This study determined whether morphokinetic variables between aneuploid and euploid embryos differ as a potential aid to select euploid embryos for transfer. Following insemination, EmbryoScope time-lapse images from 98 blastocysts were collected and analysed blinded to ploidy. The morphokinetic variables were retrospectively compared with ploidy, which was determined following trophectoderm biopsy and analysis by array comparative genomic hybridization or single-nucleotide polymorphic array. Multiple aneuploid embryos were delayed at the initiation of compaction (tSC; median 85.1 hours post insemination (hpi); P=0.02) and the time to reach full blastocyst stage (tB; median 110.9hpi, P=0.01) compared with euploid embryos (tSC median 79.7 hpi, tB median 105.9 hpi). Embryos having single or multiple aneuploidy (median 103.4 hpi, P=0.004 and 101.9 hpi, P=0.006, respectively) had delayed initiation of blastulation compared with euploid embryos (median 95.1hpi). No significant differences were observed in first or second cell-cycle length, synchrony of the second or third cell cycles, duration of blastulation, multinucleation at the 2-cell stage and irregular division patterns between euploid and aneuploid embryos. This non-invasive model for ploidy classification may be used to avoid selecting embryos with high risk of aneuploidy while selecting those with reduced risk.

Visual morphology assessment is routinely used for evaluating of embryo quality and selecting human blastocysts for transfer after in vitro fertilization (IVF). However, the assessment produces different results between embryologists and as a result, the success rate of IVF remains low. To overcome uncertainties in embryo quality, multiple embryos are often implanted resulting in undesired multiple pregnancies and complications. Unlike in other imaging fields, human embryology and IVF have not yet leveraged artificial intelligence (AI) for unbiased, automated embryo assessment. We postulated that an AI approach trained on thousands of embryos can reliably predict embryo quality without human intervention. We implemented an AI approach based on deep neural networks (DNNs) to select highest quality embryos using a large collection of human embryo time-lapse images (about 50,000 images) from a high-volume fertility center in the United States. We developed a framework (STORK) based on Google’s Inception model. STORK predicts blastocyst quality with an AUC of >0.98 and generalizes well to images from other clinics outside the US and outperforms individual embryologists. Using clinical data for 2182 embryos, we created a decision tree to integrate embryo quality and patient age to identify scenarios associated with pregnancy likelihood. Our analysis shows that the chance of pregnancy based on individual embryos varies from 13.8% (age ≥41 and poor-quality) to 66.3% (age <37 and good-quality) depending on automated blastocyst quality assessment and patient age. In conclusion, our AI-driven approach provides a reproducible way to assess embryo quality and uncovers new, potentially personalized strategies to select embryos.

Fertility societies worldwide responded to the COVID-19 pandemic by recommending that fertility clinics close, or sharply reduce, the clinical operation, leading to a shift in the management of IVF laboratories in three phases: shutdown preparation; maintenance during shutdown; and restart. Each of these phases carries distinct risks that need identification and mitigation, forcing laboratory managers to rethink and adapt their procedures in response to the pandemic. The sudden and unprecedented nature of the pandemic forced laboratory managers from around the world to base decisions on opinion and experience when evidence-based response options were unavailable. These perspectives on pandemic response were presented during a virtual international symposium on COVID-19, held on 3 April 2020, and organized by the London Laboratory Managers' Group. Laboratory managers from seven different countries at different stages of the pandemic (China, Italy, Spain, France, UK, Brazil and Australia) presented their personal experiences to a select audience of experienced laboratory managers from 19 different countries. The intention of this paper is to collect the learnings and considerations from this group of laboratory managers who collaborated to share personal experiences to contribute to the debate surrounding what constitutes good IVF laboratory practice in extraordinary circumstances, such as the COVID-19 pandemic.

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