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).

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.

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.

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.

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.

Automatic characterization of human embryos at day 4 post-insemination from time-lapse imaging using supervised contrastive learning and inductive transfer learning techniques.

Background: Embryo morphology is a predictive marker for implantation success and ultimately live births. Viability evaluation and quality grading are commonly used to select the embryo with the highest implantation potential. However, the traditional method of manual embryo assessment is time-consuming and highly susceptible to inter- and intra-observer variability.

The higher the score, the better the clinical outcome: retrospective evaluation of automatic embryo grading as a support tool for embryo selection in IVF laboratories.

Study Question: Is the automatic embryo grading function of specific time-lapse systems clinically useful as a decision support tool for IVF laboratories?

Summary Answer: Blastocyst grading according to the automatic scoring system is directly associated with the likelihood of implantation and live birth, at least in treatments without preimplantation genetic testing for aneuploidy (PGT-A).

What Is Known Already: Several embryo selection algorithms have been described since the introduction of time-lapse technology in IVF laboratories, but no one algorithm has yet been sufficiently consolidated for universal use. Multicentric models based on automated grading systems offer promise for standardization of embryo selection.

An artificial intelligence model based on the proteomic profile of euploid embryos and blastocyst morphology: a preliminary study.

Research Question: The study aimed to develop an artificial intelligence model based on artificial neural networks (ANNs) to predict the likelihood of achieving a live birth using the proteomic profile of spent culture media and blastocyst morphology.

Design: This retrospective cohort study included 212 patients who underwent single blastocyst transfer at IVI Valencia. A single image of each of 186 embryos was studied, and the protein profile was analysed in 81 samples of spent embryo culture medium from patients included in the preimplantation genetic testing programme.

Assessment of embryo implantation potential with a cloud-based automatic software.

Research Question: Is embryo selection by Dana (automatic software for embryo evaluation) associated with a higher implantation rate in IVF treatments?

Design: A three-phase study for Dana system's validation: creation of a data-cloud of known implantation data (KID) embryos from 1676 transferred embryos; embryo evaluation by Dana considering manual annotations and embryo development videos (389 transferred embryos); and validation of Dana automatic selection, without embryologist's intervention (147 transferred embryos); RESULTS: The implantation rate of the 1021 KID embryos from phase 1 served to set four grades of embryos referring to implantation rate: A = 34%, B = 25%, C = 24%, and D = 19%. Phase 2: a classification ranking according to the unit average distance (UAD) and implantation potential was established: top (UAD ≤0.50), high (UAD = 0.

Novel and conventional embryo parameters as input data for artificial neural networks: an artificial intelligence model applied for prediction of the implantation potential.

Objective: To describe novel embryo features capable of predicting implantation potential as input data for an artificial neural network (ANN) model.

Design: Retrospective cohort study.

Setting: University-affiliated private IVF center.

Structural analysis of Drosophila subobscura gypsy elements (gypsyDs).

The study of gypsy elements in Drosophila subobscura (gypsyDs) indicated that they are transcriptionally active and mobile. From the comparative analysis of a complete gypsyDs element with the canonical gypsy sequence from D. melanogaster (gypsyDm) it can be deduced that while the whole structure is maintained, the gypsyDs ORF3 encodes a non-functional Env protein.