa cells.

RESULTS

Implantation or clinical pregnancy rate were not significantly different between the two groups. Clinical pregnancy rates were 25.3% and 33.7% in the study and control groups, respectively (P = 0.212). Cumulative live birth rate was not significantly different: 22.0% (95% CI 13.0 to 31.0) in the study group and 33.7% (95% CI 24.0 to 44.0) in the control group (P = 0.077). Ovarian stimulation was significantly longer and total dose of gonadotrophins significantly higher in the study group (both P < 0.001). Serum oestradiol levels on the day of HCG were significantly lower in the study group (P = 0.001). Cancellation rate was significantly higher in the study group (P = 0.042), whereas cleavage embryos were significantly more numerous in the control group (P = 0.023). No significant differences in the expression of CYP19A1 gene in mural or cumulus granulosa cells or steroid levels in follicular fluid between the two groups were observed, but testosterone was significantly lower in the study group (P < 0.001).

CONCLUSION

Three-months of GnRHa treatment before IVF does not improve clinical pregnancy rate in women with endometriosis.

Fitting the data from embryo implantation prediction: Learning from label proportions

Machine learning techniques have been previously used to assist clinicians to select embryos for human-assisted reproduction. This work aims to show how an appropriate modeling of the problem can contribute to improve machine learning techniques for embryo selection. In this study, a dataset of 330 consecutive cycles (and associated embryos) carried out by the Unit of Assisted Reproduction of the Hospital Donostia (Spain) throughout 18 months has been analyzed. The problem of the embryo selection has been modeled by a novel weakly supervised paradigm, learning from label proportions, which considers all the available data, including embryos whose fate cannot be certainly established. Furthermore, all the collected features, describing cycles and embryos, have been considered in a multi-variate data analysis. Our integral solution has been successfully tested. Experimental results show that the proposed technique consistently outperforms an equivalent approach based on standard supervised classification. Embryos in this study were selected for transference according to the criteria of the Spanish Association for Reproduction Biology Studies. Obtained classification models outperform these criteria, specifically reordering medium-quality embryos.

Osmotic-shock produced by vitrification solutions improves immature human oocytes in vitro maturation

BACKGROUND

During cytoplasmic oocyte maturation, Ca(2+) currents are vital for regulating a broad range of physiological processes. Recent studies have demonstrated that DMSO and EG cause large transient increases in intracellular Ca(2+) in mouse oocytes. The CP used in vitrifying protocols also increases the intracellular calcium transient. The aim of this study is to evaluate the effects of vitrifying time (before and after IVM) and exposure to the vitrification solutions and ionomycin on oocyte quality and embryonic development.

METHODS

221 GV-oocytes unsuitable for IVF-ICSI cycles were randomly distributed into one of the following three groups. G1 (control group): 41 GV-oocytes IVM until MII; G2: 43 oocytes vitrified at GV stage and IVM until MII stage; and G3: 53 GV-oocytes IVM until MII and then vitrified. In order to clarify the effect of vitrification solutions (VS) on human oocyte IVM through the intracellular Ca(2+) oscillation, the following two groups were also included. G4: 43 GV-oocytes exposed to VS and IVM until MII; and G5: 41 GV-oocytes exposed to ionomycin and IVM until MII. All GV-oocytes that reached MII-stage were parthenogenetically activated to assess oocyte viability. IVM was performed in IVF-medium (24-48 h). Chemical treatment (ionomycin) and osmotic treatment (vitrification solutions) were performed without liquid-N2 immersion. The following rates were evaluated: survival (SR), in-vitro maturation (IVMR), activation (AR), development to 2-cell (DRC), development to morula (DRCM) and development to blastocyst (DRB). Ratios between the different treatment groups were compared using contingency tables analysis (chi-square test).

RESULTS

A high survival rate was obtained in G2 (95.5 %) and G4 (96.6 %). In-vitro maturation rate was significantly higher for G4 (86 %) and G2 (83.7 %) compared to G1 (63.4 %), G3 (56.6 %) and G5 (48.8 %). DRCM was significantly higher for G1 and G2 compared to G3 (G1: 15.8 %, G2: 20.7 % and G3: 0 %). DRB was only obtained for the oocytes vitrified before IVM (G2: 3.4 %). AR was also significantly higher for G2 and G4 compared to G5 (G2: 80.5 %, G4: 86.5 % and G5: 55 %). DRCM and DRB were only obtained in G2 and G4. DRCM was significantly higher for oocytes vitrified at GV stage (G2) and for oocytes exposed to the VS in G4 compared to the oocytes exposed to the ionomycin in G5 (G2: 20.7 %; G4: 37.5 % and G5: 0 %).

CONCLUSIONS

Vitrifying GV-oocytes improves their IVM. Further investigation could look to increase the oocyte pool and improve fertility preservation options.

A minimally invasive methodology based on morphometric parameters for day 2 embryo quality assessment

The risk of multiple pregnancy to maternal-fetal health can be minimized by reducing the number of embryos transferred. New tools for selecting embryos with the highest implantation potential should be developed. The aim of this study was to evaluate the ability of morphological and morphometric variables to predict implantation by analysing images of embryos. This was a retrospective study of 135 embryo photographs from 112 IVF-ICSI cycles carried out between January and March 2011. The embryos were photographed immediately before transfer using Cronus 3 software. Their images were analysed using the public program ImageJ. Significant effects (P < 0.05), and higher discriminant power to predict implantation were observed for the morphometric embryo variables compared with morphological ones. The features for successfully implanted embryos were as follows: four cells on day 2 of development; all blastomeres with circular shape (roundness factor greater than 0.9), an average zona pellucida thickness of 13 µm and an average of 17695.1 µm² for the embryo area. Embryo size, which is described by its area and the average roundness factor for each cell, provides two objective variables to consider when predicting implantation. This approach should be further investigated for its potential ability to improve embryo scoring.