Estimating the causal effect of embryo transfer day on clinical in vitro fertilization outcomes using propensity score matching.

BACKGROUND: For women undergoing in vitro fertilization (IVF), the clinical benefit of embryo transfer at the blastocyst stage (Day 5) versus cleavage stage (Day 3) remains controversial. The purpose of this study is to compare the implantation rate, clinical pregnancy rate and odds of live birth of Day 3 and Day 5 embryo transfer, and more importantly, to address the issue that patients were chosen to receive either transfer protocol due to their underlying clinical characteristics, i.e., confounding by indication. METHODS: We conducted a retrospective cohort study of 9,090 IVF cycles collected by Lee Women's Hospital in Taichung, Taiwan from 1998 to 2014. We utilized the method of propensity score matching to mimic a randomized controlled trial (RCT) where each patient with Day 5 transfer was matched by another patient with Day 3 transfer with respect to other clinical characteristics. Implantation rate, clinical pregnancy rate, and odds of live birth were compared for women underwent Day 5 transfer and Day 3 transfer to estimate the causal effects. We further investigated the causal effects in subgroups by stratifying age and anti-Mullerian hormone (AMH). RESULTS: Our analyses uncovered an evidence of a significant difference in implantation rate (p=0.04) favoring Day 5 transfer, and showed that Day 3 and Day 5 transfers made no difference in both odds of live birth (p=0.27) and clinical pregnancy rate (p=0.11). With the increase of gestational age, the trend toward non-significance of embryo transfer day in our result appeared to be consistent for subgroups stratified by age and AMH, while all analyses stratified by age and AMH were not statistically significant. CONCLUSIONS: We conclude that for women without strong indications for Day 3 or Day 5 transfer, there is a small significant difference in implantation rate in favor of Day 5 transfer. However, the two protocols have indistinguishable outcomes on odds of live birth and clinical pregnancy rate.

End-to-end deep learning for recognition of ploidy status using time-lapse videos.

PURPOSE: Our retrospective study is to investigate an end-to-end deep learning model in identifying ploidy status through raw time-lapse video. METHODS: By randomly dividing the dataset of time-lapse videos with known outcome of preimplantation genetic testing for aneuploidy (PGT-A), a deep learning model on raw videos was trained by the 80% dataset, and used to test the remaining 20%, by feeding time-lapse videos as input and the PGT-A prediction as output. The performance was measured by an average area under the curve (AUC) of the receiver operating characteristic curve. RESULT(S): With 690 sets of time-lapse video image, combined with PGT-A results, our deep learning model has achieved an AUC of 0.74 from the test dataset (138 videos), in discriminating between aneuploid embryos (group 1) and others (group 2, including euploid and mosaic embryos). CONCLUSION: Our model demonstrated a proof of concept and potential in recognizing the ploidy status of tested embryos. A larger scale and further optimization on the exclusion criteria would be included in our future investigation, as well as prospective approach.