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

Andrology laboratory techniques for micro-TESE/IVF/ICSI: a narrative review

ABSTRACT

Since the early days of assisted reproductive technology (ART), the importance of sperm processing, employed to separate the motile, morphologically normal sperm from the semen, has been shown to be beneficial. The aim of the semen processing technique has been to remove seminal plasma and facilitate capacitation. Additionally, the presence of leukocytes, bacteria, and dead spermatozoa has been shown to be detrimental as it may cause oxidative stress that has an adverse effect on oocyte fertilization and embryo development. Hence, removal of leukocytes, bacteria, and dead spermatozoa is an important step of sperm processing for assisted reproduction. Currently, several sperm processing techniques have been evolved and optimized in the field of assisted reproduction. The requirements for in vitro fertilization (IVF), intracytoplasmic sperm injection (ICSI), and testicular sperm extraction (TESE) are different than those of intrauterine insemination (IUI). The yield of as many motile, morphologically normal sperm as possible is a prerequisite for the success of IVF insemination procedure. In ICSI, where injection of a single spermatozoon into the oocyte is performed by the embryologist, sperm selection techniques play a crucial role in the ICSI procedure. Finally, sperm retrieval in TESE samples with very low number of sperm may be challenging and requires extra care during sample processing. Additionally, sperm cryopreservation is necessary in TESE cases in order to avoid multiple biopsies.

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.

Morphological Parameters of 29994 sperm in a fertile male population-based on Papanicolaou staining and SSA-II Plus

Objective

This study aims to provide reference data for sperm morphology in a healthy, fertile male population providing a foundation for future studies on male infertility assessment and sperm selection in assisted reproductive technologies.

Methods

The study included 21 healthy male participants, all of whom had partners who conceived within the past 12 months. Sperm samples were collected according to WHO guidelines and stained using the Papanicolaou method. Sperm morphology parameters, including head length, width, area, perimeter, ellipticity, and acrosome area, were measured using the Suiplus SSA-II Computer-Assisted Sperm Analysis (CASA) system. Statistical comparisons were made between CASA and traditional manual methods.

Results

The percentage of sperm with normal head morphology was 9.98%. Detailed sperm head measurements, including length, width, and area, were provided as reference values for the healthy male population. The CASA system demonstrated the ability to reduce subjective errors and showed no significant differences in sperm count and motility compared to traditional methods.

Conclusion

This study provides precise sperm morphology reference values that enhance male infertility diagnostics and treatment, particularly in sperm selection for assisted reproductive technologies like ICSI.

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).

AI predictive models and advancements in microdissection testicular sperm extraction for non-obstructive azoospermia: a systematic scoping review

STUDY QUESTION

How accurately can artificial intelligence (AI) models predict sperm retrieval in non-obstructive azoospermia (NOA) patients undergoing micro-testicular sperm extraction (m-TESE) surgery?

SUMMARY ANSWER

AI predictive models hold significant promise in predicting successful sperm retrieval in NOA patients undergoing m-TESE, although limitations regarding variability of study designs, small sample sizes, and a lack of validation studies restrict the overall generalizability of studies in this area.

WHAT IS KNOWN ALREADY

Previous studies have explored various predictors of successful sperm retrieval in m-TESE, including clinical and hormonal factors. However, no consistent predictive model has yet been established.

STUDY DESIGN SIZE DURATION

A comprehensive literature search was conducted following PRISMA-ScR guidelines, covering PubMed and Scopus databases from 2013 to 15 May 2024. Relevant English-language studies were identified using Medical Subject Headings (MeSH) terms. We also used PubMed's 'similar articles' and 'cited by' features for thorough bibliographic screening to ensure comprehensive coverage of relevant literature.

PARTICIPANTS/MATERIALS SETTING METHODS

The review included studies on patients with NOA where AI-based models were used for predicting m-TESE outcomes, by incorporating clinical data, hormonal levels, histopathological evaluations, and genetic parameters. Various machine learning and deep learning techniques, including logistic regression, were employed. The Prediction Model Risk of Bias Assessment Tool (PROBAST) evaluated the bias in the studies, and their quality was assessed using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines, ensuring robust reporting standards and methodological rigor.

MAIN RESULTS AND THE ROLE OF CHANCE

Out of 427 screened articles, 45 met the inclusion criteria, with most using logistic regression and machine learning to predict m-TESE outcomes. AI-based models demonstrated strong potential by integrating clinical, hormonal, and biological factors. However, limitations of the studies included small sample sizes, legal barriers, and challenges in generalizability and validation. While some studies featured larger, multicenter designs, many were constrained by sample size. Most studies had a low risk of bias in participant selection and outcome determination, and two-thirds were rated as low risk for predictor assessment, but the analysis methods varied.

LIMITATIONS REASONS FOR CAUTION

The limitations of this review include the heterogeneity of the included research, potential publication bias and reliance on only two databases (PubMed and Scopus), which may limit the scope of the findings. Additionally, the absence of a meta-analysis prevents quantitative assessment of the consistency of models. Despite this, the review offers valuable insights into AI predictive models for m-TESE in NOA.

WIDER IMPLICATIONS OF THE FINDINGS

The review highlights the potential of advanced AI techniques in predicting successful sperm retrieval for NOA patients undergoing m-TESE. By integrating clinical, hormonal, histopathological, and genetic factors, AI models can enhance decision-making and improve patient outcomes, reducing the number of unsuccessful procedures. However, to further enhance the precision and reliability of AI predictions in reproductive medicine, future studies should address current limitations by incorporating larger sample sizes and conducting prospective validation trials. This continued research and development is crucial for strengthening the applicability of AI models and ensuring broader clinical adoption.

STUDY FUNDING/COMPETING INTERESTS

The authors would like to acknowledge Mashhad University of Medical Sciences, Mashhad, Iran, for financial support (Grant ID: 4020802). The authors declare no competing interests.

REGISTRATION NUMBER

N/A.

Enhancing Male Fertility Through AI-Based Management of Varicoceles

REVIEW PURPOSE

The clinical management of subclinical and symptomatic varicoceles in male infertility remains challenging. Current guidelines focus on treating men with abnormal semen analyses, but a more precise approach to identify, stratify, and prognosticate men with varicoceles and fertility issues is essential.

RECENT FINDINGS

Multiple studies have utilized Artificial Intelligence (AI) to analyze clinical-demographic characteristics, semen analyses, pre-operative imaging findings, and intra-operative clinical data. These AI-driven approaches aim to discover novel biomarkers that can assess, stratify, and prognosticate men with subclinical and symptomatic varicoceles requiring early intervention. These sophisticated methodologies offer new insights and strategies for understanding normal spermatogenesis and the pathophysiology of varicocele-related male infertility. The application of AI strategies is expected to revolutionize varicocele management, enhancing male fertility and optimizing reproductive outcomes.

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.

Spermbots and Their Applications in Assisted Reproduction: Current Progress and Future Perspectives

Sperm quality is declining dramatically during the past decades. Male infertility has been a serious health and social problem. The sperm cell driven biohybrid nanorobot opens a new era for automated and precise assisted reproduction. Therefore, it is urgent and necessary to conduct an updated review and perspective from the viewpoints of the researchers and clinicians in the field of reproductive medicine. In the present review, we first update the current classification, design, control and applications of various spermbots. Then, by a comprehensive summary of the functional features of sperm cells, the journey of sperms to the oocyte, and sperm-related dysfunctions, we provide a systematic guidance to further improve the design of spermbots. Focusing on the translation of spermbots into clinical practice, we point out that the main challenges are biocompatibility, effectiveness, and ethical issues. Considering the special requirements of assisted reproduction, we also propose the three laws for the clinical usage of spermbots: good genetics, gentle operation and no contamination. Finally, a three-step roadmap is proposed to achieve the goal of clinical translation. We believe that spermbot-based treatments can be validated and approved for in vitro clinical usage in the near future. However, multi-center and multi-disciplinary collaborations are needed to further promote the translation of spermbots into in vivo clinical applications.

Automated Single-Sperm Selection Software (SiD) during ICSI: A Prospective Sibling Oocyte Evaluation

The computer-assisted program SiD was developed to assess and select sperm in real time based on motility characteristics. To date, there are limited studies examining the correlation between AI-assisted sperm selection and ICSI outcomes. To address this limit, a total of 646 sibling MII oocytes were randomly divided into two groups as follows: the ICSI group (n = 320): ICSI performed with sperm selected by the embryologist and the ICSI-SiD group (n = 326): ICSI performed with sperm selected using SiD software. Our results show a non-significant trend towards improved outcomes in the ICSI-SiD group across various biological parameters, including fertilization, cleavage, day 3 embryo development, blastocyst development, and quality on day 5. Similarly, we observed a non-significant increase in these outcomes when comparing both groups with sperm selection performed by a junior embryologist. Embryo development was monitored using a timelapse system. Some fertilization events happen significantly earlier when SiD is used for ICSI, but no significant difference was observed in the ICSI-SiD group for other timepoints. We observed comparable cumulative early and clinical pregnancy rates after ICSI-SiD. This preliminary investigation illustrated that employing the automated sperm selection software SiD leads to comparable biological outcomes, suggesting its efficacy in sperm selection.

Sperm morphology value in assisted reproduction: dismantling an enigma and key takeaways for the busy clinician

The ideal morphology of the sperm cell was initially described based on the characteristics of sperm able to migrate through the endocervical canal assuming these had the best fertilization potential. Sperm morphology assessment has moved over the years toward stricter criteria based on the findings from studies that underline its value in successful reproductive outcomes. While treatment options are clear for some conditions related to abnormal sperm morphology, the value of sperm morphology in assisted reproduction requires further investigation. The objective of this review is to offer care providers updated guidance for choosing appropriate treatment strategies based on sperm morphology assessment and morphological deviations. Issues to be considered for a reliable determination and interpretation of sperm morphology using the current thresholds and criteria are discussed. In addition, key knowledge on morphological abnormalities relevant to the clinical care of infertile patients, distinguishing between monomorphic and polymorphic forms as well as the isolated or non-isolated occurrence of teratozoospermia in semen is presented. Furthermore, the impact of impaired morphology on assisted reproductive technique outcomes is summarized in light of the latest research.