The clinical use of time-lapse in human-assisted reproduction

Deep learning applications for human embryo assessment using time-lapse imaging: scoping review

Background

The integration of deep learning (DL) and time-lapse imaging technologies offers new possibilities for improving embryo assessment and selection in clinical in vitro Fertilization (IVF).

Objectives

This scoping review aims to explore the range of deep learning model applications in the evaluation and selection of embryos monitored through time-lapse imaging systems.

Methods

A total of 6 electronic databases (Scopus, MEDLINE, EMBASE, ACM Digital Library, IEEE Xplore, and Google Scholar) were searched for peer-reviewed literature published before May 2024. We adhered to the PRISMA guidelines for reporting scoping reviews.

Results

Out of the 773 articles reviewed, 77 met the inclusion criteria. Over the past four years, the use of DL in embryo analysis has increased rapidly. The primary applications of DL in the reviewed studies included predicting embryo development and quality (61%, n = 47) and forecasting clinical outcomes, such as pregnancy and implantation (35%, n = 27). The number of embryos involved in the studies exhibited significant variation, with a mean of 10,485 (SD = 35,593) and a range from 20 to 249,635 embryos. A variety of data types have been used, namely images of blastocyst-stage embryos (47%, n = 36), followed by combined images of cleavage and blastocyst stages (23%, n = 18). Most of the studies did not provide maternal age details (82%, n = 63). Convolutional neural networks (CNNs) were the predominant deep learning architecture used, accounting for 81% (n = 62) of the studies. All studies utilized time-lapse video images (100%) as training data, while some also incorporated demographics, clinical and reproductive histories, and IVF cycle parameters. Most studies utilized accuracy as the discriminative measure (58%, n = 45).

Conclusion

Our results highlight the diverse applications and potential of deep learning in clinical IVF and suggest directions for future advancements in embryo evaluation and selection techniques.

Viewing early life without labels: optical approaches for imaging the early embryo†

Embryo quality is an important determinant of successful implantation and a resultant live birth. Current clinical approaches for evaluating embryo quality rely on subjective morphology assessments or an invasive biopsy for genetic testing. However, both approaches can be inherently inaccurate and crucially, fail to improve the live birth rate following the transfer of in vitro produced embryos. Optical imaging offers a potential non-invasive and accurate avenue for assessing embryo viability. Recent advances in various label-free optical imaging approaches have garnered increased interest in the field of reproductive biology due to their ability to rapidly capture images at high resolution, delivering both morphological and molecular information. This burgeoning field holds immense potential for further development, with profound implications for clinical translation. Here, our review aims to: (1) describe the principles of various imaging systems, distinguishing between approaches that capture morphological and molecular information, (2) highlight the recent application of these technologies in the field of reproductive biology, and (3) assess their respective merits and limitations concerning the capacity to evaluate embryo quality. Additionally, the review summarizes challenges in the translation of optical imaging systems into routine clinical practice, providing recommendations for their future development. Finally, we identify suitable imaging approaches for interrogating the mechanisms underpinning successful embryo development.

Non-invasive evaluation of embryo quality for the selection of transferable embryos in human in vitro fertilization-embryo transfer

The ultimate goal of human assisted reproductive technology is to achieve a healthy pregnancy and birth, ideally from the selection and transfer of a single competent embryo. Recently, techniques for efficiently evaluating the state and quality of preimplantation embryos using time-lapse imaging systems have been applied. Artificial intelligence programs based on deep learning technology and big data analysis of time-lapse monitoring system during in vitro culture of preimplantation embryos have also been rapidly developed. In addition, several molecular markers of the secretome have been successfully analyzed in spent embryo culture media, which could easily be obtained during in vitro embryo culture. It is also possible to analyze small amounts of cell-free nucleic acids, mitochondrial nucleic acids, miRNA, and long non-coding RNA derived from embryos using real-time polymerase chain reaction (PCR) or digital PCR, as well as next-generation sequencing. Various efforts are being made to use non-invasive evaluation of embryo quality (NiEEQ) to select the embryo with the best developmental competence. However, each NiEEQ method has some limitations that should be evaluated case by case. Therefore, an integrated analysis strategy fusing several NiEEQ methods should be urgently developed and confirmed by proper clinical trials.

Modeling three sources of uncertainty in assisted reproductive technologies with probabilistic graphical models

Embryo selection is a critical step in assisted reproduction: good selection criteria are expected to increase the probability of inducing a pregnancy. Machine learning techniques have been applied for implantation prediction or embryo quality assessment, which embryologists can use to make a decision about embryo selection. However, this is a highly uncertain real-world problem, and current proposals do not model always all the sources of uncertainty. We present a novel probabilistic graphical model that accounts for three different sources of uncertainty, the standard embryo and cycle viability, and a third one that represents any unknown factor that can drive a treatment to a failure in otherwise perfect conditions. We derive a parametric learning method based on the Expectation-Maximization strategy, which accounts for uncertainty issues. We empirically analyze the model within a real database consisting of 604 cycles (3125 embryos) carried out at Hospital Donostia (Spain). Embryologists followed the protocol of the Spanish Association for Reproduction Biology Studies (ASEBIR), based on morphological features, for embryo selection. Our model predictions are correlated with the ASEBIR protocol, which validates our model. The benefits of accounting for the different sources of uncertainty and the importance of the cycle characteristics are shown. Considering only transferred embryos, our model does not further discriminate them as implanted or failed, suggesting that the ASEBIR protocol could be understood as a thorough summary of the available morphological features.

Zygote Diameter and Total Cytoplasmic Volume as Useful Predictive Tools of Blastocyst Quality

Introduction According to the Embryo Protection Act, the selection of embryos with the greatest potential for successful implantation in Germany must be performed in the pronucleus stage. The main aim of this study was to identify morphokinetic parameters that could serve as noninvasive biomarkers of blastocyst quality in countries with restrictive reproductive medicine laws. Materials and Methods The sample comprised 191 embryos from 40 patients undergoing antagonist cycles for intracytoplasmic sperm injection. Blastocysts were cultured in an EmbryoScope chamber and video records were validated to determine the post-injection timing of various developmental stages, cleavage stages, and blastocyst formation. The Gardner and Schoolcraft scoring system was used to characterize blastocyst quality. Results Morphokinetic data showed that the zygote diameter and total cytoplasmic volume were significantly different between good and poor blastocysts quality groups, where zygotes, which formed better blastocyst quality, had smaller diameter and smaller total cytoplasmic volume. Zygotes with more rapid pronuclear disappearance developed in better-quality blastocysts. Differences between good- and poor-quality blastocysts were also observed for late-stage parameters and for the spatial arrangement of blastomere where tetrahedral embryos more frequently forming good-quality blastocyst compare to the non-tetrahedral. Conclusions The study findings could be used to enhance embryo selection, especially in countries with strict Embryo Law Regulations. Further studies, including those in which the implantation potential and pregnancy rate are considered, are warranted to confirm these preliminary results.

Case Report: Post-Partum SARS-CoV-2 Infection After the First French Uterus Transplantation

Absolute uterus factor infertility, whether congenital or acquired, renders the woman unable to carry a child. Although uterus transplantation (UTx) is being increasingly performed as a non-vital procedure to address this unfortunate condition, the immunosuppression required presents risks that are further compounded by pregnancy and during the puerperium period. These vulnerabilities require avoidance of SARS-CoV-2 infection in pregnant UTx recipients especially during the third trimester, as accumulating evidence reveals increased risks of morbidity and mortality. Here we describe a successful UTx case with delivery of a healthy child, but in which both mother and neonate developed asymptomatic SARS-CoV-2 infection seven days after RNA vaccination, on day 35 post-partum. Although the patient was successfully treated with a combination therapy comprised of two monoclonal antibodies, this case highlights the challenges associated with performing UTx in the era of Covid-19. More broadly, the risks of performing non-vital organ transplantation during a pandemic should be discussed among team members and prospective patients, weighing the risks against the benefits in improving the quality of life, which were considerable for our patient who achieved motherhood with the birth of a healthy child.