An artificial intelligence tool predicts blastocyst development from static images of fresh mature oocytes

The Role of Artificial Intelligence in Female Infertility Diagnosis: An Update

Female infertility is a multifaceted condition affecting millions of women worldwide, with causes ranging from hormonal imbalances and genetic predispositions to lifestyle and environmental factors. Traditional diagnostic approaches, such as hormonal assays, ultrasound imaging, and genetic testing, often require extensive time, resources, and expert interpretation. In recent years, artificial intelligence (AI) has emerged as a transformative tool in the field of reproductive medicine, offering advanced capabilities for improving the accuracy, efficiency, and personalization of infertility diagnosis and treatment. AI technologies demonstrate significant potential in analyzing vast and complex datasets, identifying hidden patterns, and providing data-driven insights that enhance clinical decision-making processes in assisted reproductive technologies (ART) services. This narrative review explores the current advancements in AI applications in female infertility diagnostics and therapeutics, highlighting key technological innovations, their clinical implications, and existing limitations. It also discusses the future potential of AI in revolutionizing reproductive healthcare. As AI-based technologies continue to evolve, their integration into reproductive medicine is expected to pave the way for more accessible, cost-effective, and personalized fertility care.

IVF - through the looking glass

'Through the looking-glass' is a metaphor often used to infer an unfamiliar or anomalous situation, an altered reality. This is perhaps a fitting representation of what human oocytes, spermatozoa and embryos experience when isolated and maintained in the artificial world that comprises an IVF laboratory. Rather than the dynamic and dark reproductive tract in vivo, the laboratory represents a strikingly foreign landscape to gametes and embryos, characterized by a polystyrene substrate, aqueous media and exposure to light. Furthermore, all culture systems employed over the past five decades have been static, in striking contrast to the continual movement experienced by gametes and embryos within the female tract. Recent developments in microfabrication, biomimetics and artificial intelligence, are, however, paving the way to replicate aspects of in-vivo physiology and anatomy that may enhance gamete preparation and selection, creating healthier embryos. Combined with potential improvements in culture conditions afforded by microperfusion, developments in new microscopies and in AI could also provide new ways both to visualize embryos and to acquire important data on their metabolic state to facilitate improved diagnosis of viability and aneuploidy. Such advancements will contribute to higher pregnancy rates, reducing time to pregnancy and reducing pregnancy loss, culminating in improved clinical outcomes.

Artificial intelligence in assisted reproductive technology: separating the dream from reality

This paper critically reviews the role of artificial intelligence (AI) in assisted reproductive technology (ART), a nascent field that has emerged over the last decade. While AI holds immense promise for enhancing IVF efficiency, standardization, and outcomes, its current trajectory reveals significant challenges. Much of the recent literature presents variations on established methodologies rather than groundbreaking advancements, with many studies lacking clear clinical applications or outcome-driven validations. Moreover, the growing enthusiasm for AI in ART is often accompanied by undue hype that obscures its realistic potential and fosters inflated expectations. Despite these limitations, AI-driven innovations such as advanced image analysis, personalized protocols, and automation of embryology workflows are beginning to show value. Machine learning algorithms and robotics may help address inefficiencies, alleviate staff shortages, and improve decision-making in the IVF laboratory. However, progress is tempered by drawbacks including ethical concerns, limited transparency in AI systems, and regulatory impediments. Data-sharing barriers in our field hinder AI tool development significantly. Energy-intensive computational processes and expanding data centers also raise sustainability concerns, underscoring the need for environmentally responsible development. As the field evolves, it must emphasize rigorous validation, collaborative data frameworks, and alignment with the needs of ART practitioners and patients.

A review of artificial intelligence applications in in vitro fertilization

The field of reproductive medicine has witnessed rapid advancements in artificial intelligence (AI) methods, which have significantly enhanced the efficiency of diagnosing and treating reproductive disorders. The integration of AI algorithms into the in vitro fertilization (IVF) has the potential to represent the next frontier in advancing personalized reproductive medicine and enhancing fertility outcomes for patients. The potential of AI lies in its ability to bring about a new era characterized by standardization, automation, and an improved success rate in IVF. At present, the utilization of AI in clinical practice is still in its early stages and faces numerous ethical, regulatory, and technical challenges that require attention. In this review, we present an overview of the latest advancements in various applications of AI in IVF, including follicular monitoring, oocyte assessment, embryo selection, and pregnancy outcome prediction. The aim is to reveal the current state of AI applications in the field of IVF, their limitations, and prospects for future development. Further studies, which involve the development of comprehensive models encompassing multiple functions and the conduct of large-scale randomized controlled trials, could potentially indicate the future direction of AI advancements in the field of IVF.

Use of federated learning to develop an artificial intelligence model predicting usable blastocyst formation from pre-ICSI oocyte images

RESEARCH QUESTION

Can federated learning be used to develop an artificial intelligence (AI) model for evaluating oocyte competence using two-dimensional images of denuded oocytes in metaphase II prior to intracytoplasmic sperm injection (ICSI)?

RESULTS

The oocyte AI model demonstrated area under the curve (AUC) up to 0.65 on two blind test datasets. High sensitivity for predicting competent oocytes (83-88%) was offset by lower specificity (26-36%). Exclusion of confounding biological variables (male factor infertility and maternal age ≥35 years) improved AUC up to 14%, primarily due to increased specificity. AI score correlated with size of the zona pellucida and perivitelline space, and ooplasm appearance. AI score also correlated with blastocyst expansion grade and morphological quality. The sum of AI scores from oocytes in group culture images predicted the formation of two or more usable blastocysts (AUC 0.77).

CONCLUSION

An AI model to evaluate oocyte competence was developed using federated learning, representing an essential step in protecting patient data. The AI model was significantly predictive of oocyte competence, as defined by usable blastocyst formation, which is a critical factor for IVF success. Potential clinical utility ranges from selective oocyte fertilization to guiding treatment decisions regarding additional rounds of oocyte retrieval.

DESIGN

In total, 10,677 oocyte images with associated metadata were collected prospectively by eight IVF clinics across six countries. AI training used federated learning, where data were retained on regional servers to comply with data privacy laws. The final AI model required a single image as input to evaluate oocyte competence, which was defined by the formation of a usable blastocyst (≥expansion grade 3 by day 5 or 6 post ICSI).

Quality of IVM ovarian tissue oocytes: impact of clinical, demographic, and laboratory factors

PURPOSE

To determine how clinical, demographic, and laboratory characteristics influence ovarian tissue oocyte quality.

METHODS

Immature cumulus-oocyte complexes were isolated from removed ovaries and cultured for 48-52 h in either monophasic standard or biphasic CAPA media for fertility preservation. A total of 355 MII oocytes from 53 patients were described for intracytoplasmic and extracytoplasmic anomalies. Multiple clinical, laboratory, and demographic characteristics were analyzed. Statistically significant differences between independent groups in qualitative variables were identified using Pearson's χ2 and Fisher's exact tests. The diagnostic value of quantitative variables was assessed using the ROC curve analysis. Factors associated with the development of dysmorphism, taking patient age into account, were identified using the binary logistic regression analysis.

RESULTS

Dysmorphisms were observed in 245 oocytes (69.0%), with a median number of dysmorphisms of 2. Oocyte dysmorphisms were found to be 2.211 times more likely to be detected in patients with ovarian cancer, while the presence of dark-colored cytoplasm was associated with gynecologic surgery in the anamnesis (p = 0.002; OR 16.652; 95% CI, 1.977-140.237; Cramer's V 0.187). Small polar bodies developed 2.717 times more often (95% CI, 1.195-6.18) in patients older than 35. In the case of ovarian transportation on ice at 4 ℃, the chances of development of cytoplasmic granularity increased 2.569 times (95% CI, 1.301-5.179). The use of biphasic CAPA IVM media contributed to a decrease in the probability of large polar body formation (p = 0.034) compared to the standard monophasic IVM media.

CONCLUSIONS

Both patients' characteristics and laboratory parameters have an impact on the quality of IVM ovarian tissue oocytes.

On the role of artificial intelligence in analysing oocytes during in vitro fertilisation procedures

Nowadays, the most adopted technique to address infertility problems is in vitro fertilisation (IVF). However, its success rate is limited, and the associated procedures, known as assisted reproduction technology (ART), suffer from a lack of objectivity at the laboratory level and in clinical practice. This paper deals with applications of Artificial Intelligence (AI) techniques to IVF procedures. Artificial intelligence is considered a promising tool for ascertaining the quality of embryos, a critical step in IVF. Since the oocyte quality influences the final embryo quality, we present a systematic review of the literature on AI-based techniques used to assess oocyte quality; we analyse its results and discuss several promising research directions. In particular, we highlight how AI-based techniques can support the IVF process and examine their current applications as presented in the literature. Then, we discuss the challenges research must face in fully deploying AI-based solutions in current medical practice. Among them, the availability of high-quality data sets as well as standardised imaging protocols and data formats, the use of physics-informed simulation and machine learning techniques, the study of informative, descriptive yet observable features, and, above all, studies of the quality of oocytes and embryos, specifically about their live birth potential. An improved understanding of determinants for oocyte quality can improve success rates while reducing costs, risks for long-term embryo cultures, and bioethical concerns.

Oocyte Quality in Women with Endometriosis

BACKGROUND

Endometriosis is a chronic gynecological condition that affects approximately 10% of women of reproductive age globally. It is associated with significant morbidity due to symptoms such as pelvic pain and infertility. Current knowledge suggests that endometriosis impacts oocyte quality, a critical factor for successful fertilization and pregnancy. Despite extensive research, the exact mechanisms remain unclear, and further updates are necessary to optimize treatment strategies.

OBJECTIVES

This review aims to summarize current evidence regarding the impact of endometriosis on oocyte quality and its subsequent effects on fertility outcomes, particularly in the context of in vitro fertilization (IVF).

METHODS

A comprehensive search was conducted in PubMed using the terms "endometriosis AND oocyte quality," "endometriosis AND infertility, and "endometriosis AND IVF." The review included studies published up to July 2024.

OUTCOME

The review findings indicate that endometriosis may be associated with decreased oocyte quality, characterized by impaired morphological features and molecular abnormalities. These defects potentially lead to lower fertilization rates, impaired embryo development, and reduced pregnancy outcomes. However, some studies suggest that with controlled factors such as age and ovarian reserve, IVF outcomes may be comparable to those without endometriosis.

CONCLUSIONS AND OUTLOOK

For clinicians and scientists working in medically assisted reproduction, understanding the impact of endometriosis on oocyte quality is crucial for improving fertility treatment outcomes. Advances in assisted reproductive technologies and personalized treatment approaches may mitigate these adverse effects. The potential for using artificial intelligence to assess oocyte quality presents a promising avenue for future research, as currently there is no direct and objective measure to assess this parameter.

Segmentation of mature human oocytes provides interpretable and improved blastocyst outcome predictions by a machine learning model

Within the medical field of human assisted reproductive technology, a method for interpretable, non-invasive, and objective oocyte evaluation is lacking. To address this clinical gap, a workflow utilizing machine learning techniques has been developed involving automatic multi-class segmentation of two-dimensional images, morphometric analysis, and prediction of developmental outcomes of mature denuded oocytes based on feature extraction and clinical variables. Two separate models have been developed for this purpose-a model to perform multiclass segmentation, and a classifier model to classify oocytes as likely or unlikely to develop into a blastocyst (Day 5-7 embryo). The segmentation model is highly accurate at segmenting the oocyte, ensuring high-quality segmented images (masks) are utilized as inputs for the classifier model (mask model). The mask model displayed an area under the curve (AUC) of 0.63, a sensitivity of 0.51, and a specificity of 0.66 on the test set. The AUC underwent a reduction to 0.57 when features extracted from the ooplasm were removed, suggesting the ooplasm holds the information most pertinent to oocyte developmental competence. The mask model was further compared to a deep learning model, which also utilized the segmented images as inputs. The performance of both models combined in an ensemble model was evaluated, showing an improvement (AUC 0.67) compared to either model alone. The results of this study indicate that direct assessments of the oocyte are warranted, providing the first objective insights into key features for developmental competence, a step above the current standard of care-solely utilizing oocyte age as a proxy for quality.