M. Toschi

The Outcome of Intracytoplasmic Sperm Injection Using Occasional Spermatozoa in the Ejaculate of Men With Spermatogenic Failure

PURPOSE Men with spermatogenic failure so profound that they are considered as having nonobstructive azoospermia occasionally have spermatozoa in the ejaculate. We compared intracytoplasmic sperm injection outcomes following the injection of ejaculated or surgically retrieved spermatozoa from these men. MATERIALS AND METHODS A study was performed of intracytoplasmic sperm injection cycles with no spermatozoa on initial semen analysis and 100 or fewer following centrifugation (cryptozoospermia). Only 16 couples that underwent intracytoplasmic sperm injection cycles with ejaculated spermatozoa and cycles with testicular spermatozoa were included. RESULTS Initial analysis was done to compare outcomes between the 2 semen origins. There was no difference in the rate of normal or abnormal fertilization between the 2 groups. The rate of clinical pregnancies seemed to favor testicular spermatozoa (47.4% vs 20.8%), although results were not significant. When a comparison was performed between the first testicular cycle and the ejaculated cycle closest in time to the cycle with testicular spermatozoa, a higher rate of normal fertilization with testicular spermatozoa was observed (60.9% vs 48.5%, p <0.05). Also, in this comparison a clear trend toward a higher percent of clinical pregnancies and deliveries in the testicular group was observed (50.0% vs 14.3%). CONCLUSIONS 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.

Robust Automated Assessment of Human Blastocyst Quality using Deep Learning

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 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.