Study question Do morphometric and morphokinetic profiles of pronuclei (PN) following intracytoplasmic sperm injection (ICSI) vary between male and female human zygotes? Summary answer Male and female zygotes displayed different PN morphometrics and morphokinetics. Additionally, variations were identified between sperm-originated (SPN) and oocyte-originated (OPN) pronuclei. What is known already Previous studies have investigated the use of PN-associated parameters via static observations as indicators of zygote viability, including size equality or juxtaposition. However, recent clinical application of time-lapse videography (TLV) provides a novel opportunity to assess these pronuclear events with greater accuracy and precision of morphometric and morphokinetic measurement. A number of recent TLV studies have also investigated potential live birth prediction by such PN associated measures, however whether or not there are sex associated differences in such measures which could in turn confound live birth prediction is unknown. Study design, size, duration This retrospective cohort study included 94 consecutive autologous single day 5 transfer cycles (either fresh or frozen) performed between January 2019 and March 2020. Only ICSI cycles (maternal age <40 years) leading to a singleton live birth (43 males and 51 females) were included for analysis. All oocytes were placed in the EmbryoScope incubator for culture immediately post sperm injection with all annotation performed retrospectively by one embryologist (L-SO). Participants/materials, setting, methods Timings included 2nd polar body extrusion (tPb2), SPN(tSPNa)/OPN(tOPNa) appearance (differentiated by proximity to Pb2) and PN fading (tPNF). Morphometrics were evaluated at 8 (stage 1), 4 (stage 2) and 0 hour before PNF (stage 3), measuring PN area (um2), PN juxtaposition, and nucleolus precursor body (NPB) arrangement. Means ± standard deviation were compared using student t test or logistic regression as odds ratio (OR) and 95% confidence interval (CI), and proportional data by chi-squared analysis. Main results and the role of chance Logistic regression indicated that male zygotes had longer time intervals of tPb2_tSPNa than female zygotes (4.8±1.5 vs 4.2±1.0 h, OR = 1.442, 95% CI 1.009-2.061, p = 0.044), but not tPb2_tOPNa (4.7±1.8 vs 4.5±1.3 h, OR = 1.224, 95% CI 0.868-1.728, p = 0.250) and tPb2_tPNF (19.9±2.8 vs 19.1±2.3 h, OR = 1.136, 95% CI 0.957-1.347, p = 0.144). SPN increased in size from stage 1 through 2 to 3 (435.3±70.2, 506.7±77.3, and 556.3±86.4 um2, p = 0.000) and OPN did similarly (399.0±59.4, 464.3±65.2, and 513.8±63.5 um2, p = 0.000), with SPN being significantly larger than OPN at each stage (p < 0.05 respectively). However, relative size difference between SPN and OPN was similar between male and female zygotes at 3 stages (33.6±61.7 vs 38.6±50.8 um2, p = 0.664; 38.5±53.1 vs 45.7±71.9 um2, p = 0.585; 38.4±77.4 vs 45.8±63.9 um2, p = 0.615; respectively). More male than female zygotes reached central PN juxtaposition at stage 1 (77% vs 51%, p = 0.010), stage 2 (98% vs 86%, p = 0.048) and stage 3 (98% vs 86%, p = 0.048). Furthermore, more OPN showed aligned NPBs than in SPN at stage 1 (45% vs 29%, p = 0.023), but similar proportions at stage 2 (64% vs 50%, p = 0.056) and stage 3 (76% vs 72%, p = 0.618). There were no sex associated differences detected in NPB alignment in either SPN or OPN (p > 0.05 respectively). Limitations, reasons for caution The retrospective design does not allow for control of unknown confounders. Sample size is considered relatively small. PN area measurement may not truly represent volume as PN may not be perfectly spherical. Findings were based on women <40 years old so may not apply to older population. Wider implications of the finding These findings augment and extend previous studies investigating PN parameters via static observations. The reported variations between male and female embryos may confound live birth prediction when using pronuclei morphometrics and morphokinetics. Larger scaled studies are warranted to verify these findings. Study funding/competing interest(s) N/A. Trial registration number N/A.
Study question What is the live birth rate after single, low-grade blastocyst (LGB) transfer? Summary answer The live birth rate for LGBs is 28%, ranging between 15–31% for the different inner cell mass (ICM) and trophectoderm (TE) subgroups of LGBs. What is known already Live birth rates following LGB transfer are varied and have been reported to be in the range of 5–39%. However, these estimates are inaccurate as studies investigating live birth rates following LGB transfer are inherently limited by sample size (n = 10–440 for LGB transfers) due to LGBs being ranked last for transfer. Further, these studies are heterogenous with varied LGB definitions and design. Collating LGB live birth data from multiple clinics is warranted to obtain sufficient numbers of LGB transfers to establish reliable live birth rates, and to allow for delineation of different LGB subgroups, including blastocyst age and female age. Study design, size, duration We performed a multicentre, multinational retrospective cohort study in 9 IVF centres in China and New Zealand from 2012 to 2019. We studied the outcome of 6966 single blastocyst transfer cycles on days 5–7 (fresh and frozen) according to blastocyst grade, including 875 transfers from LGBs (<3bb, this being the threshold typically applied to LGB studies). Blastocysts with expansion stage 1 or 2 (early blastocysts) were excluded. Participants/materials, setting, methods The main outcome measured was live birth rate. Blastocysts were grouped according to quality grade: good-grade blastocysts (GGBs; n = 3849, aa, ab and ba), moderate-grade blastocysts (MGBs; n = 2242, bb) and LGBs (n = 875, ac, ca, bc, cb and cc) and live birth rates compared using the Pearson Chi-squared test. A logistic regression analysis explored the relationship between blastocyst grade and live birth after adjustment for the confounders: clinic, female age, expansion stage, and blastocyst age. Main results and the role of chance The live birth rates for GGBs, MGBs and LGBs were 45%, 36% and 28% respectively (p < 0.0001). Within the LGB group, the highest live birth rates were for grade c TE (30%) and the lowest were for grade c ICM (19%). The lowest combined grade (cc) maintained a 15% live birth rate (n = 7/48). After accounting for confounding factors, including female age and blastocyst characteristics, the odds of live birth were 2.33 (95% CI = 1.88–2.89) for GGBs compared to LGBs and 1.56 (95% CI = 1.28–1.92) for MGBs compared to LGBs following fresh and frozen blastocyst transfers (p < 0.0001, odds ratios confirmed in exclusively frozen blastocyst transfer cycles). When stratified by individual ICM and TE grade, the odds of live birth according to ICM grade were 1.31 (a versus b; 95% CI = 1.15–1.48), 2.82 (a versus c; 95% CI = 1.91–4.18) and 2.16 (b versus c; 95% CI = 1.48–3.16; all p < 0.0001). The odds of live birth according to TE grade were 1.33 (a versus b; 95% CI = 1.17–1.50, p < 0.0001), 1.85 (a versus c; 95% CI = 1.45–2.34, p < 0.0001) and 1.39 (b versus c; 95% CI = 1.12–1.73, p = 0.0024). Limitations, reasons for caution Despite the large multicentre design of the study, analyses of transfers occurring within the smallest subsets of the LGB group were limited by sample size. The study was not randomised and had a retrospective character. Wider implications of the findings: LGBs maintain satisfactory live birth rates (averaging 28%) in the general IVF population. Even those in the lowest grading tier maintain modest live birth rates (15%; cc). It is recommended that LGBs not be universally discarded, and instead considered for subsequent frozen embryo transfer to maximize cumulative live birth rates. Trial registration number Not applicable
Study question What are the epistemic and ethical considerations of clinically implementing Artificial Intelligence (AI) algorithms in embryo selection? Summary answer AI embryo selection algorithms used to date are “black-box” models with significant epistemic and ethical issues, and there are no trials assessing their clinical effectiveness. What is known already The innovation of time-lapse imaging offers the potential to generate vast quantities of data for embryo assessment. Computer Vision allows image data to be analysed using algorithms developed via machine learning which learn and adapt as they are exposed to more data. Most algorithms are developed using neural networks and are uninterpretable (or “black box”). Uninterpretable models are either too complicated to understand or proprietary, in which case comprehension is impossible for outsiders. In the IVF context, these outsiders include doctors, embryologists and patients, which raises ethical questions for its use in embryo selection. Study design, size, duration We performed a scoping review of articles evaluating AI for embryo selection in IVF. We considered the epistemic and ethical implications of current approaches. Participants/materials, setting, methods We searched Medline, Embase, ClinicalTrials.gov and the EU Clinical Trials Register for full text papers evaluating AI for embryo selection using the following key words: artificial intelligence* OR AI OR neural network* OR machine learning OR support vector machine OR automatic classification AND IVF OR in vitro fertilisation OR embryo*, as well as relevant MeSH and Emtree terms for Medline and Embase respectively. Main results and the role of chance We found no trials evaluating clinical effectiveness either published or registered. We found efficacy studies which looked at 2 types of outcomes – accuracy for predicting pregnancy or live birth and agreement with embryologist evaluation. Some algorithms were shown to broadly differentiate well between “good-” and “poor-” quality embryos but not between embryos of similar quality, which is the clinical need. Almost universally, the AI models were opaque (“black box”) in that at least some part of the process was uninterpretable. “Black box” models are problematic for epistemic and ethical reasons. Epistemic concerns include information asymmetries between algorithm developers and doctors, embryologists and patients; the risk of biased prediction caused by known and/or unknown confounders during the training process; difficulties in real-time error checking due to limited interpretability; the economics of buying into commercial proprietary models, brittle to variation in the treatment process; and an overall difficulty troubleshooting. Ethical pitfalls include the risk of misrepresenting patient values; concern for the health and well-being of future children; the risk of disvaluing disability; possible societal implications; and a responsibility gap, in the event of adverse events. Limitations, reasons for caution Our search was limited to the two main medical research databases. Although we checked article references for more publications, we were less likely to identify studies that were not indexed in Medline or Embase, especially if they were not cited in studies identified in our search. Wider implications of the findings It is premature to implement AI for embryo selection outside of a clinical trial. AI for embryo selection is potentially useful, but must be done carefully and transparently, as the epistemic and ethical issues are significant. We advocate for the use of interpretable AI models to overcome these issues. Trial registration number not applicable
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