Enhancing the Accuracy of Semen Analysis With Artificial Intelligence-Based Technology

Abstract

OBJECTIVE: To characterize the impact of state IVF insurance mandates on embryo transfer practices in terms of mean number of embryos per transfer and utilization of fresh versus frozen embryo transfers.MATERIALS AND METHODS: Data from the 2018 CDC ART Fertility Clinic Success Rates Report and the National ART Surveillance System (NASS) were used to calculate the number of embryos transferred per procedure and number of fresh and frozen embryo transfers. Data were stratified into two groups, ''Comprehensive'' and ''Non-comprehensive,'' based on the degree of IVF coverage mandated by state laws. Two-sample t-test and chisquare test were used to compare mean number of embryos transferred per procedure and frozen transfers as a share of all transfers, respectively, according to mandate status.RESULTS: In 2018, 456 clinics reported data to the CDC. Comprehensive IVF mandates were identified in 6 states. Mean number of embryos per transfer was significantly lower in states with comprehensive IVF mandates compared to states without these mandates among all transfer types. On age-group sub-analysis, this difference remained significant among transfers performed in younger age groups. Significantly more embryos were transferred in fresh than frozen embryo transfers. Significantly fewer frozen transfers as a share of all embryo transfers were performed in states with comprehensive IVF mandates.CONCLUSIONS: Comprehensive IVF insurance mandates were associated with a significantly lower mean number of embryos transferred per procedure. More embryos were transferred in fresh compared to frozen embryo transfers. Frozen embryo transfers accounted for smaller share of embryo transfers in states with comprehensive IVF mandates.

‘Intracytoplasmic sperm injection (ICSI) paradox’ and ‘andrological ignorance’: AI in the era of fourth industrial revolution to navigate the blind spots

“…The conventional manual semen analysis is subjective and can have significant variability between observers. AI-assisted analysis, on the other hand, can potentially provide more consistent and reliable results [ 27 ]. Another example of potential of AI in the field of male infertility can be seen with the use of convolutional neural networks (CNNs), a type of AI, for automated sperm analysis [ 28 ].…”