Should there be an "AI" in TEAM? Embryologists selection of high implantation potential embryos improves with the aid of an artificial intelligence algorithm.

PURPOSE: A deep learning artificial intelligence (AI) algorithm has been demonstrated to outperform embryologists in identifying euploid embryos destined to implant with an accuracy of 75.3% (1). Our aim was to evaluate the performance of highly trained embryologists in selecting top quality day 5 euploid blastocysts with and without the aid of a deep learning algorithm. MATERIALS AND METHODS: A non-overlapping series of 200 sets of day 5 euploid embryo images with known implantation outcomes was distributed to 17 highly trained embryologists. One embryo in each set was known to have implanted and one failed implantation. They were asked to select which embryo to transfer from each set. The same 200 sets of embryos, with indication of which embryo in each set had been identified by the algorithm as more likely to implant was then distributed. Chi-squared, t-test, and receiver operating curves were performed to compare the embryologist performeance with and without AI. RESULTS: Fourteen embryologists completed both assessments. Embryologists provided with AI results selected successfully implanted embryos in 73.6% of cases compared to 65.5% for those selected using visual assessments alone (p < 0.001). All embryologists improved in their ability to select embryos with the aid of the AI algorithm with a mean percent improvement of 11.1% (range 1.4% to 15.5%). There were no differences in degree of improvement by embryologist level of experience (junior, intermediate, senior). CONCLUSIONS: The incorporation of an AI framework for blastocyst selection enhanced the performance of trained embryologists in identifying PGT-A euploid embryos destined to implant.

Allelic switching of the imprinted IGF2R gene in cloned bovine fetuses and calves.

Cloned animals often suffer from loss of development to term and abnormalities, typically classified under the umbrella term of Large Offspring Syndrome (LOS). Cattle are an interesting species to study because of the relatively greater success rate of nuclear transfer in this species compared with all species cloned to date. The imprinted insulin-like growth factor receptor (IGF2R; mannose-6-phosphate) gene was chosen to investigate aspects of fetal growth and development in cloned cattle in the present study. IGF2R gene expression patterns in identical genetic clones of several age groups were assessed in day 25, day 45, and day 75 fetuses as well as spontaneously aborted fetuses, calves that died shortly after birth and healthy cloned calves using single stranded conformational polymorphism gel electrophoresis. A variable pattern of IGF2R allelic expression in major organs such as the brain, cotyledon, heart, liver, lung, spleen, kidney and intercotyledon was observed using a G/A transition in the 3'UTR of IGF2R. IGF2R gene expression was also assessed by real time RT-PCR and found to be highly variable among the clone groups. Proper IGF2R gene expression is necessary for survival to term, but is most likely not a cause of early fetal lethality or an indicator of postnatal fitness. Contrary to previous reports of the transmission of imprinting patterns from somatic donor cells to cloned animals within organs in the same cloned animal the paternal allele of IGF2R can be imprinted in one tissue while the maternal allele is imprinted in another tissue. This observation has never been reported in any species in which imprinting has been studied.