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.

Transit through the male genital tract did not enhance the ability of ejaculated spermatozoa to achieve fertilization with intracytoplasmic sperm injection compared to that of testicular spermatozoa in men with severely impaired production. In ejaculated samples a lower number of spermatozoa available resulted in an impaired chance of achieving pregnancy. Using testicular spermatozoa may be a reasonable alternative for couples in whom multiple attempts at intracytoplasmic sperm injection have failed using ejaculated sperm from men with cryptozoospermia.

Morphology assessment has become the standard method for evaluation of embryo quality and selecting human blastocysts for transfer in in vitro fertilization (IVF). This process is highly subjective for some embryos and thus prone to human bias. As a result, morphological assessment results may vary extensively between embryologists and in some cases may fail to accurately predict embryo implantation and live birth potential. Here we postulated that an artificial intelligence (AI) approach trained on thousands of embryos can reliably predict embryo quality without human intervention.To test this hypothesis, we implemented an AI approach based on deep neural networks (DNNs). Our approach called STORK accurately predicts the morphological quality of blastocysts based on raw digital images of embryos with 98% accuracy. These results indicate that a DNN can automatically and accurately grade embryos based on raw images.Using clinical data for 2,182 embryos, we then created a decision tree that integrates 2 clinical parameters such as embryo quality and patient age to identify scenarios associated with increased or decreased pregnancy chance. This IVF data-driven analysis shows that the chance of pregnancy varies from 13.8% to 66.3%.In conclusion, our AI-driven approach provides a novel way to assess embryo quality and uncovers new, potentially personalized strategies to select embryos with an improved likelihood of pregnancy outcome.

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OBJECTIVE: The objective of this study is to identify the times of embryo cell division (up to 8-cell) by applying an artificial intelligence-based (AI) approach using time-lapse microscopy (TLM) images. Our hypothesis is that the unbiased AI approach would identify embryo division times without relying on human intervention.DESIGN: We built a stand-alone framework with a convolutional neural network (CNN) as the core to predict cell division times for mouse and human embryos, respectively, based on raw time-lapse digital images.MATERIALS AND METHODS: This study used 31,120 images of 100 mouse embryos from a public dataset. We also used 661,060 images of 11,898 human embryos cultured in the TLM system (EmbryoScope, Vitrolife, Sweden) at various cell stages post-insemination/ICSI. Images were separated randomly into training, validation, and test sets with a ratio of 70:15:15 for mouse and 80:10:10 for human. Separation was done on an embryo basis, Data are presented as n (%), risk ratio (RR) and 95% confidence interval (CI) *Adjusted for age at retrieval, number of oocytes retrieved during fresh cycle, BMI, embryo quality, and PGT-A e360

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