Artificial Intelligence in Andrology: From Semen Analysis to Image Diagnostics

MRI fingerprints in testes of infertile men with clinical varicocoele: A narrative review

BACKGROUND

Varicocoele represents the most common cause of male infertility, although most men with varicocoele remain fertile. Currently, no specific or sufficiently reliable criteria exist to identify patients who would benefit from varicocoele treatment to prevent impaired spermatogenesis. Emerging evidence suggests that magnetic resonance imaging (MRI) may provide valuable noninvasive insights into the mechanisms of testicular parenchymal damage in infertile men with clinical varicocoele.

OBJECTIVES

The aim of this narrative review was to discuss the applications of advanced MRI techniques, including diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), dynamic contrast-enhanced MRI (DCE-MRI), and proton magnetic resonance spectroscopy (1H-MRS) in the assessment of testes in infertile men with clinical varicocoele and their role in uncovering the complex mechanisms impairing fertility.

MATERIALS AND METHODS

PubMed database was searched for original articles published during 2010-2024.

RESULTS

Advanced MRI techniques reveal alterations in apparent diffusion coefficient (ADC), fractional anisotropy (FA), intratesticular vasculature, and testicular metabolites in men with varicocoele compared to healthy controls. Decrease in testicular ADC and FA is detected in men with varicocoele, indicating microstructural abnormalities. Testes with varicocoele enhance fast and strongly during the early phases of dynamic imaging, providing a valuable insight into tissue perfusion. Proton MR spectra of infertile testes with varicocoele show a decrease in concentrations of choline, myoinositol, Glx complex, and lipids, signifying metabolic signatures of deranged spermatogenesis.

CONCLUSIONS

Preliminary findings from advanced MRI techniques, including DWI, DTI, DCE-MRI, and 1H-MRS have demonstrated potential as noninvasive biomarkers of impaired spermatogenesis, by evaluating testicular microstructure, perfusion, and biochemical milieu.

The perception of artificial intelligence and infertility care among patients undergoing fertility treatment

PURPOSE

To characterize the opinions of patients undergoing infertility treatment on the use of artificial intelligence (AI) in their care.

METHODS

Patients planning or undergoing in vitro fertilization (IVF) or frozen embryo transfers were invited to complete an anonymous electronic survey from April to June 2024. The survey collected demographics, technological affinity, general perception of AI, and its applications to fertility care. Patient-reported trust of AI compared to a physician for fertility care (e.g. gamete selection, gonadotropin doing, and stimulation length) were analyzed. Descriptive statistics were calculated, and subgroup analyses by age, occupation, and parity were performed. Chi-squared tests were used to compare categorical variables.

RESULTS

A total of 200 patients completed the survey and were primarily female (n = 193/200) and of reproductive age (mean 37 years). Patients were well educated with high technological affinity. Respondents were familiar with AI (93%) and generally supported its use in medicine (55%), but fewer trusted AI-informed reproductive care (46%). More patients disagreed (37%) that AI should be used to determine gonadotropin dose or stimulation length compared to embryo selection (26.5%; p = 0.01). In the setting of disagreement between physician and AI recommendation, patients preferred the physician-based recommendation in all treatment-related decisions. However, a larger proportion favored AI recommendations for gamete (22%) and embryo (14.5%) selection, compared to gonadotropin dosing (6.5%) or stimulation length (7.0%). Most would not be willing to pay more for AI-informed fertility care.

CONCLUSIONS

In this highly educated infertile population familiar with AI, patients still prefer physician-based recommendations compared with AI.

Artificial Intelligence in Andrology: A New Frontier in Male Infertility Diagnosis and Treatment

PURPOSE OF REVIEW

Infertility affects approximately 15% of couples globally, with male-factor infertility contributing to about half of these cases. Despite advancements in reproductive medicine, particularly in surgical methods, the prevalence of male infertility remains high and underreported, often due to cultural stigmas. Traditional semen analysis, a crucial component in diagnosing male infertility, involves subjective assessments, leading to variability in results. This review explores the advancements and applications of Artificial Intelligence (AI) in diagnosing and treating male infertility, emphasizing its potential to revolutionize the field by providing reliable and efficient diagnostic tools and improving treatment outcomes.

RECENT FINDINGS

Recent advances in reproductive medicine, including techniques like microdissection testicular sperm extraction and intracytoplasmic sperm injection, have improved conception rates. However, the integration of AI in andrology offers even greater promise. AI techniques, including machine learning and artificial neural networks, now provide automated and objective analysis of sperm motility, and DNA integrity, significantly improving diagnostic precision. These technologies outperform traditional methods by reducing subjectivity in sperm evaluation, identifying subtle abnormalities often missed during manual assessments, and enhancing the selection process for assisted reproductive technologies. Moreover, AI-based predictive models optimize patient selection and personalize treatment protocols, increasing success rates. AI-driven technologies hold transformative potential in the field of reproductive medicine by enhancing the accuracy and efficiency of diagnosing and treating male infertility. The automated and objective analysis offered by AI can offer the possibilities of achieving parenthood for infertile men. However, the implementation of these technologies must be carefully managed, with particular attention to ethical considerations such as bias, transparency, and data privacy. AI's role in advancing reproductive medicine is promising, but responsible deployment is essential to maximize its benefits.

Artificial intelligence and clinical guidance in male reproductive health: ChatGPT4.0's AUA/ASRM guideline compliance evaluation

BACKGROUND

Male infertility is defined as the inability of a male to achieve a pregnancy in a fertile female by the American Urological Association (AUA) and the American Society for Reproductive Medicine (ASRM). Artificial intelligence, particularly in language processing models like ChatGPT4.0, offers new possibilities for supporting clinical decision-making. This study aims to assess the effectiveness of ChatGPT4.0 in responding to clinical queries regarding male infertility, which is aligned with AUA/ASRM guidelines.

METHODS

This observational study employed a design to evaluate the performance of ChatGPT4.0 across 1073 structured clinical queries categorized into true/false, multiple-choice, and open-ended. Two independent reviewers specializing in reproductive medicine assessed the responses using a six-point Likert scale to evaluate accuracy, relevance, and guideline adherence.

RESULTS

In the true/false category, the initial accuracy was 92%, which increased to 94% by the end of the study period. For multiple-choice questions, accuracy improved from 85% to 89%. The most significant gains were seen in open-ended questions, where accuracy rose from 78% to 86%. Initially, some responses did not fully align with the AUA/ASRM guidelines. However, by the end of the 60 days, these responses had become more comprehensive and clinically relevant, indicating an improvement in the model's ability to generate guideline-conformant answers (p < 0.05). The depth and accuracy of responses for higher difficulty questions also showed enhancement (p < 0.01).

CONCLUSION

ChatGPT4.0 can serve as a valuable support tool in managing male infertility, providing reliable, guideline-based information that enhances the accuracy of clinical decision-making tools and supports patient education.

A Stained-Free Sperm Morphology Measurement Method Based on Multi-Target Instance Parsing and Measurement Accuracy Enhancement

Sperm morphology assessment plays a vital role in semen analysis and the diagnosis of male infertility. By quantitatively analyzing the morphological characteristics of the sperm head, midpiece, and tail, it provides essential insights for assisted reproductive technologies (ARTs), such as in vitro fertilization (IVF). However, traditional manual evaluation methods not only rely on staining procedures that can damage the cells but also suffer from strong subjectivity and inconsistent results, underscoring the urgent need for an automated, accurate, and non-invasive method for multi-sperm morphology assessment. To address the limitations of existing techniques, this study proposes a novel method that combines a multi-scale part parsing network with a measurement accuracy enhancement strategy for non-stained sperm morphology analysis. First, a multi-scale part parsing network integrating semantic segmentation and instance segmentation is introduced to achieve instance-level parsing of sperm, enabling precise measurement of morphological parameters for each individual sperm instance. Second, to eliminate measurement errors caused by the reduced resolution of non-stained sperm images, a measurement accuracy enhancement method based on statistical analysis and signal processing is designed. This method employs an interquartile range (IQR) method to exclude outliers, Gaussian filtering to smooth data, and robust correction techniques to extract the maximum morphological features of sperm. Experimental results demonstrate that the proposed multi-scale part parsing network achieves 59.3% APvolp, surpassing the state-of-the-art AIParsing by 9.20%. Compared to evaluations based solely on segmentation results, the integration of the measurement accuracy enhancement strategy significantly reduces measurement errors, with the largest reduction in errors for head, midpiece, and tail measurements reaching up to 35.0%.

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.

Artificial Intelligence in IVF Laboratories: Elevating Outcomes Through Precision and Efficiency

Incorporating artificial intelligence (AI) into in vitro fertilization (IVF) laboratories signifies a significant advancement in reproductive medicine. AI technologies, such as neural networks, deep learning, and machine learning, promise to enhance quality control (QC) and quality assurance (QA) through increased accuracy, consistency, and operational efficiency. This comprehensive review examines the effects of AI on IVF laboratories, focusing on its role in automating processes such as embryo and sperm selection, optimizing clinical outcomes, and reducing human error. AI's data analysis and pattern recognition capabilities offer valuable predictive insights, enhancing personalized treatment plans and increasing success rates in fertility treatments. However, integrating AI also brings ethical, regulatory, and societal challenges, including concerns about data security, algorithmic bias, and the human-machine interface in clinical decision-making. Through an in-depth examination of current case studies, advancements, and future directions, this manuscript highlights how AI can revolutionize IVF by standardizing processes, improving patient outcomes, and advancing the precision of reproductive medicine. It underscores the necessity of ongoing research and ethical oversight to ensure fair and transparent applications in this sensitive field, assuring the responsible use of AI in reproductive medicine.

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.

The Intersection of Artificial Intelligence, Wearable Devices, and Sexual Medicine

PURPOSE OF REVIEW

The aim of our review paper is to provide a comprehensive overview of the current technologies in artificial intelligence and wearable devices dedicated to sexual health.

RECENT FINDINGS

Currently, AI-powered technologies are enhancing our understanding of reproductive health and sexually transmitted infections, and facilitating empathetic education and outreach to diverse populations. Additionally, innovative wearable devices are providing insights into men's erectile health, addressing ejaculatory concerns, and exploring women's orgasms in relation to pelvic floor muscles and clitoral blood flow. The field of sexual health technology is rapidly expanding, with recent innovations transforming our understanding of sexual health. As technology progresses, it is crucial to address significant ethical considerations to protect users, particularly due to the sensitive nature of the data involved.

Machine learning-based classification of varicocoele grading: A promising approach for diagnosis and treatment optimization

BACKGROUND

Varicocoele is a correctable cause of male infertility. Although physical examination is still being used in diagnosis and grading, it gives conflicting results when compared to ultrasonography-based varicocoele grading.

OBJECTIVES

We aimed to develop a multi-class machine learning model for the grading of varicocoeles based on ultrasonographic measurements.

METHOD

Between January and May 2024, we enrolled unilateral varicocoele patients at an infertility clinic, assessing their varicocoele stages using the Dubin and Amelar system. We measured vascular diameter and reflux time at the testicular apex and the subinguinal region ultrasonography in both the supine and standing positions. Using these measurements, we developed four multi-class machine learning models, evaluating their performance metrics and determining which patient position and projection were most influential in varicocoele grading.

RESULTS

We included 248 patients with unilateral varicocoele in the study, their average age was 26.61 ± 4.95 years old. Of these, 212 had left-sided and 36 had right-sided varicocoeles. According to the Dubin and Amelar system, there were 66 grade I, 96 grade II, and 86 grade III varicocoeles. Among the models we created, the random forest (RF) model performed best, with an overall accuracy of 0.81 ± 0.06, an F1 score of 0.79 ± 0.02, a sensitivity of 0.69 ± 0.02, and a specificity of 0.8 ± 0.03. Vascular diameter measurement at the testicular apex in the supine position had the most impact on grading across all models. In support vector machine and multi-layer perceptron models, reflux time measurements from the subinguinal projection in the standing position contributed the most, while in RF and k-nearest neighbors models, measurements from the subinguinal projection in the supine position were the most influential.

CONCLUSIONS

Machine learning methods have demonstrated superior accuracy in predicting disease compared to traditional statistical regressions and nomograms. These advancements hold promise for clinically automated prediction of varicocoele grades in patients. Tailored varicocoele grading for individuals has the potential to enhance treatment effectiveness and overall quality of life.

A narrative review of sperm selection technology for assisted reproduction techniques

Background and Objective

In-vitro fertilization (IVF) with intracytoplasmic sperm injection (ICSI) has become increasingly prevalent even in cases without significant male factor infertility; however, stagnant live-birth rates, both nationally and internationally, have driven more research into sperm selection. To date, nothing has replaced swim-up and density-gradient preparation methods and therefore we sought to review the state of the science.

Methods

A PubMed search was performed between years of 1989 and 2024 for English research articles reporting data on sperm selection technology in assisted reproductive technology.

Key Content and Findings

IVF with ICSI is increasingly prevalent even in men with normal semen parameters. Despite technologic advances and widespread use, reproductive outcomes with ICSI have been stagnant. This market for opportunity growth has allowed for sperm selection techniques to grow exponentially with heterogeneity in utilization and a paucity of positive reproductive outcomes. Swim-up and density-gradient centrifugation remain the most utilized sperm selection techniques. Various future technologies show promise including epigenetics, sperm biomarkers and a potential role of artificial intelligence; however, more research is needed.

Conclusions

Given unchanged IVF success rates, sperm selection technologies hold promise to improve reproductive outcomes beyond traditional ICSI. At present, no technique has shown superiority to swim up and density centrifugation.

The Andrological Landscape in the Twenty-First Century: Making Sense of the Advances in Male Infertility Management for the Busy Clinicians

Male infertility represents a significant global problem due to its essential health, social, and economic implications. It is unsurprising that scientific research is very active in this area and that advances in the diagnostic and therapeutic fields are notable. This review presents the main diagnostic advances in male infertility, starting from the changes made in the latest WHO Manual of semen analysis and discussing the more molecular aspects inherent to "omics". Furthermore, the usefulness of artificial intelligence in male infertility diagnostics and the latest advances in varicocele diagnosis will be discussed. In particular, the diagnostic path of male infertility is increasingly moving towards a personalized approach to the search for the specific biomarkers of infertility and the prediction of treatment response. The treatment of male infertility remains empirical in many regards, but despite that, advances have been made to help formulate evidence-based recommendations. Varicocele, the most common correctable cause of male infertility, has been explored for expanded indications for repair. The following expanded indications were discussed: elevated sperm DNA fragmentation, hypogonadism, orchalgia, and the role of varicocele repair in non-obstructive azoospermia. Moving forward with the available data, we discussed the stepwise approach to surgical sperm retrieval techniques and the current measures that have been investigated for optimizing such patients before testicular sperm extraction. Finally, the key points and expert recommendations regarding the best practice for diagnosing and treating men with infertility were summarized to conclude this review.

Predictive parameters of the efficacy of varicocele repair: a review

ABSTRACT

Varicocele has a prevalence of 15% in the population and represents a primary cause of infertility in 40% of cases and a secondary cause in approximately 80% of cases. It is considered the major correctable cause of male infertility. Despite its high prevalence in the infertile population, a large number of patients with varicocele do not experience reproductive difficulties. For this reason, it is still highly debated which parameters could be used to predict which patients with varicocele will be most likely to benefit from its repair. The main international and European guidelines state that treatment should only be considered in infertile patients with abnormal sperm quality. However, these guidelines do not help physicians to identify which of these patients may benefit from the treatment. Therefore, this narrative review collects the evidence in the literature on the usefulness of some factors as predictors of improvement, highlighting how some of them may be effective in an initial selection of patients to be treated, while others are promising but further studies are needed. Finally, a brief consideration on the possible role of artificial intelligence is proposed.

Diagnostic value of oxidation-reduction potential for male infertility: a systematic review and meta-analysis

Background

In the last few years, studies have initially confirmed the diagnostic significance of oxidation-reduction potential (ORP) in male infertility patients. In this article, we used meta-analysis to clarify the role of ORP in the diagnosis of male infertility.

Methods

PubMed, Embase, Web of Science, and Cochrane Library were searched by computer for relevant published literature. Quality assessment of the included literature was performed by Quality Assessment of Diagnostic Accuracy Studies (QUADAS) scale. Heterogeneity analysis of included studies was conducted using Metadisc 1.4 and Stata 12.0, and effective models for quantitative synthesis were selected based on heterogeneity results; the sensitivity and specificity of the synthesis were obtained using the software, and in order to reduce the effects of heterogeneity and thresholds, the information of sensitivity and specificity was integrated. We used the subject receiver operating characteristic (SROC) curve, area under the curve (AUC) and Q* index for comprehensive evaluation.

Results

Seven papers were eventually included in the study, and the results showed that ORP had a sensitivity of 0.81 [95% confidence interval (CI): 0.80-0.82] and specificity of 0.66 (95% CI: 0.63-0.69), an AUC of 0.8 and a Q* index of 0.74 for the diagnosis of male infertility.

Conclusions

ORP has high sensitivity and specificity for diagnosing male infertility.

Biomarker-based human and animal sperm phenotyping: the good, the bad and the ugly†

Conventional, brightfield-microscopic semen analysis provides important baseline information about sperm quality of an individual; however, it falls short of identifying subtle subcellular and molecular defects in cohorts of "bad," defective human and animal spermatozoa with seemingly normal phenotypes. To bridge this gap, it is desirable to increase the precision of andrological evaluation in humans and livestock animals by pursuing advanced biomarker-based imaging methods. This review, spiced up with occasional classic movie references but seriously scholastic at the same time, focuses mainly on the biomarkers of altered male germ cell proteostasis resulting in post-testicular carryovers of proteins associated with ubiquitin-proteasome system. Also addressed are sperm redox homeostasis, epididymal sperm maturation, sperm-seminal plasma interactions, and sperm surface glycosylation. Zinc ion homeostasis-associated biomarkers and sperm-borne components, including the elements of neurodegenerative pathways such as Huntington and Alzheimer disease, are discussed. Such spectrum of biomarkers, imaged by highly specific vital fluorescent molecular probes, lectins, and antibodies, reveals both obvious and subtle defects of sperm chromatin, deoxyribonucleic acid, and accessory structures of the sperm head and tail. Introduction of next-generation image-based flow cytometry into research and clinical andrology will soon enable the incorporation of machine and deep learning algorithms with the end point of developing simple, label-free methods for clinical diagnostics and high-throughput phenotyping of spermatozoa in humans and economically important livestock animals.

Sperm YOLOv8E-TrackEVD: A Novel Approach for Sperm Detection and Tracking

Male infertility is a global health issue, with 40-50% attributed to sperm abnormalities. The subjectivity and irreproducibility of existing detection methods pose challenges to sperm assessment, making the design of automated semen analysis algorithms crucial for enhancing the reliability of sperm evaluations. This paper proposes a comprehensive sperm tracking algorithm (Sperm YOLOv8E-TrackEVD) that combines an enhanced YOLOv8 small object detection algorithm (SpermYOLOv8-E) with an improved DeepOCSORT tracking algorithm (SpermTrack-EVD) to detect human sperm in a microscopic field of view and track healthy sperm in a sample in a short period effectively. Firstly, we trained the improved YOLOv8 model on the VISEM-Tracking dataset for accurate sperm detection. To enhance the detection of small sperm objects, we introduced an attention mechanism, added a small object detection layer, and integrated the SPDConv and Detect_DyHead modules. Furthermore, we used a new distance metric method and chose IoU loss calculation. Ultimately, we achieved a 1.3% increase in precision, a 1.4% increase in recall rate, and a 2.0% improvement in mAP@0.5:0.95. We applied SpermYOLOv8-E combined with SpermTrack-EVD for sperm tracking. On the VISEM-Tracking dataset, we achieved 74.303% HOTA and 71.167% MOTA. These results show the effectiveness of the designed Sperm YOLOv8E-TrackEVD approach in sperm tracking scenarios.

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.

ChatGPT's Accuracy on Magnetic Resonance Imaging Basics: Characteristics and Limitations Depending on the Question Type

Our study aimed to assess the accuracy and limitations of ChatGPT in the domain of MRI, focused on evaluating ChatGPT's performance in answering simple knowledge questions and specialized multiple-choice questions related to MRI. A two-step approach was used to evaluate ChatGPT. In the first step, 50 simple MRI-related questions were asked, and ChatGPT's answers were categorized as correct, partially correct, or incorrect by independent researchers. In the second step, 75 multiple-choice questions covering various MRI topics were posed, and the answers were similarly categorized. The study utilized Cohen's kappa coefficient for assessing interobserver agreement. ChatGPT demonstrated high accuracy in answering straightforward MRI questions, with over 85% classified as correct. However, its performance varied significantly across multiple-choice questions, with accuracy rates ranging from 40% to 66.7%, depending on the topic. This indicated a notable gap in its ability to handle more complex, specialized questions requiring deeper understanding and context. In conclusion, this study critically evaluates the accuracy of ChatGPT in addressing questions related to Magnetic Resonance Imaging (MRI), highlighting its potential and limitations in the healthcare sector, particularly in radiology. Our findings demonstrate that ChatGPT, while proficient in responding to straightforward MRI-related questions, exhibits variability in its ability to accurately answer complex multiple-choice questions that require more profound, specialized knowledge of MRI. This discrepancy underscores the nuanced role AI can play in medical education and healthcare decision-making, necessitating a balanced approach to its application.

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.

Sperm DNA fragmentation in male infertility: From bench to bedside

Objectives

Sperm DNA fragmentation (SDF) is a molecular marker of sperm chromatin health. Elevated SDF is associated with male infertility, recurrent pregnancy loss, and failure of assisted reproductive technologies (ART). In 2021, the sixth edition of the World Health Organization (WHO) Manual for the Laboratory Examination and Processing of Human Semen has listed SDF as an extended test of semen that can be ordered under certain circumstances. However, the manual neither explained the indications for testing nor provided clear guidance on diagnostic thresholds.

Methods

This article summarizes the current body of knowledge regarding clinical applications of SDF, including the appropriate population to test, methods of testing, and management strategies.

Results

Several etiologic factors and pathophysiologic mechanisms for SDF have been described including poor lifestyle habits, noxious exposures, and varicocele. Four SDF assays are included in the WHO manual and may be utilized based on resources and expertise. Strategies to lower SDF levels in infertile men include addressing underlying causes, supplementation with antioxidants, shorter abstinence periods, and use of testicular sperm for intracytoplasmic sperm injection.

Conclusion

SDF testing can be implemented in the evaluation of infertile men and couples experiencing ART failure and appropriate management strategies can be offered to improve reproductive outcomes. There is vast potential for future research regarding the clinical utility of SDF in the evaluation and treatment of infertile couples.