Advancements and Challenges in Preimplantation Genetic Testing for Aneuploidies: In the Pathway to Non-Invasive Techniques.

The evolution of preimplantation genetic testing for aneuploidy (PGT-A) techniques has been crucial in assisted reproductive technologies (ARTs), improving embryo selection and increasing success rates in in vitro fertilization (IVF) treatments. Techniques ranging from fluorescence in situ hybridization (FISH) to next-generation sequencing (NGS) have relied on cellular material extraction through biopsies of blastomeres at the cleavage stage on day three or from trophectoderm (TE) cells of the blastocyst. However, this has raised concerns about its potential impact on embryo development.

Machine learning tool for predicting mature oocyte yield and trigger day from start of stimulation: towards personalized treatment.

Research Question: Can machine learning tools predict the number of metaphase II (MII) oocytes and trigger day at the start of the ovarian stimulation cycle?

Design: A multicentre, retrospective study including 56,490 ovarian stimulation cycles (primary dataset) was carried out between 2020 and 2022 for analysis and feature selection. Of these, 13,090 were used to develop machine learning models for trigger day and the number of MII prediction, and another 5103 ovarian stimulation cycles (clinical validation dataset) from 2023 for clinical validation. Machine learning algorithms using deep learning were developed using optimal features from the primary dataset based on correlation.

A Combination of Artificial Intelligence with Genetic Algorithms on Static Time-Lapse Images Improves Consistency in Blastocyst Assessment, An Interpretable Tool to Automate Human Embryo Evaluation: A Retrospective Cohort Study.

Background: In recent times, various algorithms have been developed to assist in the selection of embryos for transfer based on artificial intelligence (AI). Nevertheless, the majority of AI models employed in this context were characterized by a lack of transparency. To address these concerns, we aim to design an interpretable tool to automate human embryo evaluation by combining artificial neural networks (ANNs) and genetic algorithms (GA).

Corticosteroids, androgens, progestogens and oestrogens in the endometrial microenvironment, and their association with endometrial progression and function.

Research Question: How does the intracrine action of progestagens, oestrogens, androgens and corticosteroids affect endometrial tissue progression and function?

Design: In this prospective observational study, 76 patients (<50 years old, no uterine pathologies and at least one failed IVF cycle) undergoing endometrial biopsy collection for endometrial evaluation between 2018 and 2021 were included. The concentrations of 11 steroid metabolites (cortisone, cortisol, progesterone, oestrone, 2-methoxyestrone, oestradiol, oestriol, testosterone, androstenedione, 17α-hydroxyprogesterone and 17-hydroxypregnenolone) were measured by ultra-performance liquid chromatography-tandem mass spectrometry in the endometrial tissue samples collected during the mid-secretory phase. Endometrial dating and reproductive outcomes (relative to the next good-quality fresh or frozen embryo transfer after the biopsy) were analysed in relation to endometrial steroid concentrations using Barnard's test; correlations between metabolite concentrations were measured by Pearson's correlation co-efficient.

External validation of a fully automated evaluation tool: a retrospective analysis of 68,471 scored embryos.

Objective: To externally validate a fully automated embryo classification system for in vitro fertilization (IVF) treatments.

Design: Retrospective cohort study.

Setting: Clinic.

Visual interpretability of image-based classification models by generative latent space disentanglement applied to in vitro fertilization.

The success of deep learning in identifying complex patterns exceeding human intuition comes at the cost of interpretability. Non-linear entanglement of image features makes deep learning a "black box" lacking human meaningful explanations for the models' decision. We present DISCOVER, a generative model designed to discover the underlying visual properties driving image-based classification models.

Testing an artificial intelligence algorithm to predict fetal heartbeat of vitrified-warmed blastocysts from a single image: predictive ability in different settings.

Study Question: Could an artificial intelligence (AI) algorithm predict fetal heartbeat from images of vitrified-warmed embryos?

Summary Answer: Applying AI to vitrified-warmed blastocysts may help predict which ones will result in implantation failure early enough to thaw another.

What Is Known Already: The application of AI in the field of embryology has already proven effective in assessing the quality of fresh embryos. Therefore, it could also be useful to predict the outcome of frozen embryo transfers, some of which do not recover their pre-vitrification volume, collapse, or degenerate after warming without prior evidence.

Undisturbed culture: a clinical examination of this culture strategy on embryo in vitro development and clinical outcomes.

Objective: To compare the effect of a fully undisturbed culture strategy over a sequential one on embryo in vitro development and clinical outcomes in intracytoplasmic sperm injection (ICSI) cycles.

Design: Retrospective cohort study.

Setting: University-affiliated private IVF center.

Systemic changes induced by autologous stem cell ovarian transplant in plasma proteome of women with impaired ovarian reserves.

Patients with poor ovarian response (POR) and premature ovarian insufficiency (POI) are challenging to treat, with oocyte donation remaining as the only feasible option to achieve pregnancy in some cases. The Autologous stem cell ovarian transplantation (ASCOT) technique allows follicle development, enabling pregnancies and births of healthy babies in these patients. Previous results suggest that growth factors and cytokines secreted by stem cells are partially responsible for their regenerative properties.

Deep learning system for classification of ploidy status using time-lapse videos.

Objective: To develop a spatiotemporal model for de prediction of euploid and aneuploid embryos using time-lapse videos from 10-115 hours after insemination (hpi).

Design: Retrospective study.

Main Outcome Measures: The research used an end-to-end approach to develop an automated artificial intelligence system capable of extracting features from images and classifying them, considering spatiotemporal dependencies.

Time-lapse imaging: Morphokinetic analysis of in vitro fertilization outcomes.

Time-lapse imaging (TLI) allows continuous monitoring of embryo development without disturbing culture conditions by removing embryos from the incubator. The study of embryo kinetics using TLI has given rise to some new markers for embryo selection that are able to document and evaluate embryo morphology and the timing of developmental events through continuous live image tracking. Time-lapse imaging has emerged as a powerful tool for creating predictive models of in vitro fertilization outcomes.

Clinical validation of an automatic classification algorithm applied on cleavage stage embryos: analysis for blastulation, euploidy, implantation, and live-birth potential.

Study Question: Is a commercially available embryo assessment algorithm for early embryo evaluation based on the automatic annotation of morphokinetic timings a useful tool for embryo selection in IVF cycles?

Summary Answer: The classification provided by the algorithm was shown to be significantly predictive, especially when combined with conventional morphological evaluation, for development to blastocyst, implantation, and live birth, but not for euploidy.

What Is Known Already: The gold standard for embryo selection is still morphological evaluation conducted by embryologists. Since the introduction of time-lapse technology to embryo culture, many algorithms for embryo selection have been developed based on embryo morphokinetics, providing complementary information to morphological evaluation.

Single-cell multi-omic analysis profiles defective genome activation and epigenetic reprogramming associated with human pre-implantation embryo arrest.

During pre-implantation stages of mammalian development, maternally stored material promotes both the erasure of the sperm and oocyte epigenetic profiles and is responsible for concomitant genome activation. Here, we have utilized single-cell methylome and transcriptome sequencing (scM&T-seq) to quantify both mRNA expression and DNA methylation in oocytes and a developmental series of human embryos at single-cell resolution. We fully characterize embryonic genome activation and maternal transcript degradation and map key epigenetic reprogramming events in developmentally high-quality embryos.

Rivaroxaban Monotherapy in Patients with Pulmonary Embolism: Off-Label vs. Labeled Therapy.

Background: The use of rivaroxaban in clinical practice often deviates from manufacturer prescribing information. No studies have demonstrated an association between this practice and improved outcomes. Methods: We used the RIETE registry to assess the clinical characteristics of patients with pulmonary embolism (PE) who received off-label rivaroxaban, and to compare their 3-month outcomes with those receiving the labeled therapy.

A propensity score-based, comparative study assessing humid and dry time-lapse incubation, with single-step medium, on embryo development and clinical outcomes.

Study Question: Does culture in a high relative humidity atmosphere improve clinical outcomes when using a time-lapse integrated incubator and single-step culture medium?

Summary Answer: Using an integrated time-lapse system and single-step culture medium, culture in a high relative humidity atmosphere increases the likelihood of embryos, especially those subjected to preimplantation genetic testing for aneuploidies, to achieve a pregnancy compared to those cultured in dry conditions.

What Is Known Already: The use of a humid atmosphere inside incubators can reduce changes in culture media osmolality, which has been reported to have a significant effect on embryo quality and morphokinetics. Studies assessing the effect of humid culture (HC) in clinical outcomes are, however, scarce and inconclusive, mostly due to a high variability in culture conditions and reduced sample size.