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What is the pregnancy and neonatal outcomes of an interpretable artificial intelligence (AI) model for embryo selection in a prospective clinical trial? This single-centre prospective cohort study was carried out from October 2021 to March 2022. A total of 330 eligible patients were assigned to their preferred groups, with 250 patients undergoing a fresh single-blastocyst transfer cycle after the exclusion criteria had been applied. For the AI-assisted group (AAG), embryologists selected the embryos for transfer based on the ranking recommendations provided by an interpretable AI system, while with the manual group, embryologists used the Gardner grading system to make their decisions. The implantation rate was significantly higher in the AAG than the manual group (80.87% versus 68.15%, P = 0.022). No significant difference was found in terms of monozygotic twin rate, miscarriage rate, live birth rate and ectopic pregnancy rate between the groups. Furthermore, there was no significant difference in terms of neonatal outcomes, including gestational weeks, premature birth rate, birth height, birthweight, sex ratio at birth and newborn malformation rate. The consensus rate between the AI and retrospective analysis by the embryologists was significantly higher for good-quality embryos (i.e. grade 4BB or higher) versus poor-quality embryos (i.e. less than 4BB) (84.71% versus 25%, P < 0.001). These prospective trial results suggest that the proposed AI system could effectively help embryologists to improve the implantation rate with single-blastocyst transfer compared with traditional manual evaluation methods. [ABSTRACT FROM AUTHOR]

What is the efficiency and efficacy of the novel Biorocks semi-automated vitrification system, which is based on a hydrogel? This comparative experimental laboratory study used mouse model and human day 6 blastocysts. Mouse oocytes and embryos were quality assessed post-vitrification. The Biorocks system successfully automated the solution exchanges during the vitrification process, achieving a significantly improved throughput of up to 36 embryos/oocytes per hour. Using hydrogel for cryoprotective agent delivery, 12 vessels could be processed simultaneously, fitting comfortably within an assisted reproductive technology (ART) workstation. In tests involving the cryopreservation of oocytes and embryos, the system yielded outcomes equivalent to the manual Cryotop method. For example, the survival rate for mouse oocytes was 98% with the Biorocks vitrification system (n = 46) and 95% for the manual Cryotop method (n = 39), of which 46% and 41%, respectively, progressed to blastocysts on day 5 after IVF. CC-grade day 6 human blastocysts processed with the Biorocks system (n = 39) were associated with a 92% 2 h re-expansion rate, equivalent to the 90% with Cryotop (n = 30). The cooling/warming rates achieved by the Biorocks system were 31,900°C/minute and 24,700°C/minute, respectively. Oocyte quality was comparable or better post-vitrification for Biorocks than Cryotop. The Biorocks semi-automated vitrification system offers enhanced throughput without compromising the survival and developmental potential of oocytes and embryos. This innovative system may help to increase the efficiency and standardization of vitrification in ART clinics. Further investigations are needed to confirm its efficacy in a broader clinical context. [ABSTRACT FROM AUTHOR]

Can a non-invasive embryo transfer strategy provide a reference for embryo selection to be established? Chromosome sequencing of 345 paired blastocyst culture medium and whole blastocyst samples was carried out and a non-invasive embryo grading system was developed based on the random forest machine learning algorithm to predict blastocyst ploidy. The system was validated in 266 patients, and a blinded prospective observational study was conducted to investigate clinical outcomes between machine learning-guided and traditional non-invasive preimplantation genetic testing for aneuploidy (niPGT-A) analyses. Embryos were graded as A, B or C according to their euploidy probability levels predicted by non-invasive chromosomal screening (NICS). Higher live birth rate was observed in A- versus C-grade embryos (50.4% versus 27.1%, P = 0.006) and B- versus C-grade embryos (45.3% versus 27.1%, P = 0.022) and lower miscarriage rate in A- versus C-grade embryos (15.9% versus 33.3%, P = 0.026) and B- versus C-grade embryos (14.3% versus 33.3%, P = 0.021). The embryo utilization rate was significantly higher through the machine learning strategy than the conventional dichotomic judgment of euploidy or aneuploidy in the niPGT-A analysis (78.8% versus 57.9%, P < 0.001). Better outcomes were observed in A- and B-grade embryos versus C-grade embryos and higher embryo utilization rates through the machine learning strategy compared with traditional niPGT-A analysis. A machine learning guided embryo grading system can be used to optimize embryo selection and avoid wastage of potential embryos. [ABSTRACT FROM AUTHOR]