Classical preimplantation embryo culture is performed in static fluid environments. Whether a dynamic fluid environment, like the fallopian tube, is beneficial for embryo development remains to be determined across mammalian species. Objectives of these proof-of-concept studies were to determine if controllable dynamic microfluidic culture would enhance preimplantation murine, bovine, and human embryo development compared to static culture. This prospective randomized controlled trial tested static versus controlled dynamic culture of preimplantation mouse (n = 397), bovine (n = 242), and human (n = 512) zygotes to blastocyst stages with outcome measures of embryo cleavage, cellular fragmentation, apoptosis, and blastocyst conversion rates. Dynamic culture of mouse and bovine zygotes with microfluidics significantly improved embryo development. Mouse placental imprinted gene expression was significantly different between embryos derived in vivo, by static culture, and by dynamic culture. Using human sibling zygotes, this dynamic microfluidic culture system increased the number of blastomeres per cleavage-stage embryo, reduced cellular fragmentation or apoptosis, improved blastocyst conversion rates, and enhanced blastocyst developmental stages. In conclusion, species-specific longitudinal studies demonstrated that dynamic microfluidic culture significantly improved embryo development, independent of culture media composition, temperature, and gaseous environment. These cellular indicators represent improved embryo development that can translate into higher pregnancy rates in transgenics, domestic livestock and endangered species and treating human infertility.

Study question: Is it possible to estimate blastocyst ploidy with an artificial intelligence (AI) algorithm built using embryo morphokinetics data? Summary answer: AI was able to estimate blastocysts status in euploid or aneuploid with an accuracy of 0.99 (training) and 0.71 and 0.70 AUC (blind-test), respectively. What is known already: Morphokinetic parameters are well associated with implantation rates, thus ensuring time-lapse as a useful tool to enhance embryo ranking and selection. However, regarding embryo ploidy status, clinics still rely exclusively on molecular/genetic testing. Our previous studies has indicated that euploid blastocysts are faster at time of pronucleous fading (tPNf) and time to blastulation (tB). Considering the amount of data derived from the morphokinetic annotations and the promising results that AI approach is bringing to the field, this study aims to verify AI accuracy to distinguish euploid and aneuploidy blastocysts using 17 morphokinetics parameters and prospectively compared to embryo biopsy results. Study design, size, duration: This is a prospective cohort study including 402 embryos (cultured in a time-lapse incubator, EmbryoscopePlus, Vitrolife) from 140 patients undergoing IVF treatment with preimplantation genetic testing for aneuploidy (NGS platform) after inform consent form signature between July 2019 and September 2021. Morphokinetics annotations were analyzed by AI in an association of the technique of artificial neural networks (ANN) and genetic algorithms (GA) for ploidy assessment. Participants/materials, setting, methods: Morphokinetic parameters were manually annotated up to full-expanded blastocyst time. Time intervals and time ratios were calculated, resulting in 17 variables for the IA analysis. Of the 402 embryos data, 252 were randomly divided into training, validating and testing (70%, 15% and 15%, respectively) of the ANN. The remaining 150 data were used for the blind test. The area under the curve (AUC) of the receiver operating characteristic curve was measured to obtain predictive power. Main results and the role of chance: From 402 blastocysts biopsied, 185 were euploid and 217 aneuploid. The AI algorithm was trained with 176 embryos (AUC for both euploid and aneuploid= 0.99), tested with 38 embryos (AUC for euploid=0.62 and aneuploid=0.61), validated with 38 embryos (AUC for euploid=0.82 and aneuploid=0.83). For the blind-test 150 embryos were used (AUC for euploid=0.70 and aneuploid=0.71). Blind-test database was checked only after AI algorithm was tested. Limitations, reasons for caution: The development of this AI algorithm was built from a single IVF center database. The absence of an extent dataset for the blind-test does not allow us to transpose this algorithm for clinical use at this moment. Wider implications of the findings: The use of artificial intelligence for embryo assessment is a promising tool in IVF laboratories. In our model, using only morphokinetics, a considerable predictive power to evaluate euploid (0.71 AUC) and aneuploid (0.70 AUC) embryos was achieved, indicating a potential use as a non-invasive approach for embryo ranking and selection. Trial registration number: Not applicable

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.