Enhancing predictive models for egg donation: time to blastocyst hatching and machine learning insights.

Data sciences and artificial intelligence are becoming encouraging tools in assisted reproduction, favored by time-lapse technology incubators. Our objective is to analyze, compare and identify the most predictive machine learning algorithm developed using a known implantation database of embryos transferred in our egg donation program, including morphokinetic and morphological variables, and recognize the most predictive embryo parameters in order to enhance IVF treatments clinical outcomes. Multicenter retrospective cohort study carried out in 378 egg donor recipients who performed a fresh single embryo transfer during 2021. All treatments were performed by Intracytoplasmic Sperm Injection, using fresh or frozen oocytes. The embryos were cultured in Geri® time-lapse incubators until transfer on day 5. The embryonic morphokinetic events of 378 blastocysts with known implantation and live birth were analyzed. Classical statistical analysis (binary logistic regression) and 10 machine learning algorithms were applied including Multi-Layer Perceptron, Support Vector Machines, k-Nearest Neighbor, Cart and C0.5 Classification Trees, Random Forest (RF), AdaBoost Classification Trees, Stochastic Gradient boost, Bagged CART and eXtrem Gradient Boosting. These algorithms were developed and optimized by maximizing the area under the curve. The Random Forest emerged as the most predictive algorithm for implantation (area under the curve, AUC = 0.725, IC 95% [0.6232-0826]). Overall, implantation and miscarriage rates stood at 56.08% and 18.39%, respectively. Overall live birth rate was 41.26%. Significant disparities were observed regarding time to hatching out of the zona pellucida (p = 0.039). The Random Forest algorithm demonstrated good predictive capabilities for live birth (AUC = 0.689, IC 95% [0.5821-0.7921]), but the AdaBoost classification trees proved to be the most predictive model for live birth (AUC = 0.749, IC 95% [0.6522-0.8452]). Other important variables with substantial predictive weight for implantation and live birth were duration of visible pronuclei (DESAPPN-APPN), synchronization of cleavage patterns (T8-T5), duration of compaction (TM-TiCOM), duration of compaction until first sign of cavitation (TiCAV-TM) and time to early compaction (TiCOM). This study highlights Random Forest and AdaBoost as the most effective machine learning models in our Known Implantation and Live Birth Database from our egg donation program. Notably, time to blastocyst hatching out of the zona pellucida emerged as a highly reliable parameter significantly influencing our implantation machine learning predictive models. Processes involving syngamy, genomic imprinting during embryo cleavage, and embryo compaction are also influential and could be crucial for implantation and live birth outcomes.

Ten J ，Herrero L ，Linares Á ，Álvarez E ，Ortiz JA ，Bernabeu A ，Bernabéu R ... -  《Reproductive Biology and Endocrinology》

Clinical validation of an automatic classification algorithm applied on cleavage stage embryos: analysis for blastulation, euploidy, implantation, and live-birth potential.

Is a commercially available embryo assessment algorithm for early embryo evaluation based on the automatic annotation of morphokinetic timings a useful tool for embryo selection in IVF cycles? The classification provided by the algorithm was shown to be significantly predictive, especially when combined with conventional morphological evaluation, for development to blastocyst, implantation, and live birth, but not for euploidy. The gold standard for embryo selection is still morphological evaluation conducted by embryologists. Since the introduction of time-lapse technology to embryo culture, many algorithms for embryo selection have been developed based on embryo morphokinetics, providing complementary information to morphological evaluation. However, manual annotations of developmental events and application of algorithms can be time-consuming and subjective processes. The introduction of automation to morphokinetic annotations is a promising approach that can potentially reduce subjectivity in the embryo selection process and improve the workflow in IVF laboratories. This observational, retrospective cohort study was performed in a single IVF clinic between 2018 and 2021 and included 3736 embryos from oocyte donation cycles (423 cycles) and 1291 embryos from autologous cycles with preimplantation genetic testing for aneuploidies (PGT-A, 185 cycles). Embryos were classified on Day 3 with a score from 1 (best) to 5 (worst) by the automatic embryo assessment algorithm. The performance of the embryo classification model for blastocyst development, implantation, live birth, and euploidy prediction was assessed. All embryos were monitored by a time-lapse system with an automatic cell-tracking and embryo assessment software during culture. The embryo assessment algorithm was applied on Day 3, resulting in embryo classification from 1 to 5 (from highest to lowest developmental potential) depending on four parameters: P2 (t3-t2), P3 (t4-t3), oocyte age, and number of cells. There were 959 embryos selected for transfer on Day 5 or 6 based on conventional morphological evaluation. The blastocyst development, implantation, live birth, and euploidy rates (for embryos subjected to PGT-A) were compared between the different scores. The correlation of the algorithm scoring with the occurrence of those outcomes was quantified by generalized estimating equations (GEEs). Finally, the performance of the GEE model using the embryo assessment algorithm as the predictor was compared to that using conventional morphological evaluation, as well as to a model using a combination of both classification systems. The blastocyst rate was higher with lower the scores generated by the embryo assessment algorithm. A GEE model confirmed the positive association between lower embryo score and higher odds of blastulation (odds ratio (OR) (1 vs 5 score) = 15.849; P < 0.001). This association was consistent in both oocyte donation and autologous embryos subjected to PGT-A. The automatic embryo classification results were also statistically associated with implantation and live birth. The OR of Score 1 vs 5 was 2.920 (95% CI 1.440-5.925; P = 0.003; E = 2.81) for implantation and 3.317 (95% CI 1.615-6.814; P = 0.001; E = 3.04) for live birth. However, this association was not found in embryos subjected to PGT-A. The highest performance was achieved when combining the automatic embryo scoring and traditional morphological classification (AUC for implantation potential = 0.629; AUC for live-birth potential = 0.636). Again, no association was found between the embryo classification and euploidy status in embryos subjected to PGT-A (OR (1 vs 5) = 0.755 (95% CI 0.255-0.981); P = 0.489; E = 1.57). The retrospective nature of this study may be a reason for caution, although the large sample size reinforced the ability of the model for embryo selection. Time-lapse technology with automated embryo assessment can be used together with conventional morphological evaluation to increase the accuracy of embryo selection process and improve the success rates of assisted reproduction cycles. To our knowledge, this is the largest embryo dataset analysed with this embryo assessment algorithm. This research was supported by Agencia Valenciana de Innovació and European Social Fund (ACIF/2019/264 and CIBEFP/2021/13). In the last 5 years, M.M. received speaker fees from Vitrolife, Merck, Ferring, Gideon Richter, Angelini, and Theramex, and B.A.-R. received speaker fees from Merck. The remaining authors have no competing interests to declare. N/A.

Valera MA ，Aparicio-Ruiz B ，Pérez-Albalá S ，Romany L ，Remohí J ，Meseguer M ... -  《-》

Correlation between aneuploidy, standard morphology evaluation and morphokinetic development in 1730 biopsied blastocysts: a consecutive case series study.

Are there correlations among human blastocyst ploidy status, standard morphology evaluation and time-lapse kinetics? Correlations were observed, in that euploid human blastocysts showed a higher percentage with top quality inner cell mass (ICM) and trophectoderm (TE), higher expansion grades and shorter time to start of blastulation, expansion and hatching, compared to aneuploid ones. Embryo quality has always been considered an important predictor of successful implantation and pregnancy. Nevertheless, knowledge of the relative impact of each morphological parameter at the blastocyst stage needs to be increased. Recently, with the introduction of time-lapse technology, morphokinetic parameters can also be evaluated. However, a large number of studies has reported conflicting outcomes. This was a consecutive case series study. The morphology of 1730 blastocysts obtained in 530 PGS cycles performed from September 2012 to April 2014 that underwent TE biopsy and array comparative genomic hybridization was analyzed retrospectively. A total of 928 blastocysts were cultured in a time-lapse incubator allowing morphokinetic parameters to be analyzed. Mean female age was 36.8 ± 4.24 years. Four hunderd fifty-four couples were enrolled in the study: 384, 64 and 6 of them performed single, double or triple PGS cycles, respectively. In standard morphology evaluation, the expansion grade, and quality of the ICM and TE were analyzed. The morphokinetic parameters observed were second polar body extrusion, appearance of two pronuclei, pronuclear fading, onset of two- to eight-cell divisions, time between the two- and three-cell (cc2) and three- and four-cell (s2) stages, morulae formation time, starting blastulation, full blastocyst stage, expansion and hatching timing. Of the 1730 biopsied blastocysts, 603 were euploid and 1127 aneuploid. We observed that 47.2% of euploid and 32.8% of aneuploid blastocysts showed top quality ICM (P < 0.001), and 17.1% of euploid and 28.5% of aneuploid blastocysts showed poor quality ICM (P < 0.001). Top quality TE was present in 46.5% of euploid and 31.1% of aneuploid blastocysts (P < 0.001), while 26.6% of euploid and 38.1% of aneuploid blastocysts showed poor quality TE (P < 0.001). Regarding expansion grade, 81.1% of euploid and 72.4% of aneuploid blastocysts were fully expanded (Grade 5-6; P < 0.001). The timing of cleavage from the three- to four-cell stage, of reaching four-cell stage, of starting blastulation, reaching full blastocyst stage, blastocyst expansion and hatching were 2.6 (95% confidence interval (CI): 1.7-3.5), 40.0 (95% CI: 39.3-40.6), 103.4 (95% CI: 102.2-104.6), 110.2 (95% CI: 108.8-111.5), 118.7 (95% CI: 117.0-120.5) and 133.2 (95% CI: 131.2-135.2) hours in euploid blastocysts, and 4.2 (95% CI: 3.6-4.8), 41.1 (95% CI: 40.6-41.6), 105.0 (95% CI: 104.0-106.0), 112.8 (95% CI: 111.7-113.9), 122.1 (95% CI: 120.7-123.4) and 137.4 (95% CI: 135.7-139.1) hours in aneuploid blastocysts (P < 0.05 for early and P < 0.0001 for later stages of development), respectively. No statistically significant differences were found between euploid and aneuploid blastocysts for the remaining morphokinetic parameters.A total of 407 embryo transfers were performed (155 fresh, 252 frozen-thawed blastocysts). Higher clinical pregnancy, implantation and live birth rates were obtained in frozen-thawed compared to fresh embryo transfers (P = 0.0104, 0.0091 and 0.0148, respectively). The miscarriage rate was 16.1% and 19.6% in cryopreserved and fresh embryo transfer, respectively. The mean female age was lower in the euploid compared to aneuploid groups (35.0 ± 3.78 versus 36.7 ± 4.13 years, respectively), We found an increasing probability for aneuploidy with female age of 10% per year (odds ratio (OR) = 1.1, 95% CI: 1.1-1.2, P < 0.001). The main limitation of morphology assessment is that it is a static system and can be operator-dependent. In this study, eight embryologists performed morphology assessments. The main limitation of the time-lapse technology is that it is impossible to rotate the embryos making it very difficult to observe them in case of blastomere overlapping or increased cytoplasmic fragmentation. Although there seems to be a relationship between the ploidy status and blastocyst morphology/development dynamics, the evaluation of morphological and morphokinetic parameters cannot currently be improved upon, and therefore replace, PGS. Our results on ongoing pregnancy and miscarriage rates suggest that embryo evaluation by PGS or time-lapse imaging may not improve IVF outcome. However, time-lapse monitoring could be used in conjunction with PGS to choose, within a cohort, the blastocysts to analyze or, when more than one euploid blastocyst is available, to select which one should be transferred. No specific funding was obtained for this study. None of the authors have any competing interests to declare.

Minasi MG ，Colasante A ，Riccio T ，Ruberti A ，Casciani V ，Scarselli F ，Spinella F ，Fiorentino F ，Varricchio MT ，Greco E ... -  《-》

Development of a generally applicable morphokinetic algorithm capable of predicting the implantation potential of embryos transferred on Day 3.

Can a generally applicable morphokinetic algorithm suitable for Day 3 transfers of time-lapse monitored embryos originating from different culture conditions and fertilization methods be developed for the purpose of supporting the embryologist's decision on which embryo to transfer back to the patient in assisted reproduction? The algorithm presented here can be used independently of culture conditions and fertilization method and provides predictive power not surpassed by other published algorithms for ranking embryos according to their blastocyst formation potential. Generally applicable algorithms have so far been developed only for predicting blastocyst formation. A number of clinics have reported validated implantation prediction algorithms, which have been developed based on clinic-specific culture conditions and clinical environment. However, a generally applicable embryo evaluation algorithm based on actual implantation outcome has not yet been reported. Retrospective evaluation of data extracted from a database of known implantation data (KID) originating from 3275 embryos transferred on Day 3 conducted in 24 clinics between 2009 and 2014. The data represented different culture conditions (reduced and ambient oxygen with various culture medium strategies) and fertilization methods (IVF, ICSI). The capability to predict blastocyst formation was evaluated on an independent set of morphokinetic data from 11 218 embryos which had been cultured to Day 5. PARTICIPANTS/MATERIALS, SETTING, The algorithm was developed by applying automated recursive partitioning to a large number of annotation types and derived equations, progressing to a five-fold cross-validation test of the complete data set and a validation test of different incubation conditions and fertilization methods. The results were expressed as receiver operating characteristics curves using the area under the curve (AUC) to establish the predictive strength of the algorithm. By applying the here developed algorithm (KIDScore), which was based on six annotations (the number of pronuclei equals 2 at the 1-cell stage, time from insemination to pronuclei fading at the 1-cell stage, time from insemination to the 2-cell stage, time from insemination to the 3-cell stage, time from insemination to the 5-cell stage and time from insemination to the 8-cell stage) and ranking the embryos in five groups, the implantation potential of the embryos was predicted with an AUC of 0.650. On Day 3 the KIDScore algorithm was capable of predicting blastocyst development with an AUC of 0.745 and blastocyst quality with an AUC of 0.679. In a comparison of blastocyst prediction including six other published algorithms and KIDScore, only KIDScore and one more algorithm surpassed an algorithm constructed on conventional Alpha/ESHRE consensus timings in terms of predictive power. Some morphological assessments were not available and consequently three of the algorithms in the comparison were not used in full and may therefore have been put at a disadvantage. Algorithms based on implantation data from Day 3 embryo transfers require adjustments to be capable of predicting the implantation potential of Day 5 embryo transfers. The current study is restricted by its retrospective nature and absence of live birth information. Prospective Randomized Controlled Trials should be used in future studies to establish the value of time-lapse technology and morphokinetic evaluation. Algorithms applicable to different culture conditions can be developed if based on large data sets of heterogeneous origin. This study was funded by Vitrolife A/S, Denmark and Vitrolife AB, Sweden. B.M.P.'s company BMP Analytics is performing consultancy for Vitrolife A/S. M.B. is employed at Vitrolife A/S. M.M.'s company ilabcomm GmbH received honorarium for consultancy from Vitrolife AB. D.K.G. received research support from Vitrolife AB.

Petersen BM ，Boel M ，Montag M ，Gardner DK ... -  《-》

'Two countries-two labs': the transnational gamete donation (TGD) programme to support egg donation.

Coccia ME ，Rizzello F ，Wakunga S ，Badolato L ，Evangelisti P ，Bertocci F ，Giachini C ，Criscuoli L ，Micelli E ，Picone R ... -  《-》