Background Abnormal pronuclear formation, including five pronuclei (5PN), is generally considered indicative of abnormal fertilization (e.g., polyspermy or failure of polar body extrusion) and leads to routine embryo discard in many IVF programs. However, emerging evidence suggests that a minority of embryos with atypical pronuclear patterns may develop into a blastocyst and present diploid constitution after genetic testing, challenging the assumption that abnormal pronuclear morphology invariably predicts non-viability. Case report We report a rare case in which a 5PN zygote developed into a morphologically high-quality blastocyst was confirmed euploid-diploid by PGT-A, transferred in a subsequent frozen embryo transfer cycle, and resulted in the live birth of a healthy child. Conclusion This case adds to the limited but growing evidence that embryos with atypical pronuclear presentation may retain reproductive potential when development on time-lapse appears coherent and validated genetic testing confirms diploidy and euploidy, supported by appropriate patient counseling and informed consent.

RESEARCH QUESTION:What are the reproductive outcomes of women who returned to a Brazilian fertility clinic to achieve motherhood after elective oocyte cryopreservation (EOC)? DESIGN:Retrospective single-centre study (n = 2073 women; n = 2431 EOC cycles) between January 2013 and December 2022. Women freezing oocytes for medical indications or infertility treatment were excluded. RESULTS:Mean age at freezing was 36.5 ± 2.8 years. The annual number of EOC cycles increased nearly 500% from 2013 to 2022. Of 1755 women with cryopreserved oocytes, 134 (7.6%) returned to the same clinic for oocyte warming after a mean interval of 4.1 ± 2.2 years; from this cohort, 69% achieved embryo transfer and 47.3% of those achieved a live birth. Older age at freezing was associated with lower odds of reaching embryo transfer (adjusted OR [aOR] 0.86, 95% CI 0.74 to 0.99, P = 0.037), whereas a higher cumulative number of vitrified mature oocytes increased those odds (aOR 1.11, 95% CI 1.03 to 1.20, P = 0.004). The number of oocytes warmed, post-warming survival, fertilization and blastocyst rate were independently associated with embryo transfer; no variable independently predicted live birth. Kaplan-Meier analysis showed cumulative utilization of 4.02% at 3 years and 9.37% at 5 years after freezing. Utilization differed according to cumulative number of vitrified mature oocytes, but not age at freezing. Among women who warmed oocytes, 27% pursued single motherhood. CONCLUSION:As elective oocyte cryopreservation increases, a greater number of women may return for future use. Counselling should emphasize freezing at younger ages, achieving an adequate number of vitrified mature oocytes, and the limitations of assisted reproductive technology at advanced maternal age.

The need to reduce the number of embryos transferred in assisted reproductive care to prevent multiple gestations has led to a stronger emphasis on selecting embryos with the highest morphological quality. Although this evaluation has traditionally been performed by trained embryologists, the increasing use of time-lapse incubators has introduced a greater volume of data and subjectivity in decision-making. Artificial intelligence (AI)-based tools can support embryologists by offering objective, standardized embryo assessments.In Brazil, like other countries, where imported embryo selection technologies may not account for local demographic and ethnic profiles, an AI model — Morphological Artificial Intelligence Assistance (MAIA) — was developed through a collaboration between a university and a private fertility clinic in São Paulo. The model was trained using 1,015 embryo images and prospectively tested in a clinical setting on 200 single embryo transfers. In clinical testing, MAIA achieved an overall accuracy of 66.5%. In elective embryo transfers, where there were more than one embryo eligible for transfer, MAIA achieved 70.1% accuracy for predicting clinical pregnancy. Designed with a user-friendly interface tailored by embryologists, MAIA provides real-time embryo evaluations to support decision-making in routine care.

Abstract The aim of this study was to evaluate the effect of multilamellar vesicles (MLVs) of 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) in co-culture with in vitro-produced bovine embryos (IVPEs). The stability of five concentrations of MLVs (1.0, 1.25, 1.5, 1.75, and 2.0 mM) produced using ultrapure water or embryonic culture medium with 24 or 48 h of incubation at 38.5 °C with 5% CO2 was assessed. In addition, the toxicity of MLVs and their modulation of the lipid profile of the plasma membrane of IVPEs were evaluated after 48 h of co-culture. Both media allowed the production of MLVs. Incubation (24 and 48 h) did not impair the MLV structure but affected the average diameter. The rate of blastocyst production was not reduced, demonstrating the nontoxicity of the MLVs even at 2.0 mmol/L. The lipid profile of the embryos was different depending on the MLV concentration. In comparison with control embryos, embryos cultured with MLVs at 2.0 mmol/L had a higher relative abundance of six lipid ions (m/z 720.6, 754.9, 759.0, 779.1, 781.2, and 797.3). This study sheds light on a new culture system in which the MLV concentration could change the lipid profile of the embryonic cell membrane in a dose-dependent manner.

Despite the use of new techniques on embryo selection and the presence of equipment on the market, such as EmbryoScope® and Geri®, which help in the evaluation of embryo quality, there is still a subjectivity between the embryologist’s classifications, which are subjected to inter- and intra-observer variability, therefore compromising the successful implantation of the embryo. Nonetheless, with the acquisition of images through the time-lapse system, it is possible to perform digital processing of these images, providing a better analysis of the embryo, in addition to enabling the automatic analysis of a large volume of information. An image processing protocol was developed using well-established techniques to segment the image of blastocysts and extract variables of interest. A total of 33 variables were automatically generated by digital image processing, each one representing a different aspect of the embryo and describing a different characteristic of the blastocyst. These variables can be categorized into texture, gray-level average, gray-level standard deviation, modal value, relations, and light level. The automated and directed steps of the proposed processing protocol exclude spurious results, except when image quality (e.g., focus) prevents correct segmentation. The image processing protocol can segment human blastocyst images and automatically extract 33 variables that describe quantitative aspects of the blastocyst’s regions, with potential utility in embryo selection for assisted reproductive technology (ART).

Morphology, morphokinetics, preimplantation genetic screening and lately artificial intelligence (AI) algorithms are used to rank and select embryos with higher potential to achieve a live birth. Training and validating an AI for embryo selection evolves the analysis of their pregnancy prediction potential out of a transferred embryo. In order to truly understand the efficiency of an in development algorithm , we compared senior (>8 years of experience) embryologists (EMBs) and an AI algorithm abilities for pregnancy prediction in a cohort of single-embryos transferred.

The aim of this study is to develop an artificial intelligence (AI) model able to predict the life-birth probability of in-vitro cultured embryos, combining morphology and morphokinetic (MK) information of their development with their oxidative stress level. This retrospective study includes 131 transferred embryos (fresh and frozen-thawed) cultured individually in an Embryoscope (ESD) incubator (Vitrolife, Denmark) until day 5/6. They belong to 100 ICSI cycles performed between May 2017 and December 2018, using autologous or donated eggs. After transfer, the oxidative status of the culture media was assessed by the Thermochemioluminiscence (TCL) AnalyzerTM (Carmel diagnostics, Israel) as an indirect measurement of their metabolic activity. A machine learning algorithm was trained using MK data obtained using the time-lapse monitoring system, the TCL values and the clinical outcome of each embryo, generating a predictive model of life-birth probability. Fertilization was performed following the standard protocol of the clinic. Scoring and selection for transfer/freezing were performed according to the ASEBIR criteria, combining morphological and MK assessment. The oxidative status of 15 μL aliquots of media was measured by the TCL Analyzer, which counts the photons emitted per second (cps) as result of the heat-induced oxidation and modification of the sample. TCL parameters used were H1, H2 and H3 (TCL amplitude at 55, 155 and 255 seconds after heating, respectively), as well as their "sm" variants, resulting from applying a smoothing algorithm to normalize the data. The AI analysis was performed using machine learning algorithms, combining different sets of variables. Data from 105 embryos was used for the training of the machine, and 26 for the simulation. Properly timed development and higher TCL parameters directly related to higher chances of achieving life-birth, as previously published. Five datasets were formed using different combinations of MK and TCL parameters with the stronger statistic correlation with life-birth result: MK+H1+H2+H3, MK+H1sm+H2sm+H3sm, MK+H1sm, MK+H2sm and MK+H3sm. They were compared to a reference model using only MK. The combination with the highest predictive capacity was MK + H2sm. In the training, the model achieved a 93.6% accuracy for predicting positive life-birth (LB+) and 86.2% for negative (LB-), compared to the 97.9% LB+ and 94.8% LB- using solely MK. The blind test of MK + H2sm model had a stronger predictive power (83.3% LB+, 85.7% LB-) than only MK (66.7% LB+, 85.7% LB-). Other combinations of oxidation variables did not show an improved predictive capacity when compared to the MK algorithm. The presented AI algorithm combining MK and TCL parameter H2sm scores a higher predictive power for life-birth than using only MK data. This supports the relevance of the oxidative status of the culture media as an indirect measurement of metabolic activity, higher in embryos that achieve birth. This AI algorithm provides an upgraded score system to assist embryo selection, potentially improving clinical results.

Over the past years, the assisted reproductive technologies (ARTs) have been accompanied by constant innovations. For instance, intracytoplasmic sperm injection (ICSI), time-lapse monitoring of the embryonic morphokinetics, and PGS are innovative techniques that increased the success of the ART. In the same trend, the use of artificial intelligence (AI) techniques is being intensively researched whether in the embryo or spermatozoa selection. Despite several studies already published, the use of AI within assisted reproduction clinics is not yet a reality. This is largely due to the different AI techniques that are being proposed to be used in the daily routine of the clinics, which causes some uncertainty in their use. To shed light on this complex scenario, this review briefly describes some of the most frequently used AI algorithms, their functionalities, and their potential use. Several databases were analyzed in search of articles where applied artificial intelligence algorithms were used on reproductive data. Our focus was on the classification of embryonic cells and semen samples. Of a total of 124 articles analyzed, 32 were selected for this review. From the proposed algorithms, most have achieved a satisfactory precision, demonstrating the potential of a wide range of AI techniques. However, the evaluation of these studies suggests the need for more standardized research to validate the proposed models and their algorithms. Routine use of AI in assisted reproduction clinics is just a matter of time. However, the choice of AI technique to be used is supported by a better understanding of the principles subjacent to each technique, that is, its robustness, pros, and cons. We provide some current (although incipient) and potential uses of AI on the clinic routine, discussing how accurate and friendly it could be. Finally, we propose some standards for AI research on the selection of the embryo to be transferred and other future hints. For us, the imminence of its use is evident, providing a revolutionary milestone that will impact the ART.