Artificial intelligence in the fertility clinic: status, pitfalls and possibilities

Predicting the risk of a high proportion of three/multiple pronuclei (3PN/MPN) zygotes in individual IVF cycles using comparative machine learning algorithms

BACKGROUND

The majority of machine learning applications in assisted reproduction have been focused on predicting the likelihood of pregnancy. In the present study, we aim to investigate which machine learning models are most effective in predicting the occurrence of a high proportion (>30 %) of 3PN/MPN zygotes in individual IVF cycles.

METHODS

Eight machine learning algorithms were trained and compared, including the AdaBoost and Gaussian NB. Data from IVF cycles carried out from September 2015 to September 2019 were used as a training set. Cycle data from October 2019 to June 2020 were used as a validation set to verify the training model. Cycles with a 3PN/MPN zygote proportion higher than 30 % were classified as high 3PN/MPN zygote proportion cycles.

RESULTS

The AdaBoost algorithm was the best model for model construction and external validation. In both the training and validation sets, age, basal FSH, FSH and E2 level on the day of Gonadotrophin (GN) stimulation, and FSH and LH levels on the day of HCG were statistically higher in patients with 3PN/MPN > 30 % than in patients with 3PN/MPN ≤ 30 %; AFC, AMH, E2 level on HCG day and total number of oocytes were lower in patients with 3PN/MPN > 30 % than in patients with 3PN/MPN ≤ 30 %. The top five predictors were the number of oocytes retrieved, age, male factor infertility, AFC, and total days of GN stimulation.

CONCLUSION

By applying a suitable machine learning algorithm, we can potentially predict the risk of a high proportion of 3PN/MPN zygotes in individual IVF cycles before insemination and avoid polyspermy fertilization by ICSI fertilization method.

Utilizing MV-FLOW™ and multidimensional ultrasound characteristics for prognosticating FET outcomes in RIF patients: Study Protocol for a cross-sectional study

Recurrent implantation failure (RIF) is a common issue in frozen-thawed embryo transfer (FET). Prior to transfer, uterine endometrial receptivity of FET patients can be assessed using multimodal transvaginal ultrasound indicators to predict the success rate of the current FET cycle. Endometrial blood flow is a crucial element in evaluating endometrial receptivity. MV-FLOW™ is an advanced two-dimensional superb microvascular imaging technology that can detect and display blood flow in micro-vessels. The data for this study were obtained from an ongoing cross-sectional study comprising 323 RIF patients and 323 first implantation (FI) patients, who underwent transvaginal ultrasound before FET. We collected basic clinical data and multimodal ultrasound data from these patients as predictive features, with clinical pregnancy as the predictive label, for model training. Based on the above, this study aims to establish and validate a clinical prediction model for FET outcomes using support vector classification (SVC) algorithms, based on MV-FLOW™ and multidimensional transvaginal ultrasound imaging features. The objective is to determine the predictive role of multimodal transvaginal ultrasound in embryo transfer outcomes and provide evidence for the clinical application of MV-FLOW™. Trial registration: Trial Registration: ChiCTR2400086401.

Ethics of artificial intelligence in embryo assessment: mapping the terrain

Artificial intelligence (AI) has the potential to standardize and automate important aspects of fertility treatment, improving clinical outcomes. One promising application of AI in the fertility clinic is the use of machine learning (ML) tools to assess embryos for transfer. The successful clinical implementation of these tools in ways that do not erode consumer trust requires an awareness of the ethical issues that these technologies raise, and the development of strategies to manage any ethical concerns. However, to date, there has been little published literature on the ethics of using ML in embryo assessment. This mini-review contributes to this nascent area of discussion by surveying the key ethical concerns raised by ML technologies in healthcare and medicine more generally, and identifying which are germane to the use of ML in the assessment of embryos. We report concerns about the 'dehumanization' of human reproduction, algorithmic bias, responsibility, transparency and explainability, deskilling, and justice.

Artificial intelligence and sexual reproductive health and rights: a technological leap towards achieving sustainable development goal target 3.7

Target 3.7 of the Sustainable Development Goals (SDGs) aims for universal access to sexual and reproductive health (SRH) services by 2030, including family planning services, information, education, and integration into national strategies. In contemporary times, reproductive medicine is progressively incorporating artificial intelligence (AI) to enhance sperm cell prediction and selection, in vitro fertilisation models, infertility and pregnancy screening. AI is being integrated into five core components of Sexual Reproductive Health, including improving care, providing high-quality contraception and infertility services, eliminating unsafe abortions, as well as facilitating the prevention and treatment of sexually transmitted infections. Though AI can improve sexual reproductive health and rights by addressing disparities and enhancing service delivery, AI-facilitated components have ethical implications, based on existing human rights and international conventions. Heated debates persist in implementing AI, particularly in maternal health, as well as sexual, reproductive health as the discussion centers on a torn between human touch and machine-driven care. In spite of this and other challenges, AI's application in sexual, and reproductive health and rights is crucial, particularly for developing countries, especially those that are yet to explore the application of AI in healthcare. Action plans are needed to roll out AI use in these areas effectively, and capacity building for health workers is essential to achieve the Sustainable Development Goals' Target 3.7. This commentary discusses innovations in sexual, and reproductive health and rights in meeting target 3.7 of the SDGs with a focus on artificial intelligence and highlights the need for a more circumspective approach in response to the ethical and human rights implications of using AI in providing sexual and reproductive health services.

Preimplantation development analysis of aneuploid embryos with different chromosomal abnormalities

Background

The change of morphokinetic pattern in aneuploid embryos will facilitate the non-invasive selection of euploid embryos. In this study, we investigated the impact of different chromosomal abnormalities on the morphokinetic patterns of embryonic development.

Methods

Our cohort includes 939 time-lapse preimplantation genetic testing cycles performed between January 2019 and July 2022 at a single academic fertility center, with a total of 2876 biopsied blastocysts. Intracytoplasmic sperm injection, blastocyst culture, trophectoderm biopsy, time-lapse monitoring, and next-generation sequencing were performed.

Results

After adjusting for patient- and cycle-related factors, six morphokinetic parameters (t5, P = 0.006; t8, P = 0.048; tSB, P < 0.001; tB,P < 0.001; t5-t2, P = 0.004; tB-tSB, P < 0.001) were significant in multilevel mixed-effects logistic regression model analysis for morphokinetic parameters to predict euploid or aneuploid embryos. None of the patient- or cycle-related factors systematically affected any morphokinetic parameter. Morphokinetic parameters of late cleavage and blastocyst stages in embryos with chromosome fragment deletion (t4 to t8, tB, t5-t2, tB-tSB, ECC2, ECC3, s2, P < 0.05) or duplication (t4, t5, tSB, tB, t5-t2, P < 0.05) were prolonged, and the morphokinetic parameters of the blastocyst stage in monosomic embryos (tSB, tB, tB-tSB, P < 0.01) were prolonged. Partial or complete chromosome 20 or 22 deletion can cause significant delays in multiple parameters of cleavage and blastocyst stages (from t4 to tB, P < 0.05).

Conclusions

Our study found that different chromosomal abnormalities have different effects on the morphokinetic parameters. Significant delays in morphokinetic parameters at different stages were found in fragment-mutated embryos and monosomic embryos. This can provide insights into the pre-implantation development pattern of aneuploid embryos and help non-invasive embryo selection.

Artificial Intelligence in Human Reproduction

The use of artificial intelligence (AI) in human reproduction is a rapidly evolving field with both exciting possibilities and ethical considerations. This technology has the potential to improve success rates and reduce the emotional and financial burden of infertility. However, it also raises ethical and privacy concerns. This paper presents an overview of the current and potential applications of AI in human reproduction. It explores the use of AI in various aspects of reproductive medicine, including fertility tracking, assisted reproductive technologies, management of pregnancy complications, and laboratory automation. In addition, we discuss the need for robust ethical frameworks and regulations to ensure the responsible and equitable use of AI in reproductive medicine.

Unlocking therapeutic frontiers: harnessing artificial intelligence in drug discovery for neurodegenerative diseases

Neurodegenerative diseases (NDs) pose serious healthcare challenges with limited therapeutic treatments and high social burdens. The integration of artificial intelligence (AI) into drug discovery has emerged as a promising approach to address these challenges. This review explores the application of AI techniques to unravel therapeutic frontiers for NDs. We examine the current landscape of AI-driven drug discovery and discuss the potentials of AI in accelerating the identification of novel therapeutic targets on ND research and drug development, optimization of drug candidates, and expediating personalized medicine approaches. Finally, we outline future directions and challenges in harnessing AI for the advancement of therapeutics in this critical area by emphasizing the importance of interdisciplinary collaboration and ethical considerations.

Outcomes of different transfer strategies for in vitro fertilization/intracytoplasmic sperm injection with poor-quality embryos-Analysis of embryonic development, perinatal period, and neonatal outcomes

During the in vitro fertilization and embryo transfer process, some expectant mothers may not have good embryos to choose from before the embryo transfer. Recommendations for this condition are currently unclear, and relevant clinical and neonatal outcomes are still lacking. This study analyzed the outcomes of poor-quality embryo transfers, including fetal outcomes, in the fresh cycle and frozen-thawed embryo transfer cycle. Embryos were also analyzed for abnormalities during the cleavage stage. The results indicate that in the absence of good embryos, clinicians and embryologists could advise expectant mothers to continue culturing the embryos to the blastocyst stage and undergo transfer if blastocysts are formed. This finding can also be used as a reference for many expectant mothers with frozen embryos that have not yet been thawed.

Cytoplasmic strings in human blastocysts: hypotheses of their role and implications for embryo selection

STUDY QUESTION

What are the implications of the presence cytoplasmic strings (Cyt-S) and their quantity and dynamics for the pre-implantation development of human blastocysts?

SUMMARY ANSWER

Cyt-S are common in human embryos and are associated with faster blastocyst development, larger expansion, and better morphological quality.

WHAT IS KNOWN ALREADY

Cyt-S are dynamic cellular projections connecting inner cell mass and trophectoderm (TE) cells, that can be observed during blastocyst expansion. Their prevalence in human embryos has been estimated to be between 44% and 93%. Data relevant to their clinical implications and role in development are lacking, limited, or controversial.

STUDY DESIGN, SIZE, DURATION

Retrospective study conducted at a single IVF center between May 2013 and November 2014 and involving 124 pre-implantation genetic testing for aneuploidy cycles in a time-lapse incubator with ≥1 blastocyst biopsied and vitrified (N = 370 embryos assessed). These cycles resulted in 87 vitrified-warmed single-euploid blastocyst transfers.

PARTICIPANTS/MATERIALS, SETTING, METHODS

ICSI, continuous blastocyst culture (Days 5-7), TE biopsy of fully expanded blastocysts without Day 3 zona pellucida drilling, qPCR to assess uniform full-chromosome aneuploidies, and vitrification were all performed. Only vitrified-warmed euploid single-embryo-transfers were conducted. Blastocyst morphological quality was defined according to Gardner's criteria. The AI-based software CHLOE™ (Fairtility) automatically registered timings from time of starting blastulation (tSB) to biopsy (t-biopsy, i.e. blastocyst full-expansion) as hours-post-insemination (hpi), embryo area (including zona pellucida in µm2), and spontaneous blastocyst collapses. One senior embryologist manually annotated Cyt-S presence, quantity, timings, and type (thick cell-to-cell connections and/or threads). All significant associations were confirmed through regression analyses. All couples', cycles', and embryos' main features were also tested for associations with Cyt-S presence, quantity, and dynamics.

MAIN RESULTS AND THE ROLE OF CHANCE

About 94.3% of the patients (N = 117/124) had ≥1 embryo with Cyt-S. Out of a total of 370 blastocysts, 55 degenerated between blastulation and full-expansion (N = 55/370, 14.9%). The degeneration rate among embryos with ≥1 Cyt-S was 10.8% (N = 33/304), significantly lower than that of embryos without Cyt-S (33.3%, N = 22/66, P < 0.01). Of the remaining 315 viable blastocysts analyzed, 86% (N = 271/315; P < 0.01) had ≥1 Cyt-S, on average 3.5 ± 2.1 per embryo ranging 1-13. The first Cyt-S per viable embryo appeared at 115.3 ± 12.5 hpi (85.7-157.7), corresponding to 10.5 ± 5.8 h (0.5-31) after tSB. Overall, we analyzed 937 Cyt-S showing a mean duration of 3.8 ± 2.7 h (0.3-20.9). Cyt-S were mostly threads (N = 508/937, 54.2%) or thick cell-to-cell connections becoming threads (N = 382/937, 40.8%) than thick bridges (N = 47/937, 5.0%). The presence and quantity of Cyt-S were significantly associated with developmentally faster (on average 6-12 h faster) and more expanded (on average 2700 µm2-larger blastocyst's area at t-biopsy) embryos. Also, the presence and duration of Cyt-S were associated with better morphology. Lastly, while euploidy rates were comparable between blastocysts with and without Cyt-S, all euploid blastocysts transferred from the latter group failed to implant (N = 10).

LIMITATIONS, REASONS FOR CAUTION

Cyt-S presence and dynamics were assessed manually on seven focal planes from video frames recorded every 15 min. The patients included were mostly of advanced maternal age. Only associations could be reported, but no causations/consequences. Lastly, larger datasets are required to better assess Cyt-S associations with clinical outcomes.

WIDER IMPLICATIONS OF THE FINDINGS

Cyt-S are common during human blastocyst expansion, suggesting their physiological implication in this process. Their presence, quantity and dynamics mirror embryo viability, and morphological quality, yet their role is still unknown. Future basic science studies are encouraged to finally describe Cyt-S molecular nature and biophysical properties, and Artificial Intelligence tools should aid these studies by incorporating Cyt-S assessment.

STUDY FUNDING/COMPETING INTEREST(S)

None.

TRIAL REGISTRATION NUMBER

N/A.

Artificial intelligence in andrology - fact or fiction: essential takeaway for busy clinicians

ABSTRACT

Artificial intelligence (AI) is revolutionizing the current approach to medicine. AI uses machine learning algorithms to predict the success of therapeutic procedures or assist the clinician in the decision-making process. To date, machine learning studies in the andrological field have mainly focused on prostate cancer imaging and management. However, an increasing number of studies are documenting the use of AI to assist clinicians in decision-making and patient management in andrological diseases such as varicocele or sexual dysfunction. Additionally, machine learning applications are being employed to enhance success rates in assisted reproductive techniques (ARTs). This article offers the clinicians as well as the researchers with a brief overview of the current use of AI in andrology, highlighting the current state-of-the-art scientific evidence, the direction in which the research is going, and the strengths and limitations of this approach.

Non-invasive prediction of human embryonic ploidy using artificial intelligence: a systematic review and meta-analysis

Background

Embryonic ploidy is critical for the success of embryo transfer. Currently, preimplantation genetic testing for aneuploidy (PGT-A) is the gold standard for detecting ploidy abnormalities. However, PGT-A has several inherent limitations, including invasive biopsy, high economic burden, and ethical constraints. This paper provides the first comprehensive systematic review and meta-analysis of the performance of artificial intelligence (AI) algorithms using embryonic images for non-invasive prediction of embryonic ploidy.

Methods

Comprehensive searches of studies that developed or utilized AI algorithms to predict embryonic ploidy from embryonic imaging, published up until August 10, 2024, across PubMed, MEDLINE, Embase, IEEE, SCOPUS, Web of Science, and the Cochrane Central Register of Controlled Trials were performed. Studies with prospective or retrospective designs were included without language restrictions. The summary receiver operating characteristic curve, along with pooled sensitivity and specificity, was estimated using a bivariate random-effects model. The risk of bias and study quality were evaluated using the QUADAS-AI tool. Heterogeneity was quantified using the inconsistency index (I 2 ), derived from Cochran's Q test. Predefined subgroup analyses and bivariate meta-regression were conducted to explore potential sources of heterogeneity. This study was registered with PROSPERO (CRD42024500409).

Findings

Twenty eligible studies were identified, with twelve studies included in the meta-analysis. The pooled sensitivity, specificity, and area under the curve of AI for predicting embryonic euploidy were 0.71 (95% CI: 0.59-0.81), 0.75 (95% CI: 0.69-0.80), and 0.80 (95% CI: 0.76-0.83), respectively, based on a total of 6879 embryos (3110 euploid and 3769 aneuploid). Meta-regression and subgroup analyses identified the type of AI-driven decision support system, external validation, risk of bias, and year of publication as the primary contributors to the observed heterogeneity. There was no evidence of publication bias.

Interpretation

Our findings indicate that AI algorithms exhibit promising performance in predicting embryonic euploidy based on embryonic imaging. Although the current AI models developed cannot entirely replace invasive methods for determining embryo ploidy, AI demonstrates promise as an auxiliary decision-making tool for embryo selection, particularly for individuals who are unable to undergo PGT-A. To enhance the quality of future research, it is essential to overcome the specific challenges and limitations associated with AI studies in reproductive medicine.

Funding

This work was supported by the National Key R&D Program of China (2022YFC2702905), the Shengjing Freelance Researcher Plan of Shengjing Hospital and the 345 talent project of Shengjing Hospital.

Recent advancements to increase success in assisted reproductive technologies in cattle

Assisted reproductive technologies (ART) are fundamental for cattle breeding and sustainable food production. Together with genomic selection, these technologies contribute to reducing the generation interval and accelerating genetic progress. In this paper, we discuss advancements in technologies used in the fertility evaluation of breeding animals, and the collection, processing, and preservation of the gametes. It is of utmost importance for the breeding industry to select dams and sires of the next generation as young as possible, as is the efficient and timely collection of gametes. There is a need for reliable and easily applicable methods to evaluate sexual maturity and fertility. Although gametes processing and preservation have been improved in recent decades, challenges are still encountered. The targeted use of sexed semen and beef semen has obliterated the production of surplus replacement heifers and bull calves from dairy breeds, markedly improving animal welfare and ethical considerations in production practices. Parallel with new technologies, many well-established technologies remain relevant, although with evolving applications. In vitro production (IVP) has become the predominant method of embryo production. Although fundamental improvements in IVP procedures have been established, the quality of IVP embryos remains inferior to their in vivo counterparts. Improvements to facilitate oocyte maturation and development of new culture systems, e.g. microfluidics, are presented in this paper. New non-invasive and objective tools are needed to select embryos for transfer. Cryopreservation of semen and embryos plays a pivotal role in the distribution of genetics, and we discuss the challenges and opportunities in this field. Finally, machine learning (ML) is gaining ground in agriculture and ART. This paper delves into the utilization of emerging technologies in ART, along with the current status, key challenges, and future prospects of ML in both research and practical applications within ART.

Use of artificial intelligence embryo selection based on static images to predict first-trimester pregnancy loss

RESEARCH QUESTION

Can an artificial intelligence embryo selection assistant predict the incidence of first-trimester spontaneous abortion using static images of IVF embryos?

DESIGN

In a blind, retrospective study, a cohort of 172 blastocysts from IVF cases with single embryo transfer and a positive biochemical pregnancy test was ranked retrospectively by the artificial intelligence morphometric algorithm ERICA. Making use of static embryo images from a light microscope, each blastocyst was assigned to one of four possible groups (optimal, good, fair or poor), and linear regression was used to correlate the results with the presence or absence of a normal fetal heart beat as an indicator of ongoing pregnancy or spontaneous abortion, respectively. Additional analyses included modelling for recipient age and chromosomal status established by preimplantation genetic testing for aneuploidy (PGT-A).

RESULTS

Embryos classified as optimal/good had a lower incidence of spontaneous abortion (16.1%) compared with embryos classified as fair/poor (25%; OR = 0.46, P = 0.005). The incidence of spontaneous abortion in chromosomally normal embryos (determined by PGT-A) was 13.3% for optimal/good embryos and 20.0% for fair/poor embryos, although the difference was not significant (P = 0.531). There was a significant association between embryo rank and recipient age (P = 0.018), in that the incidence of spontaneous abortion was unexpectedly lower in older recipients (21.3% for age ≤35 years, 17.9% for age 36-38 years, 16.4% for age ≥39 years; OR = 0.354, P = 0.0181). Overall, these results support correlation between risk of spontaneous abortion and embryo rank as determined by artificial intelligence; classification accuracy was calculated to be 67.4%.

CONCLUSIONS

This preliminary study suggests that artificial intelligence (ERICA), which was designed as a ranking system to assist with embryo transfer decisions and ploidy prediction, may also be useful to provide information for couples on the risk of spontaneous abortion. Future work will include a larger sample size and karyotyping of miscarried pregnancy tissue.

Harnessing Artificial Intelligence to Predict Ovarian Stimulation Outcomes in In Vitro Fertilization: Scoping Review

BACKGROUND

In the realm of in vitro fertilization (IVF), artificial intelligence (AI) models serve as invaluable tools for clinicians, offering predictive insights into ovarian stimulation outcomes. Predicting and understanding a patient's response to ovarian stimulation can help in personalizing doses of drugs, preventing adverse outcomes (eg, hyperstimulation), and improving the likelihood of successful fertilization and pregnancy. Given the pivotal role of accurate predictions in IVF procedures, it becomes important to investigate the landscape of AI models that are being used to predict the outcomes of ovarian stimulation.

OBJECTIVE

The objective of this review is to comprehensively examine the literature to explore the characteristics of AI models used for predicting ovarian stimulation outcomes in the context of IVF.

METHODS

A total of 6 electronic databases were searched for peer-reviewed literature published before August 2023, using the concepts of IVF and AI, along with their related terms. Records were independently screened by 2 reviewers against the eligibility criteria. The extracted data were then consolidated and presented through narrative synthesis.

RESULTS

Upon reviewing 1348 articles, 30 met the predetermined inclusion criteria. The literature primarily focused on the number of oocytes retrieved as the main predicted outcome. Microscopy images stood out as the primary ground truth reference. The reviewed studies also highlighted that the most frequently adopted stimulation protocol was the gonadotropin-releasing hormone (GnRH) antagonist. In terms of using trigger medication, human chorionic gonadotropin (hCG) was the most commonly selected option. Among the machine learning techniques, the favored choice was the support vector machine. As for the validation of AI algorithms, the hold-out cross-validation method was the most prevalent. The area under the curve was highlighted as the primary evaluation metric. The literature exhibited a wide variation in the number of features used for AI algorithm development, ranging from 2 to 28,054 features. Data were mostly sourced from patient demographics, followed by laboratory data, specifically hormonal levels. Notably, the vast majority of studies were restricted to a single infertility clinic and exclusively relied on nonpublic data sets.

CONCLUSIONS

These insights highlight an urgent need to diversify data sources and explore varied AI techniques for improved prediction accuracy and generalizability of AI models for the prediction of ovarian stimulation outcomes. Future research should prioritize multiclinic collaborations and consider leveraging public data sets, aiming for more precise AI-driven predictions that ultimately boost patient care and IVF success rates.

Male Infertility: New Developments, Current Challenges, and Future Directions

There have been many significant scientific advances in the diagnostics and treatment modalities in the field of male infertility in recent decades. Examples of these include assisted reproductive technologies, sperm selection techniques for intracytoplasmic sperm injection, surgical procedures for sperm retrieval, and novel tests of sperm function. However, there is certainly a need for new developments in this field. In this review, we discuss advances in the management of male infertility, such as seminal oxidative stress testing, sperm DNA fragmentation testing, genetic and epigenetic tests, genetic manipulations, artificial intelligence, personalized medicine, and telemedicine. The role of the reproductive urologist will continue to expand in future years to address different topzics related to diverse questions and controversies of pathophysiology, diagnosis, and therapy of male infertility, training researchers and physicians in medical and scientific research in reproductive urology/andrology, and further development of andrology as an independent specialty.

How should the best human embryo in vitro be? Current and future challenges for embryo selection

In-vitro fertilization (IVF) aims at overcoming the causes of infertility and lead to a healthy live birth. To maximize IVF efficiency, it is critical to identify and transfer the most competent embryo within a cohort produced by a couple during a cycle. Conventional static embryo morphological assessment involves sequential observations under a light microscope at specific timepoints. The introduction of time-lapse technology enhanced morphological evaluation via the continuous monitoring of embryo preimplantation in vitro development, thereby unveiling features otherwise undetectable via multiple static assessments. Although an association exists, blastocyst morphology poorly predicts chromosomal competence. In fact, the only reliable approach currently available to diagnose the embryonic karyotype is trophectoderm biopsy and comprehensive chromosome testing to assess non-mosaic aneuploidies, namely preimplantation genetic testing for aneuploidies (PGT-A). Lately, the focus is shifting towards the fine-tuning of non-invasive technologies, such as "omic" analyses of waste products of IVF (e.g., spent culture media) and/or artificial intelligence-powered morphologic/morphodynamic evaluations. This review summarizes the main tools currently available to assess (or predict) embryo developmental, chromosomal, and reproductive competence, their strengths, the limitations, and the most probable future challenges.

The prospect of artificial intelligence to personalize assisted reproductive technology

Infertility affects 1-in-6 couples, with repeated intensive cycles of assisted reproductive technology (ART) required by many to achieve a desired live birth. In ART, typically, clinicians and laboratory staff consider patient characteristics, previous treatment responses, and ongoing monitoring to determine treatment decisions. However, the reproducibility, weighting, and interpretation of these characteristics are contentious, and highly operator-dependent, resulting in considerable reliance on clinical experience. Artificial intelligence (AI) is ideally suited to handle, process, and analyze large, dynamic, temporal datasets with multiple intermediary outcomes that are generated during an ART cycle. Here, we review how AI has demonstrated potential for optimization and personalization of key steps in a reproducible manner, including: drug selection and dosing, cycle monitoring, induction of oocyte maturation, and selection of the most competent gametes and embryos, to improve the overall efficacy and safety of ART.

A brief history of artificial intelligence embryo selection: from black-box to glass-box

With the exponential growth of computing power and accumulation of embryo image data in recent years, artificial intelligence (AI) is starting to be utilized in embryo selection in IVF. Amongst different AI technologies, machine learning (ML) has the potential to reduce operator-related subjectivity in embryo selection while saving labor time on this task. However, as modern deep learning (DL) techniques, a subcategory of ML, are increasingly used, its integrated black-box attracts growing concern owing to the well-recognized issues regarding lack of interpretability. Currently, there is a lack of randomized controlled trials to confirm the effectiveness of such black-box models. Recently, emerging evidence has shown underperformance of black-box models compared to the more interpretable traditional ML models in embryo selection. Meanwhile, glass-box AI, such as interpretable ML, is being increasingly promoted across a wide range of fields and is supported by its ethical advantages and technical feasibility. In this review, we propose a novel classification system for traditional and AI-driven systems from an embryology standpoint, defining different morphology-based selection approaches with an emphasis on subjectivity, explainability, and interpretability.

Potential for artificial intelligence in medicine and its application to male infertility

Background

The third AI boom, which began in 2010, has been characterized by the rapid evolution and diversification of AI and marked by the development of key technologies such as machine learning and deep learning. AI is revolutionizing the medical field, enhancing diagnostic accuracy, surgical outcomes, and drug production.

Methods

This review includes explanations of digital transformation (DX), the history of AI, the difference between machine learning and deep learning, recent AI topics, medical AI, and AI research in male infertility.

Main Findings Results

In research on male infertility, I established an AI-based prediction model for Johnsen scores and an AI predictive model for sperm retrieval in non-obstructive azoospermia, both by no-code AI.

Conclusions

AI is making constant progress. It would be ideal for physicians to acquire a knowledge of AI and even create AI models. No-code AI tools have revolutionized model creation, allowing individuals to independently handle data preparation and model development. Previously a team effort, this shift empowers users to craft customized AI models solo, offering greater flexibility and control in the model creation process.

Artificial Intelligence in Andrology: From Semen Analysis to Image Diagnostics

Artificial intelligence (AI) in medicine has gained a lot of momentum in the last decades and has been applied to various fields of medicine. Advances in computer science, medical informatics, robotics, and the need for personalized medicine have facilitated the role of AI in modern healthcare. Similarly, as in other fields, AI applications, such as machine learning, artificial neural networks, and deep learning, have shown great potential in andrology and reproductive medicine. AI-based tools are poised to become valuable assets with abilities to support and aid in diagnosing and treating male infertility, and in improving the accuracy of patient care. These automated, AI-based predictions may offer consistency and efficiency in terms of time and cost in infertility research and clinical management. In andrology and reproductive medicine, AI has been used for objective sperm, oocyte, and embryo selection, prediction of surgical outcomes, cost-effective assessment, development of robotic surgery, and clinical decision-making systems. In the future, better integration and implementation of AI into medicine will undoubtedly lead to pioneering evidence-based breakthroughs and the reshaping of andrology and reproductive medicine.

The Role of Artificial Intelligence and Machine Learning in Assisted Reproductive Technologies

Artificial intelligence (AI) and machine learning, the form most commonly used in medicine, offer powerful tools utilizing the strengths of large data sets and intelligent algorithms. These systems can help to revolutionize delivery of treatments, access to medical care, and improvement of outcomes, particularly in the realm of reproductive medicine. Whether that is more robust oocyte and embryo grading or more accurate follicular measurement, AI will be able to aid clinicians, and most importantly patients, in providing the best possible and individualized care. However, despite all of the potential strengths of AI, algorithms are not immune to bias and are vulnerable to the many socioeconomic and demographic biases that current healthcare systems suffer from. Wrong diagnoses as well is furthering of healthcare discrimination are real possibilities if both the capabilities and limitations of AI are not well understood. Armed with appropriate knowledge of how AI can most appropriately operate within medicine, and specifically reproductive medicine, will enable clinicians to both create and utilize machine learning-based innovations for the furthering of reproductive medicine and ultimately achieving the goal of building of healthy families.

Only the Best of the Bunch-Sperm Preparation Is Not Just about Numbers

In this Seminar, we present an overview of the current and emerging methods and technologies for optimizing the man and the sperm sample for fertility treatment. We argue that sperms are the secret to success, and that there are many avenues for improving both treatment and basic understanding of their role in outcomes. These outcomes encompass not just whether treatment is successful or not, but the wider intergenerational health of the offspring. We discuss outstanding challenges and opportunities of new technologies such as microfluidics and artificial intelligence, including potential pitfalls and advantages. This article aims to provide a comprehensive overview of the importance of sperm in fertility treatment and suggests future directions for research and innovation.

Deep learning for embryo evaluation using time-lapse: a systematic review of diagnostic test accuracy

OBJECTIVE

This study aimed to investigate the accuracy of convolutional neural network models in the assessment of embryos using time-lapse monitoring.

DATA SOURCES

A systematic search was conducted in PubMed and Web of Science databases from January 2016 to December 2022. The search strategy was carried out by using key words and MeSH (Medical Subject Headings) terms.

STUDY ELIGIBILITY CRITERIA

Studies were included if they reported the accuracy of convolutional neural network models for embryo evaluation using time-lapse monitoring. The review was registered with PROSPERO (International Prospective Register of Systematic Reviews; identification number CRD42021275916).

METHODS

Two reviewer authors independently screened results using the Covidence systematic review software. The full-text articles were reviewed when studies met the inclusion criteria or in any uncertainty. Nonconsensus was resolved by a third reviewer. Risk of bias and applicability were evaluated using the QUADAS-2 tool and the modified Joanna Briggs Institute or JBI checklist.

RESULTS

Following a systematic search of the literature, 22 studies were identified as eligible for inclusion. All studies were retrospective. A total of 522,516 images of 222,998 embryos were analyzed. Three main outcomes were evaluated: successful in vitro fertilization, blastocyst stage classification, and blastocyst quality. Most studies reported >80% accuracy, and embryologists were outperformed in some. Ten studies had a high risk of bias, mostly because of patient bias.

CONCLUSION

The application of artificial intelligence in time-lapse monitoring has the potential to provide more efficient, accurate, and objective embryo evaluation. Models that examined blastocyst stage classification showed the best predictions. Models that predicted live birth had a low risk of bias, used the largest databases, and had external validation, which heightens their relevance to clinical application. Our systematic review is limited by the high heterogeneity among the included studies. Researchers should share databases and standardize reporting.

Discard or not discard, that is the question: an international survey across 117 embryologists on the clinical management of borderline quality blastocysts

STUDY QUESTION

Do embryologists from different European countries agree on embryo disposition decisions ('use' or 'discard') about Day 7 (>144 h post-insemination) and/or low-quality blastocysts (LQB;

SUMMARY ANSWER

The prevalence of 'discard' answers was 38.7%; nevertheless, embryologists' agreement was overall just fair (Fleiss-k = 0.26).

WHAT IS KNOWN ALREADY

The utilization of LQBs and adoption of culture beyond 144 h post-insemination is increasing worldwide. Although morphology and morphokinetics are associated with embryo developmental competence, previous studies demonstrated significant interobserver variability among embryologists regarding embryo quality assessment and disposition decisions for borderline quality blastocysts.

STUDY DESIGN, SIZE, DURATION

An anonymous survey was run in a large network of IVF centers. A total of 117 embryologists from 6 European countries and 29 IVF centers filled in the survey. Randomly selected anonymous time-lapse videos of 50 Day 7 and/or LQB whole embryo preimplantation development were assessed by the embryologists. The key information on patients/cycles was provided along with each video. All cycles entailed preimplantation genetic testing for aneuploidies. Each embryologist specified whether he/she would have discarded or used ('transfer-fresh'/'cryopreserve'/'biopsy') any embryo. Inter-rater agreement was measured with Fleiss-k.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Examiners were asked about their years of experience, center location, average number of cycles and average maternal age, number of colleagues, and use of time-lapse incubators at their centers. All participants were blinded to artificial intelligence (AI) scores generated by two commercially available software packages, chromosomal diagnosis (all blastocysts were tested for aneuploidies), and clinical outcomes after vitrified-warmed euploid single blastocyst transfer. These data were known only by one embryologist not involved in the survey.

MAIN RESULTS AND THE ROLE OF CHANCE

Participants were Italian (40%, N = 47), Spanish (24%, N = 28), Portuguese (5%, N = 6), Czech (5%, N = 6), Swedish (23%, N = 27), and Icelandic (3%, N = 3). In total, 2263 (38.7%) 'discard' and 3587 (61.3%) 'use' decisions were recorded. Czech, Portuguese, and Italian embryologists expressed lower 'discard' decision rates (mean ± SD 17 ± 7%, range 8-24%; 23 ± 14% range 4-46%; and 27 ± 18% range 2-72%, respectively), while Spanish gave intermediate (37 ± 16% range 4-66%) and Nordic gave higher (67 ± 11% range 40-90%) rates. The prevalence of 'discard' answers was 38.7% out of 5850 choices (mean per embryologist: 39 ± 23% range 2-90%). Only embryologists' country and IVF group were associated with this rate. Overall agreement among embryologists was fair (Fleiss-k = 0.26). The prevalence of 'discard' responses per embryo was 37 ± 24% (range 2-87%). Only the number of sibling blastocysts influenced this rate (i.e. the larger the cohort, the higher the inclination to 'discard'). No difference was shown for the two scores between euploid and aneuploid borderline quality blastocysts, while the embryologists were, by chance, more prone to 'discard' the latter (28.3 ± 21% range 9-71% versus 41.6 ± 24.8% range 2-87%, respectively).

LIMITATIONS, REASONS FOR CAUTION

The survey included only private IVF clinics located in Europe. Moreover, a key variable is missing, namely patients' access to care. Indeed, all embryologists involved in the survey were part of the same network of private IVF clinics, while the embryo disposition decisions might be different in a public setting.

WIDER IMPLICATIONS OF THE FINDINGS

Decision-making by European embryologists regarding Day 7 embryos or LQBs is inconsistent with putative clinical consequences, especially in patients with low prognosis. Although the embryologists could make decisions independent from their local regulations, their mindset and clinical background influenced their choices. In the future, AI tools should be trained to assess borderline quality embryos and empowered with cost-effectiveness information to support embryologists' decisions with more objective assessments.

STUDY FUNDING/COMPETING INTEREST(S)

No external funding was obtained for this study. The authors have no conflict of interest to declare.

TRIAL REGISTRATION NUMBER

N/A.

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Key Words

• Day 7 blastocysts
• artificial intelligence
• decision-making
• embryo competence
• low-quality blastocysts

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MESH Headings

• Female
• Humans
• Aneuploidy
• Artificial Intelligence
• Blastocyst/physiology
• Embryo Transfer/methods
• Maternal Age
• Retrospective Studies
• Male

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Affiliation(s)

Viviana Chiappetta IVIRMA Global Research Alliance, GENERA, Clinica Valle Giulia, Rome, Italy
Federica Innocenti IVIRMA Global Research Alliance, GENERA, Clinica Valle Giulia, Rome, Italy
Giovanni Coticchio IVIRMA Global Research Alliance, 9.baby, Bologna, Italy
Aisling Ahlström IVIRMA Global Research Alliance, LIVIO, Göteborg, Sweden
Laura Albricci IVIRMA Global Research Alliance, GENERA, Clinica Valle Giulia, Rome, Italy
Vicente Badajoz IVIRMA Global Research Alliance, GINEFIV, Madrid, Spain
Maria Hebles IVIRMA Global Research Alliance, GINEMED, Sevilla, Spain
Miguel Gallardo IVIRMA Global Research Alliance, GINEMED, Lisbon, Portugal
Francesca Benini IVIRMA Global Research Alliance, DEMETRA, Florence, Italy
Stefano Canosa IVIRMA Global Research Alliance, LIVET, Turin, Italy
Jiří Kumpošt IVIRMA Global Research Alliance, FERTICARE, Prague, Czech Republic
Katarina Milton IVIRMA Global Research Alliance, CARL VON LINNÈ KLINIKEN, Uppsala, Sweden
Diletta Montanino Oliva Department of Biology and Biotechnology "Lazzaro Spallanzani", University of Pavia, Pavia, Italy
Roberta Maggiulli IVIRMA Global Research Alliance, GENERA, Clinica Valle Giulia, Rome, Italy
Laura Rienzi IVIRMA Global Research Alliance, GENERA, Clinica Valle Giulia, Rome, Italy Department of Biomolecular Sciences, University of Urbino "Carlo Bo", Urbino, Italy
Danilo Cimadomo IVIRMA Global Research Alliance, GENERA, Clinica Valle Giulia, Rome, Italy

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External validation of models for predicting cumulative live birth over multiple complete cycles of IVF treatment

STUDY QUESTION

Can two prediction models developed using data from 1999 to 2009 accurately predict the cumulative probability of live birth per woman over multiple complete cycles of IVF in an updated UK cohort?

SUMMARY ANSWER

After being updated, the models were able to estimate individualized chances of cumulative live birth over multiple complete cycles of IVF with greater accuracy.

WHAT IS KNOWN ALREADY

The McLernon models were the first to predict cumulative live birth over multiple complete cycles of IVF. They were converted into an online calculator called OPIS (Outcome Prediction In Subfertility) which has 3000 users per month on average. A previous study externally validated the McLernon models using a Dutch prospective cohort containing data from 2011 to 2014. With changes in IVF practice over time, it is important that the McLernon models are externally validated on a more recent cohort of patients to ensure that predictions remain accurate.

STUDY DESIGN, SIZE, DURATION

A population-based cohort of 91 035 women undergoing IVF in the UK between January 2010 and December 2016 was used for external validation. Data on frozen embryo transfers associated with these complete IVF cycles conducted from 1 January 2017 to 31 December 2017 were also collected.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Data on IVF treatments were obtained from the Human Fertilisation and Embryology Authority (HFEA). The predictive performances of the McLernon models were evaluated in terms of discrimination and calibration. Discrimination was assessed using the c-statistic and calibration was assessed using calibration-in-the-large, calibration slope, and calibration plots. Where any model demonstrated poor calibration in the validation cohort, the models were updated using intercept recalibration, logistic recalibration, or model revision to improve model performance.

MAIN RESULTS AND THE ROLE OF CHANCE

Following exclusions, 91 035 women who underwent 144 734 complete cycles were included. The validation cohort had a similar distribution age profile to women in the development cohort. Live birth rates over all complete cycles of IVF per woman were higher in the validation cohort. After calibration assessment, both models required updating. The coefficients of the pre-treatment model were revised, and the updated model showed reasonable discrimination (c-statistic: 0.67, 95% CI: 0.66 to 0.68). After logistic recalibration, the post-treatment model showed good discrimination (c-statistic: 0.75, 95% CI: 0.74 to 0.76). As an example, in the updated pre-treatment model, a 32-year-old woman with 2 years of primary infertility has a 42% chance of having a live birth in the first complete ICSI cycle and a 77% chance over three complete cycles. In a couple with 2 years of primary male factor infertility where a 30-year-old woman has 15 oocytes collected in the first cycle, a single fresh blastocyst embryo transferred in the first cycle and spare embryos cryopreserved, the estimated chance of live birth provided by the post-treatment model is 46% in the first complete ICSI cycle and 81% over three complete cycles.

LIMITATIONS, REASONS FOR CAUTION

Two predictors from the original models, duration of infertility and previous pregnancy, which were not available in the recent HFEA dataset, were imputed using data from the older cohort used to develop the models. The HFEA dataset does not contain some other potentially important predictors, e.g. BMI, ethnicity, race, smoking and alcohol intake in women, as well as measures of ovarian reserve such as antral follicle count.

WIDER IMPLICATIONS OF THE FINDINGS

Both updated models show improved predictive ability and provide estimates which are more reflective of current practice and patient case mix. The updated OPIS tool can be used by clinicians to help shape couples' expectations by informing them of their individualized chances of live birth over a sequence of multiple complete cycles of IVF.

STUDY FUNDING/COMPETING INTEREST(S)

This study was supported by an Elphinstone scholarship scheme at the University of Aberdeen and Aberdeen Fertility Centre, University of Aberdeen. S.B. has a commitment of research funding from Merck. D.J.M. and M.B.R. declare support for the present manuscript from Elphinstone scholarship scheme at the University of Aberdeen and Assisted Reproduction Unit at Aberdeen Fertility Centre, University of Aberdeen. D.J.M. declares grants received by University of Aberdeen from NHS Grampian, The Meikle Foundation, and Chief Scientist Office in the past 3 years. D.J.M. declares receiving an honorarium for lectures from Merck. D.J.M. is Associate Editor of Human Reproduction Open and Statistical Advisor for Reproductive BioMed Online. S.B. declares royalties from Cambridge University Press for a book. S.B. declares receiving an honorarium for lectures from Merck, Organon, Ferring, Obstetric and Gynaecological Society of Singapore, and Taiwanese Society for Reproductive Medicine. S.B. has received support from Merck, ESHRE, and Ferring for attending meetings as speaker and is on the METAFOR and CAPRE Trials Data Monitoring Committee.

TRIAL REGISTRATION NUMBER

N/A.

Improving outcomes of assisted reproductive technologies using artificial intelligence for sperm selection

Within the field of assisted reproductive technology, artificial intelligence has become an attractive tool for potentially improving success rates. Recently, artificial intelligence-based tools for sperm evaluation and selection during intracytoplasmic sperm injection (ICSI) have been explored, mainly to improve fertilization outcomes and decrease variability within ICSI procedures. Although significant advances have been achieved in developing algorithms that track and rank single sperm in real-time during ICSI, the clinical benefits these might have in improving pregnancy rates from a single assisted reproductive technology cycle remain to be established.

Application of artificial intelligence in gametes and embryos selection

Gamete and embryo quality are critical to the success rate of Assisted Reproductive Technology (ART) cycles, but there remains a lack of methods to accurately measure the quality of sperm, oocytes and embryos. The ability of Artificial Intelligence (AI) technology to analyze large amounts of data, especially video and images, is particularly useful in gamete and embryo assessment and selection. The well-trained model has fast calculation speed and high accuracy, which can help embryologists to perform more objective gamete and embryo selection. Various artificial intelligence models have been developed for gamete and embryo assessment, some of which exhibit good performance. In this review, we summarize the latest applications of AI technology in semen analysis, as well as selection for sperm, oocyte and embryo, and discuss the existing problems and development directions of artificial intelligence in this field.

Sperm motility assessed by deep convolutional neural networks into WHO categories

Semen analysis is central in infertility investigation. Manual assessment of sperm motility according to the WHO recommendations is the golden standard, and extensive training is a requirement for accurate and reproducible results. Deep convolutional neural networks (DCNN) are especially suitable for image classification. In this study, we evaluated the performance of the DCNN ResNet-50 in predicting the proportion of sperm in the WHO motility categories. Two models were evaluated using tenfold cross-validation with 65 video recordings of wet semen preparations from an external quality assessment programme for semen analysis. The corresponding manually assessed data was obtained from several of the reference laboratories, and the mean values were used for training of the DCNN models. One model was trained to predict the three categories progressive motility, non-progressive motility, and immotile spermatozoa. Another model was used in predicting four categories, where progressive motility was differentiated into rapid and slow. The resulting average mean absolute error (MAE) was 0.05 and 0.07, and the average ZeroR baseline was 0.09 and 0.10 for the three-category and the four-category model, respectively. Manual and DCNN-predicted motility was compared by Pearson's correlation coefficient and by difference plots. The strongest correlation between the mean manually assessed values and DCNN-predicted motility was observed for % progressively motile spermatozoa (Pearson's r = 0.88, p < 0.001) and % immotile spermatozoa (r = 0.89, p < 0.001). For rapid progressive motility, the correlation was moderate (Pearson's r = 0.673, p < 0.001). The median difference between manual and predicted progressive motility was 0 and 2 for immotile spermatozoa. The largest bias was observed at high and low percentages of progressive and immotile spermatozoa. The DCNN-predicted value was within the range of the interlaboratory variation of the results for most of the samples. In conclusion, DCNN models were able to predict the proportion of spermatozoa into the WHO motility categories with significantly lower error than the baseline. The best correlation between the manual and the DCNN-predicted motility values was found for the categories progressive and immotile. Of note, there was considerable variation between the mean motility values obtained for each category by the reference laboratories, especially for rapid progressive motility, which impacts the training of the DCNN models.

ESHRE good practice recommendations on recurrent implantation failure

STUDY QUESTION

How should recurrent implantation failure (RIF) in patients undergoing ART be defined and managed?

SUMMARY ANSWER

This is the first ESHRE good practice recommendations paper providing a definition for RIF together with recommendations on how to investigate causes and contributing factors, and how to improve the chances of a pregnancy.

WHAT IS KNOWN ALREADY

RIF is a challenge in the ART clinic, with a multitude of investigations and interventions offered and applied in clinical practice, often without biological rationale or with unequivocal evidence of benefit.

STUDY DESIGN SIZE DURATION

This document was developed according to a predefined methodology for ESHRE good practice recommendations. Recommendations are supported by data from the literature, if available, and the results of a previously published survey on clinical practice in RIF and the expertise of the working group. A literature search was performed in PubMed and Cochrane focussing on 'recurrent reproductive failure', 'recurrent implantation failure', and 'repeated implantation failure'.

PARTICIPANTS/MATERIALS SETTING METHODS

The ESHRE Working Group on Recurrent Implantation Failure included eight members representing the ESHRE Special Interest Groups for Implantation and Early Pregnancy, Reproductive Endocrinology, and Embryology, with an independent chair and an expert in statistics. The recommendations for clinical practice were formulated based on the expert opinion of the working group, while taking into consideration the published data and results of the survey on uptake in clinical practice. The draft document was then open to ESHRE members for online peer review and was revised in light of the comments received.

MAIN RESULTS AND THE ROLE OF CHANCE

The working group recommends considering RIF as a secondary phenomenon of ART, as it can only be observed in patients undergoing IVF, and that the following description of RIF be adopted: 'RIF describes the scenario in which the transfer of embryos considered to be viable has failed to result in a positive pregnancy test sufficiently often in a specific patient to warrant consideration of further investigations and/or interventions'. It was agreed that the recommended threshold for the cumulative predicted chance of implantation to identify RIF for the purposes of initiating further investigation is 60%. When a couple have not had a successful implantation by a certain number of embryo transfers and the cumulative predicted chance of implantation associated with that number is greater than 60%, then they should be counselled on further investigation and/or treatment options. This term defines clinical RIF for which further actions should be considered. Nineteen recommendations were formulated on investigations when RIF is suspected, and 13 on interventions. Recommendations were colour-coded based on whether the investigations/interventions were recommended (green), to be considered (orange), or not recommended, i.e. not to be offered routinely (red).

LIMITATIONS REASONS FOR CAUTION

While awaiting the results of further studies and trials, the ESHRE Working Group on Recurrent Implantation Failure recommends identifying RIF based on the chance of successful implantation for the individual patient or couple and to restrict investigations and treatments to those supported by a clear rationale and data indicating their likely benefit.

WIDER IMPLICATIONS OF THE FINDINGS

This article provides not only good practice advice but also highlights the investigations and interventions that need further research. This research, when well-conducted, will be key to making progress in the clinical management of RIF.

STUDY FUNDING/COMPETING INTERESTS

The meetings and technical support for this project were funded by ESHRE. N.M. declared consulting fees from ArtPRED (The Netherlands) and Freya Biosciences (Denmark); Honoraria for lectures from Gedeon Richter, Merck, Abbott, and IBSA; being co-founder of Verso Biosense. He is Co-Chief Editor of Reproductive Biomedicine Online (RBMO). D.C. declared being an Associate Editor of Human Reproduction Update, and declared honoraria for lectures from Merck, Organon, IBSA, and Fairtility; support for attending meetings from Cooper Surgical, Fujifilm Irvine Scientific. G.G. declared that he or his institution received financial or non-financial support for research, lectures, workshops, advisory roles, or travelling from Ferring, Merck, Gedeon-Richter, PregLem, Abbott, Vifor, Organon, MSD, Coopersurgical, ObsEVA, and ReprodWissen. He is an Editor of the journals Archives of Obstetrics and Gynecology and Reproductive Biomedicine Online, and Editor in Chief of Journal Gynäkologische Endokrinologie. He is involved in guideline developments and quality control on national and international level. G.L. declared he or his institution received honoraria for lectures from Merck, Ferring, Vianex/Organon, and MSD. He is an Associate Editor of Human Reproduction Update, immediate past Coordinator of Special Interest Group for Reproductive Endocrinology of ESHRE and has been involved in Guideline Development Groups of ESHRE and national fertility authorities. D.J.M. declared being an Associate Editor for Human Reproduction Open and statistical Advisor for Reproductive Biomedicine Online. B.T. declared being shareholder of Reprognostics and she or her institution received financial or non-financial support for research, clinical trials, lectures, workshops, advisory roles or travelling from support for attending meetings from Ferring, MSD, Exeltis, Merck Serono, Bayer, Teva, Theramex and Novartis, Astropharm, Ferring. The other authors had nothing to disclose.

DISCLAIMER

This Good Practice Recommendations (GPR) document represents the views of ESHRE, which are the result of consensus between the relevant ESHRE stakeholders and are based on the scientific evidence available at the time of preparation. ESHRE GPRs should be used for information and educational purposes. They should not be interpreted as setting a standard of care or be deemed inclusive of all proper methods of care, or be exclusive of other methods of care reasonably directed to obtaining the same results. They do not replace the need for application of clinical judgement to each individual presentation, or variations based on locality and facility type. Furthermore, ESHRE GPRs do not constitute or imply the endorsement, or favouring, of any of the included technologies by ESHRE.

VISEM-Tracking, a human spermatozoa tracking dataset

A manual assessment of sperm motility requires microscopy observation, which is challenging due to the fast-moving spermatozoa in the field of view. To obtain correct results, manual evaluation requires extensive training. Therefore, computer-aided sperm analysis (CASA) has become increasingly used in clinics. Despite this, more data is needed to train supervised machine learning approaches in order to improve accuracy and reliability in the assessment of sperm motility and kinematics. In this regard, we provide a dataset called VISEM-Tracking with 20 video recordings of 30 seconds (comprising 29,196 frames) of wet semen preparations with manually annotated bounding-box coordinates and a set of sperm characteristics analyzed by experts in the domain. In addition to the annotated data, we provide unlabeled video clips for easy-to-use access and analysis of the data via methods such as self- or unsupervised learning. As part of this paper, we present baseline sperm detection performances using the YOLOv5 deep learning (DL) model trained on the VISEM-Tracking dataset. As a result, we show that the dataset can be used to train complex DL models to analyze spermatozoa.

What happens to abnormally fertilized embryos? A scoping review

A dearth of evidence exists on embryos derived from oocytes without two pronuclei (2PN) or 'normal fertilization', i.e. embryos arising from non-pronuclear oocytes (0PN), mono-pronuclear oocytes (1PN) and tri-pronuclear oocytes (3PN). We searched the published literature on non-2PN oocytes and their clinical outcomes using a two-part collection strategy of relevant articles. A total of 33 articles were deemed eligible for the scoping review. A significant difference exists between potential development of oocytes with an abnormal number of pronuclei and those with 2PN in most studies; the abnormal pronuclei oocytes occur rarely and significant attrition occurs between day 1 and day 6, with corresponding reduction in chromosome integrity and clinical utility. Most recent studies describe outcomes of blastocysts derived from non-2PN oocytes, rather than cleavage stage embryo transfers. Compared with 2PN oocytes, blastocyst rates are lower in 1PN oocytes (68.3 versus 32.2%), with larger 1PN oocytes having better developmental potential compared with their smaller counterparts. Blastocysts from 1PN oocytes seem to have a slightly reduced implantation potential compared with those from 2PN blastocysts (33.3% versus 35.9%), with a reduced ongoing pregnancy rate (27.3% versus 28.1%). Live birth rates were only reported in 13 of the included studies. The comparators varied between studies, with live birth rates provided ranging from 0-66.7%, with two case reports (100%); this is a clear indication of the variability in practices and the significant heterogeneity of studies. A distinct lack of evidence exists on non-2PN oocytes; however, it seems that most abnormally fertilized oocytes that are non-viable will developmentally arrest in culture, and those that are viable can form viable pregnancies. Concerns remain about the outcome of pregnancies arising from the use of abnormally fertilized oocytes. Coupled with appropriate outcome measures, abnormally fertilized oocytes hold the potential to increase the pool of embryos eligible for transfer.

The Renaissance of Male Infertility Management in the Golden Age of Andrology

Infertility affects nearly 186 million people worldwide and the male partner is the cause in about half of the cases. Meta-regression data indicate an unexplained decline in sperm concentration and total sperm count over the last four decades, with an increasing prevalence of male infertility. This suggests an urgent need to implement further basic and clinical research in Andrology. Andrology developed as a branch of urology, gynecology, endocrinology, and, dermatology. The first scientific journal devoted to andrological sciences was founded in 1969. Since then, despite great advancements, andrology has encountered several obstacles in its growth. In fact, for cultural reasons, the male partner has often been neglected in the diagnostic and therapeutic workup of the infertile couple. Furthermore, the development of assisted reproductive techniques (ART) has driven a strong impression that this biotechnology can overcome all forms of infertility, with a common belief that having a spermatozoon from a male partner (a sort of sperm donor) is all that is needed to achieve pregnancy. However, clinical practice has shown that the quality of the male gamete is important for a successful ART outcome. Furthermore, the safety of ART has been questioned because of the high prevalence of comorbidities in the offspring of ART conceptions compared to spontaneous conceptions. These issues have paved the way for more research and a greater understanding of the mechanisms of spermatogenesis and male infertility. Consequently, numerous discoveries have been made in the field of andrology, ranging from genetics to several "omics" technologies, oxidative stress and sperm DNA fragmentation, the sixth edition of the WHO manual, artificial intelligence, management of azoospermia, fertility in cancers survivors, artificial testis, 3D printing, gene engineering, stem cells therapy for spermatogenesis, and reconstructive microsurgery and seminal microbiome. Nevertheless, as many cases of male infertility remain idiopathic, further studies are required to improve the clinical management of infertile males. A multidisciplinary strategy involving both clinicians and scientists in basic, translational, and clinical research is the core principle that will allow andrology to overcome its limits and reach further goals. This state-of-the-art article aims to present a historical review of andrology, and, particularly, male infertility, from its "Middle Ages" to its "Renaissance", a golden age of andrology.

Towards Automation in IVF: Pre-Clinical Validation of a Deep Learning-Based Embryo Grading System during PGT-A Cycles

Preimplantation genetic testing for aneuploidies (PGT-A) is arguably the most effective embryo selection strategy. Nevertheless, it requires greater workload, costs, and expertise. Therefore, a quest towards user-friendly, non-invasive strategies is ongoing. Although insufficient to replace PGT-A, embryo morphological evaluation is significantly associated with embryonic competence, but scarcely reproducible. Recently, artificial intelligence-powered analyses have been proposed to objectify and automate image evaluations. iDAScore v1.0 is a deep-learning model based on a 3D convolutional neural network trained on time-lapse videos from implanted and non-implanted blastocysts. It is a decision support system for ranking blastocysts without manual input. This retrospective, pre-clinical, external validation included 3604 blastocysts and 808 euploid transfers from 1232 cycles. All blastocysts were retrospectively assessed through the iDAScore v1.0; therefore, it did not influence embryologists' decision-making process. iDAScore v1.0 was significantly associated with embryo morphology and competence, although AUCs for euploidy and live-birth prediction were 0.60 and 0.66, respectively, which is rather comparable to embryologists' performance. Nevertheless, iDAScore v1.0 is objective and reproducible, while embryologists' evaluations are not. In a retrospective simulation, iDAScore v1.0 would have ranked euploid blastocysts as top quality in 63% of cases with one or more euploid and aneuploid blastocysts, and it would have questioned embryologists' ranking in 48% of cases with two or more euploid blastocysts and one or more live birth. Therefore, iDAScore v1.0 may objectify embryologists' evaluations, but randomized controlled trials are required to assess its clinical value.

Quantitative approaches in clinical reproductive endocrinology

Understanding the human hypothalamic-pituitary-gonadal (HPG) axis presents a major challenge for medical science. Dysregulation of the HPG axis is linked to infertility and a thorough understanding of its dynamic behaviour is necessary to both aid diagnosis and to identify the most appropriate hormonal interventions. Here, we review how quantitative models are being used in the context of clinical reproductive endocrinology to: 1. analyse the secretory patterns of reproductive hormones; 2. evaluate the effect of drugs in fertility treatment; 3. aid in the personalization of assisted reproductive technology (ART). In this review, we demonstrate that quantitative models are indispensable tools enabling us to describe the complex dynamic behaviour of the reproductive axis, refine the treatment of fertility disorders, and predict clinical intervention outcomes.

Preliminary prediction of semen quality based on modifiable lifestyle factors by using the XGBoost algorithm

Introduction

Semen quality has decreased gradually in recent years, and lifestyle changes are among the primary causes for this issue. Thus far, the specific lifestyle factors affecting semen quality remain to be elucidated.

Materials and methods

In this study, data on the following factors were collected from 5,109 men examined at our reproductive medicine center: 10 lifestyle factors that potentially affect semen quality (smoking status, alcohol consumption, staying up late, sleeplessness, consumption of pungent food, intensity of sports activity, sedentary lifestyle, working in hot conditions, sauna use in the last 3 months, and exposure to radioactivity); general factors including age, abstinence period, and season of semen examination; and comprehensive semen parameters [semen volume, sperm concentration, progressive and total sperm motility, sperm morphology, and DNA fragmentation index (DFI)]. Then, machine learning with the XGBoost algorithm was applied to establish a primary prediction model by using the collected data. Furthermore, the accuracy of the model was verified via multiple logistic regression following k-fold cross-validation analyses.

Results

The results indicated that for semen volume, sperm concentration, progressive and total sperm motility, and DFI, the area under the curve (AUC) values ranged from 0.648 to 0.697, while the AUC for sperm morphology was only 0.506. Among the 13 factors, smoking status was the major factor affecting semen volume, sperm concentration, and progressive and total sperm motility. Age was the most important factor affecting DFI. Logistic combined with cross-validation analysis revealed similar results. Furthermore, it showed that heavy smoking (>20 cigarettes/day) had an overall negative effect on semen volume and sperm concentration and progressive and total sperm motility (OR = 4.69, 6.97, 11.16, and 10.35, respectively), while age of >35 years was associated with increased DFI (OR = 5.47).

Conclusion

The preliminary lifestyle-based model developed for semen quality prediction by using the XGBoost algorithm showed potential for clinical application and further optimization with larger training datasets.

Human blastocyst spontaneous collapse is associated with worse morphological quality and higher degeneration and aneuploidy rates: a comprehensive analysis standardized through artificial intelligence

STUDY QUESTION

What are the factors associated with human blastocyst spontaneous collapse and the consequences of this event?

SUMMARY ANSWER

Approximately 50% of blastocysts collapsed, especially when non-viable, morphologically poor and/or aneuploid.

WHAT IS KNOWN ALREADY

Time-lapse microscopy (TLM) is a powerful tool to observe preimplantation development dynamics. Lately, artificial intelligence (AI) has been harnessed to automate and standardize such observations. Here, we adopted AI to comprehensively portray blastocyst spontaneous collapse, namely the phenomenon of reduction in size of the embryo accompanied by efflux of blastocoel fluid and the detachment of the trophectoderm (TE) from the zona pellucida (ZP). Although the underlying causes are unknown, blastocyst spontaneous collapse deserves attention as a possible marker of reduced competence.

STUDY DESIGN, SIZE, DURATION

An observational study was carried out, including 2348 TLM videos recorded during preimplantation genetic testing for aneuploidies (PGT-A, n = 720) cycles performed between January 2013 and December 2020. All embryos in the analysis at least reached the time of starting blastulation (tSB), 1943 of them reached full expansion, and were biopsied and then vitrified.

PARTICIPANTS/MATERIALS, SETTING, METHODS

ICSI, blastocyst culture, TE biopsy without Day 3 ZP drilling, comprehensive chromosome testing and vitrification were performed. The AI software automatically registered tSB and time of expanding blastocyst (tEB), start and end time of each collapse, time between consecutive collapses, embryo proper area, percentage of shrinkage, embryo:ZP ratio at embryo collapse, time of biopsy (t-biopsy) and related area of the fully (re-)expanded blastocyst before biopsy, time between the last collapse and biopsy. Blastocyst morphological quality was defined according to both Gardner's criteria and an AI-generated implantation score. Euploidy rate per biopsied blastocyst and live birth rate (LBR) per euploid single embryo transfer (SET) were the main outcomes. All significant associations were confirmed through regression analyses. All couple, cycle and embryo main features were also investigated for possible associations with blastocyst spontaneous collapse.

MAIN RESULTS AND THE ROLE OF CHANCE

At least one collapsing embryo (either viable or subsequently undergoing degeneration) was recorded in 559 cycles (77.6%) and in 498 cycles (69.2%) if considering only viable blastocysts. The prevalence of blastocyst spontaneous collapse after the tSB, but before the achievement of full expansion, was 50% (N = 1168/2348), irrespective of cycle and/or couple characteristics. Blastocyst degeneration was 13% among non-collapsing embryos, while it was 18%, 20%, 26% and 39% among embryos collapsing once, twice, three times or ≥4 times, respectively. The results showed that 47.3% (N = 918/1943) of the viable blastocysts experienced at least one spontaneous collapse (ranging from 1 up to 9). Although starting from similar tSB, the number of spontaneous collapses was associated with a delay in both tEB and time of biopsy. Of note, the worse the quality of a blastocyst, the more and the longer its spontaneous collapses. Blastocyst spontaneous collapse was significantly associated with lower euploidy rates (47% in non-collapsing and 38%, 32%, 31% and 20% in blastocysts collapsing once, twice, three times or ≥4 times, respectively; multivariate odds ratio 0.78, 95%CI 0.62-0.98, adjusted P = 0.03). The difference in the LBR after euploid vitrified-warmed SET was not significant (46% and 39% in non-collapsing and collapsing blastocysts, respectively).

LIMITATIONS, REASONS FOR CAUTION

An association between chromosomal mosaicism and blastocyst collapse cannot be reliably assessed on a single TE biopsy. Gestational and perinatal outcomes were not evaluated. Other culture strategies and media should be tested for their association with blastocyst spontaneous collapse. Future studies with a larger sample size are needed to investigate putative impacts on clinical outcomes after euploid transfers.

WIDER IMPLICATIONS OF THE FINDINGS

These results demonstrate the synergistic power of TLM and AI to increase the throughput of embryo preimplantation development observation. They also highlight the transition from compaction to full blastocyst as a delicate morphogenetic process. Blastocyst spontaneous collapse is common and associates with inherently lower competence, but additional data are required to deepen our knowledge on its causes and consequences.

STUDY FUNDING/COMPETING INTEREST(S)

There is no external funding to report. I.E., A.B.-M., I.H.-V. and B.K. are Fairtility employees. I.E. and B.K. also have stock or stock options of Fairtility.

TRIAL REGISTRATION NUMBER

N/A.

Parameters to identify good quality oocytes and embryos in cattle

Oocyte/embryo selection methodologies are either invasive or noninvasive and can be applied at various stages of development from the oocyte to cleaved embryos and up to the blastocyst stage. Morphology and the proportion of embryos developing to the blastocyst stage are important criteria to assess developmental competence. Evaluation of morphology remains the method of choice for selecting viable oocytes for IVP or embryos prior to transfer. Although non-invasive approaches are improving, invasive ones have been extremely helpful in finding candidate genes to determine oocyte/embryo quality. There is still a strong need for further refinement of existing oocyte and embryo selection methods and quality parameters. The development of novel, robust and non-invasive procedures will ensure that only embryos with the highest developmental potential are chosen for transfer. In the present review, various methods for assessing the quality of oocytes and preimplantation embryos, particularly in cattle, are considered. These methods include assessment of morphology including different staining procedures, transcriptomic and proteomic analyses, metabolic profiling, as well as the use of artificial intelligence technologies.