Machine learning for sperm selection

Novel bioinformatic analyses of somatic cell contamination in sperm samples

The assessment of epigenetic profiles in sperm is sensitive to somatic cell contamination, which can influence methylation signals at gene promoters. This contamination is particularly problematic in the assessment of DNA methylation in samples with low sperm counts, where fractional amounts of somatic cell DNA can lead to significant shifts in measured methylation state. In this study, a new method of detecting possible somatic cell contamination is proposed through two multi-region bioinformatic models: a traditional differential methylation analysis and a machine learning logistic regression model. These models were trained on publicly available sperm (n = 489) and blood (n = 1029) DNA methylation array data and tested on a contamination set, wherein the sperm of four donors with normal sperm counts were run on a 450k methylation array with four permutations each, including pure blood, half blood and half sperm by DNA concentration, half blood and half sperm by cell count, and pure sperm (n = 16). The DMR and logistic regression model classified the contamination testing set with 100% and 94% accuracy, respectively. These new methods of detecting the effects of somatic cell contamination allow for more accurate differentiation between epigenetic profiles that contain a biological somatic-like shift and those that have somatic-like signatures because of contamination.

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.

Advances in microfluidic technology for sperm screening and in vitro fertilization

About 18% of reproductive-age adults worldwide are affected by infertility. In vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) are widely used assisted reproductive technologies (ARTs) aimed at improving clinical outcomes. Efficient and noninvasive selection and isolation of highly motile sperm with intact DNA are essential for the success of IVF and ICSI and can potentially impact the therapeutic efficacy and the health of the offspring. Compared to traditional methods, microfluidic technology offers significant advantages such as low sample consumption, high efficiency, minimal damage, high integration, similar microenvironment, and high automation, providing a new platform for ARTs. Here, we review the current situation of microfluidic technology in the field of sperm motility screening and evaluation and IVF research. First, we focus on the working principle, structural design, and screening results of sperm selection microfluidic platforms. We then highlight how the multiple steps of the IVF process can be facilitated and integrated into a microfluidic chip, including oocyte capture, sperm collection and isolation, sperm sorting, fertilization, and embryo culture. Ultimately, we summarize how microfluidics can complement and optimize current sperm sorting and IVF protocols, and challenges and possible solutions are discussed.

"Modeling Diffusive Search by Non-Adaptive Sperm: Empirical and Computational Insights"

During fertilization, mammalian sperm undergo a winnowing selection process that reduces the candidate pool of potential fertilizers from ~106-1011 cells to 101-102 cells (depending on the species). Classical sperm competition theory addresses the positive or 'stabilizing' selection that acts on sperm phenotypes within populations of organisms but does not strictly address the developmental consequences of sperm traits among individual organisms that are under purifying selection during fertilization. It is the latter that is of utmost concern for improving assisted reproductive technologies (ART) because 'low fitness' sperm may be inadvertently used for fertilization during interventions that rely heavily on artificial sperm selection, such as intracytoplasmic sperm injection (ICSI). Importantly, some form of sperm selection is used in nearly all forms of ART (e.g., differential centrifugation, swim-up, or hyaluronan binding assays, etc.). To date, there is no unifying quantitative framework (i.e., theory of sperm selection) that synthesizes causal mechanisms of selection with observed natural variation in individual sperm traits. In this report, we reframe the physiological function of sperm as a collective diffusive search process and develop multi-scale computational models to explore the causal dynamics that constrain sperm 'fitness' during fertilization. Several experimentally useful concepts are developed, including a probabilistic measure of sperm 'fitness' as well as an information theoretic measure of the magnitude of sperm selection, each of which are assessed under systematic increases in microenvironmental selective pressure acting on sperm motility patterns.

Artificial intelligence and porcine breeding

Livestock management is evolving into a new era, characterized by the analysis of vast quantities of data (Big Data) collected from both traditional breeding methods and new technologies such as sensors, automated monitoring system, and advanced analytics. Artificial intelligence (A-In), which refers to the capability of machines to mimic human intelligence, including subfields like machine learning and deep learning, is playing a pivotal role in this transformation. A wide array of A-In techniques, successfully employed in various industrial and scientific contexts, are now being integrated into mainstream livestock management practices. In the case of swine breeding, while traditional methods have yielded considerable success, the increasing amount of information requires the adoption of new technologies such as A-In to drive productivity, enhance animal welfare, and reduce environmental impact. Current findings suggest that these techniques have the potential to match or exceed the performance of traditional methods, often being more scalable in terms of efficiency and sustainability within the breeding industry. This review provides insights into the application of A-In in porcine breeding, from the perspectives of both sows (including welfare and reproductive management) and boars (including semen quality and health), and explores new approaches which are already being applied in other species.

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

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

Testing the generalizability and effectiveness of deep learning models among clinics: sperm detection as a pilot study

BACKGROUND

Deep learning has been increasingly investigated for assisting clinical in vitro fertilization (IVF). The first technical step in many tasks is to visually detect and locate sperm, oocytes, and embryos in images. For clinical deployment of such deep learning models, different clinics use different image acquisition hardware and different sample preprocessing protocols, raising the concern over whether the reported accuracy of a deep learning model by one clinic could be reproduced in another clinic. Here we aim to investigate the effect of each imaging factor on the generalizability of object detection models, using sperm analysis as a pilot example.

METHODS

Ablation studies were performed using state-of-the-art models for detecting human sperm to quantitatively assess how model precision (false-positive detection) and recall (missed detection) were affected by imaging magnification, imaging mode, and sample preprocessing protocols. The results led to the hypothesis that the richness of image acquisition conditions in a training dataset deterministically affects model generalizability. The hypothesis was tested by first enriching the training dataset with a wide range of imaging conditions, then validated through internal blind tests on new samples and external multi-center clinical validations.

RESULTS

Ablation experiments revealed that removing subsets of data from the training dataset significantly reduced model precision. Removing raw sample images from the training dataset caused the largest drop in model precision, whereas removing 20x images caused the largest drop in model recall. by incorporating different imaging and sample preprocessing conditions into a rich training dataset, the model achieved an intraclass correlation coefficient (ICC) of 0.97 (95% CI: 0.94-0.99) for precision, and an ICC of 0.97 (95% CI: 0.93-0.99) for recall. Multi-center clinical validation showed no significant differences in model precision or recall across different clinics and applications.

CONCLUSIONS

The results validated the hypothesis that the richness of data in the training dataset is a key factor impacting model generalizability. These findings highlight the importance of diversity in a training dataset for model evaluation and suggest that future deep learning models in andrology and reproductive medicine should incorporate comprehensive feature sets for enhanced generalizability across clinics.

Advancing Intelligent Organ-on-a-Chip Systems with Comprehensive In Situ Bioanalysis

In vitro models are essential to a broad range of biomedical research, such as pathological studies, drug development, and personalized medicine. As a potentially transformative paradigm for 3D in vitro models, organ-on-a-chip (OOC) technology has been extensively developed to recapitulate sophisticated architectures and dynamic microenvironments of human organs by applying the principles of life sciences and leveraging micro- and nanoscale engineering capabilities. A pivotal function of OOC devices is to support multifaceted and timely characterization of cultured cells and their microenvironments. However, in-depth analysis of OOC models typically requires biomedical assay procedures that are labor-intensive and interruptive. Herein, the latest advances toward intelligent OOC (iOOC) systems, where sensors integrated with OOC devices continuously report cellular and microenvironmental information for comprehensive in situ bioanalysis, are examined. It is proposed that the multimodal data in iOOC systems can support closed-loop control of the in vitro models and offer holistic biomedical insights for diverse applications. Essential techniques for establishing iOOC systems are surveyed, encompassing in situ sensing, data processing, and dynamic modulation. Eventually, the future development of iOOC systems featuring cross-disciplinary strategies is discussed.

Microfluidics as an emerging paradigm for assisted reproductive technology: A sperm separation perspective

Millions of people are subject to infertility worldwide and one in every six people, regardless of gender, experiences infertility at some period in their life, according to the World Health Organization. Assisted reproductive technologies are defined as a set of procedures that can address the infertility issue among couples, culminating in the alleviation of the condition. However, the costly conventional procedures of assisted reproduction and the inherent vagaries of the processes involved represent a setback for its successful implementation. Microfluidics, an emerging tool for processing low-volume samples, have recently started to play a role in infertility diagnosis and treatment. Given its host of benefits, including manipulating cells at the microscale, repeatability, automation, and superior biocompatibility, microfluidics have been adopted for various procedures in assisted reproduction, ranging from sperm sorting and analysis to more advanced processes such as IVF-on-a-chip. In this review, we try to adopt a more holistic approach and cover different uses of microfluidics for a variety of applications, specifically aimed at sperm separation and analysis. We present various sperm separation microfluidic techniques, categorized as natural and non-natural methods. A few of the recent developments in on-chip fertilization are also discussed.

Predicting Male Infertility Using Artificial Neural Networks: A Review of the Literature

Male infertility is a relevant public health problem, but there is no systematic review of the different machine learning (ML) models and their accuracy so far. The present review aims to comprehensively investigate the use of ML algorithms in predicting male infertility, thus reporting the accuracy of the used models in the prediction of male infertility as a primary outcome. Particular attention will be paid to the use of artificial neural networks (ANNs). A comprehensive literature search was conducted in PubMed, Scopus, and Science Direct between 15 July and 23 October 2023, conducted under the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We performed a quality assessment of the included studies using the recommended tools suggested for the type of study design adopted. We also made a screening of the Risk of Bias (RoB) associated with the included studies. Thus, 43 relevant publications were included in this review, for a total of 40 different ML models detected. The studies included reported a good quality, even if RoB was not always good for all the types of studies. The included studies reported a median accuracy of 88% in predicting male infertility using ML models. We found only seven studies using ANN models for male infertility prediction, reporting a median accuracy of 84%.

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.

Frequency, morbidity and equity - the case for increased research on male fertility

Currently, most men with infertility cannot be given an aetiology, which reflects a lack of knowledge around gamete production and how it is affected by genetics and the environment. A failure to recognize the burden of male infertility and its potential as a biomarker for systemic illness exists. The absence of such knowledge results in patients generally being treated as a uniform group, for whom the strategy is to bypass the causality using medically assisted reproduction (MAR) techniques. In doing so, opportunities to prevent co-morbidity are missed and the burden of MAR is shifted to the woman. To advance understanding of men's reproductive health, longitudinal and multi-national centres for data and sample collection are essential. Such programmes must enable an integrated view of the consequences of genetics, epigenetics and environmental factors on fertility and offspring health. Definition and possible amelioration of the consequences of MAR for conceived children are needed. Inherent in this statement is the necessity to promote fertility restoration and/or use the least invasive MAR strategy available. To achieve this aim, protocols must be rigorously tested and the move towards personalized medicine encouraged. Equally, education of the public, governments and clinicians on the frequency and consequences of infertility is needed. Health options, including male contraceptives, must be expanded, and the opportunities encompassed in such investment understood. The pressing questions related to male reproductive health, spanning the spectrum of andrology are identified in the Expert Recommendation.

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.

Ensembling noisy segmentation masks of blurred sperm images

BACKGROUND

Sperm tail morphology and motility have been demonstrated to be important factors in determining sperm quality for in vitro fertilization. However, many existing computer-aided sperm analysis systems leave the sperm tail out of the analysis, as detecting a few tail pixels is challenging. Moreover, some publicly available datasets for classifying morphological defects contain images limited only to the sperm head. This study focuses on the segmentation of full sperm, which consists of the head and tail parts, and appear alone and in groups.

METHODS

We re-purpose the Feature Pyramid Network to ensemble an input image with multiple masks from state-of-the-art segmentation algorithms using a scale-specific cross-attention module. We normalize homogeneous backgrounds for improved training. The low field depth of microscopes blurs the images, easily confusing human raters in discerning minuscule sperm from large backgrounds. We thus propose evaluation protocols for scoring segmentation models trained on imbalanced data and noisy ground truth.

RESULTS

The neural ensembling of noisy segmentation masks outperforms all single, state-of-the-art segmentation algorithms in full sperm segmentation. Human raters agree more on the head than tail masks. The algorithms also segment the head better than the tail.

CONCLUSIONS

The extensive evaluation of state-of-the-art segmentation algorithms shows that full sperm segmentation is challenging. We release the SegSperm dataset of images from Intracytoplasmic Sperm Injection procedures to spur further progress on full sperm segmentation with noisy and imbalanced ground truth. The dataset is publicly available at https://doi.org/10.34808/6wm7-1159.

Assessing Optimal Cell Counts in Sperm Shape Abnormality Assays in Rodents

Rodents have been the preferred models for the evaluation of the toxicity of pollutants and drugs and their genotoxic effects, including sperm shape abnormalities. The scientific literature is dominated by studies conducted with model animals in laboratory conditions, but a generally accepted and standardized protocol addressing the optimal number of sperm cells to count is still lacking. In this study, we reviewed the literature regarding the number of counted sperm cells in such assessments, published from 1969 to 2023. To infer the number of counts providing the best cost/benefit regarding the robustness of the assay results, a new dataset involving the analysis of two populations of wild rodents was produced. We evaluated the frequency of sperm shape abnormalities in a total of 50 wild brown rats (Rattus norvegicus) captured in two port cities, aiming to detect the impact of differential sperm cell counts in the obtained results. During necropsy, the fresh epididymis tail of adult male rats was excised, and sperm cells were fixated in slides. For each animal, a total of 300, 500, 1000, and 2000 cells were sequentially counted, and head abnormalities were registered. Counting 300 sperm cells failed to detect significant differences between groups and 500 counts resulted in marginally significant differences. Only when 1000 or 2000 sperm cells were counted, significant differences emerged between groups. We propose that studies addressing sperm shape abnormalities should standardize counts to an optimal value of 1000 cells per animal, warranting robust statistical results while providing the best compromise concerning labor time.

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.

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.

Advancements in the future of automating micromanipulation techniques in the IVF laboratory using deep convolutional neural networks

PURPOSE

To determine if deep learning artificial intelligence algorithms can be used to accurately identify key morphologic landmarks on oocytes and cleavage stage embryo images for micromanipulation procedures such as intracytoplasmic sperm injection (ICSI) or assisted hatching (AH).

METHODS

Two convolutional neural network (CNN) models were trained, validated, and tested over three replicates to identify key morphologic landmarks used to guide embryologists when performing micromanipulation procedures. The first model (CNN-ICSI) was trained (n = 13,992), validated (n = 1920), and tested (n = 3900) to identify the optimal location for ICSI through polar body identification. The second model (CNN-AH) was trained (n = 13,908), validated (n = 1908), and tested (n = 3888) to identify the optimal location for AH on the zona pellucida that maximizes distance from healthy blastomeres.

RESULTS

The CNN-ICSI model accurately identified the polar body and corresponding optimal ICSI location with 98.9% accuracy (95% CI 98.5-99.2%) with a receiver operator characteristic (ROC) with micro and macro area under the curves (AUC) of 1. The CNN-AH model accurately identified the optimal AH location with 99.41% accuracy (95% CI 99.11-99.62%) with a ROC with micro and macro AUCs of 1.

CONCLUSION

Deep CNN models demonstrate powerful potential in accurately identifying key landmarks on oocytes and cleavage stage embryos for micromanipulation. These findings are novel, essential stepping stones in the automation of micromanipulation procedures.

The bibliometric analysis of studies on intracytoplasmic sperm injection from 2002 to 2021

BACKGROUND

Infertility is estimated to occur in 1 out of every 4-7 couples. Intracytoplasmic sperm injection (ICSI), a type of assisted reproduction introduced in 1992, has been used across the world for almost all indications of infertility, yielding high pregnancy rates. There is a growing concern worldwide about ICSI since semen quality has declined in recent years, accompanied with the potential risks of this technology. This study aims to analyze the current status and hotspots of ICSI via a bibliometric analysis.

METHODS

We retrieved publications on ICSI from the Web of Science Core Collection database from 2002 to 2021. CiteSpace was used to summarize knowledge mapping of subject categories, keywords, and co-citation relationships with the strongest citation bursts. VOSviewer was used to explore co-citation and co-occurrence relationships for countries, organizations, references, authors, and keywords.

RESULTS

A total of 8271 publications were analyzed between 2002 and 2021. The major findings are as follows: the USA, China, Italy, Japan, and Belgium are the top five prolific countries. The Free University of Brussels, University of Copenhagen, University of Valencia, Ghent University, and the University of California San Francisco are the top five contributing organizations. Fertility and Sterility and Human Reproduction are the most productive and cited journals. The hotspot topics are risks of ICSI, oocyte preservation, live birth rate, infertile men, and embryo quality in the past two decades.

CONCLUSION

This study presents a research overview of ICSI from different perspectives. These findings will contribute to a better understanding of the current status of ICSI research and provide hotspots and trends for future studies.

A novel Human Conception Optimizer for solving optimization problems

Computational techniques are widely used to solve complex optimization problems in different fields such as engineering, finance, biology, and so on. In this paper, the Human Conception Optimizer (HCO) is proposed as a novel metaheuristic algorithm to solve any optimization problems. The idea of this algorithm is based on some biological principles of the human conception process, such as the selective nature of cervical gel in the female reproductive system to allow only healthy sperm cells into the cervix, the guidance nature of mucus gel to help sperm track a genital tracking path towards the egg in the Fallopian tube, the asymmetric nature of flagellar movement which allows sperm cells to move in the reproductive system, the sperm hyperactivation process to make them able to fertilize an egg. Thus, the strategies pursued by the sperm in searching for the egg in the Fallopian tube are modeled mathematically. The best sperm which will meet the position of the egg will be the solution of the algorithm. The performance of the proposed HCO algorithm is examined with a set of basic benchmark test functions called IEEE CEC-2005 and IEEE CEC-2020. A comparative study is also performed between the HCO algorithm and other available algorithms. The significance of the results is verified with statistical test methods. To validate the proposed HCO algorithm, two real-world engineering optimization problems are examined. For this purpose, a complex 14 over-current relay based IEEE 8 bus distribution system is considered. With the proposed algorithm, an improvement of 50% to 60% in total relay operating times is observed comparing with some existing results for the same system. Another engineering problem of designing an optimal proportional integral derivative (PID) controller for a blower driven patient hose mechanical ventilator (MV) is examined. A significant improvement in terms of response time, settling time is observed in the MV system by comparing with existing results.

The relevance of sperm morphology in male infertility

This brief report concerns the role of human sperm morphology assessment in different fields of male infertility: basic research, genetics, assisted reproduction technologies, oxidative stress. One of the best methods in studying sperm morphology is transmission electron microscopy (TEM) that enables defining the concept of sperm pathology and classifying alterations in non-systematic and systematic. Non-systematic sperm defects affect head and tail in variable ratio, whereas the rare systematic defects are characterized by a particular anomaly that marks most sperm of an ejaculate. TEM analysis and fluorescence in situ hybridization represent outstanding methods in the study of sperm morphology and cytogenetic in patients with altered karyotype characterizing their semen quality before intracytoplasmic sperm injection. In recent years, the genetic investigations on systematic sperm defects, made extraordinary progress identifying candidate genes whose mutations induce morphological sperm anomalies. The question if sperm morphology has an impact on assisted fertilization outcome is debated. Nowadays, oxidative stress represents one of the most important causes of altered sperm morphology and function and can be analyzed from two points of view: 1) spermatozoa with cytoplasmic residue produce reactive oxygen species, 2) the pathologies with inflammatory/oxidative stress background cause morphological alterations. Finally, sperm morphology is also considered an important endpoint in in vitro experiments where toxic substances, drugs, antioxidants are tested. We think that the field of sperm morphology is far from being exhausted and needs other research. This parameter can be still considered a valuable indicator of sperm dysfunction both in basic and clinical research.

Disposable Paper-Based Microfluidics for Fertility Testing

Fifteen percent of couples of reproductive age suffer from infertility globally and the burden of infertility disproportionately impacts residents of developing countries. Assisted reproductive technologies (ARTs), including in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI), have been successful in overcoming various reasons for infertility including borderline and severe male factor infertility which consists of 20%–30% of all infertile cases. Approximately half of male infertility cases stem from suboptimal sperm parameters. Therefore, healthy/normal sperm enrichment and sorting remains crucial in advancing reproductive medicine. Microfluidic technologies have emerged as promising tools to develop in-home rapid fertility tests and point-of-care (POC) diagnostic tools. Here, we review advancements in fabrication methods for paper-based microfluidic devices and their emerging fertility testing applications assessing sperm concentration, sperm motility, sperm DNA analysis, and other sperm functionalities, and provide a glimpse into future directions for paper-based fertility microfluidic systems.

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Paper-based technologies are emerging to develop in-home rapid fertility tests

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Fabrication methods for paper-based microfluidic devices are presented

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Emerging disposable paper-based fertility testing applications are reviewed

Lab on a chip devices for fertility: from proof-of-concept to clinical impact

Microfluidics offers tremendous opportunities to understand the underlying biology of fertilization at the single-cell level and improve infertility management, however, its true clinical impact is yet to be realized. Lab-on-a-chip devices have generally failed to diffuse into clinical practice due to issues associated with their translation or their practicality and performance in clinical settings. In this perspective, I reflect on how the full potential of microfluidic technologies for fertility can be realized by considering regulatory and manufacturing considerations at the development stage and by redefining our developmental goals to directly target the ultimate clinical needs. I also challenge the common rationale around developing technologies for infertility treatment based on reducing cost and complexity in operation as the ultimate outcome is invaluable, human life.

What advances may the future bring to the diagnosis, treatment, and care of male sexual and reproductive health?

Over the past 40 years, since the publication of the original WHO Laboratory Manual for the Examination and Processing of Human Semen, the laboratory methods used to evaluate semen markedly changed and benefited from improved precision and accuracy, as well as the development of new tests and improved, standardized methodologies. Herein, we present the impact of the changes put forth in the sixth edition together with our views of evolving technologies that may change the methods used for the routine semen analysis, up-and-coming areas for the development of new procedures, and diagnostic approaches that will help to extend the often-descriptive interpretations of several commonly performed semen tests that promise to provide etiologies for the abnormal semen parameters observed. As we look toward the publication of the seventh edition of the manual in approximately 10 years, we describe potential advances that could markedly impact the field of andrology in the future.

Technological Advancements in Male Infertility Microsurgery

There have been significant advancements in male infertility microsurgery over time, and there continues to be significant promise for new and emerging techniques, technologies, and methodologies. In this review, we discuss the history of male infertility and the evolution of microsurgery, the essential role of education and training in male infertility microsurgery, and new technologies in this space. We also review the potentially important role of artificial intelligence (AI) in male infertility and microsurgery.