The Future Is Coming: Artificial Intelligence in the Treatment of Infertility Could Improve Assisted Reproduction Outcomes-The Value of Regulatory Frameworks

Analysis of use of different rFSHs during IVF/ICSI-assisted conception in elderly population and effect of double trigger on clinical outcomes

OBJECTIVE

To compare the number of oocytes retrieved and clinical outcomes of ovulation induction in an older population treated with in vitro fertilization (IVF)/intracytoplasmic sperm injection (ICSI) (IVF/ICSI) using different rFSH options and the effectiveness of antagonist treatment to induce ovulation using gonadotropin-releasing hormone agonists (GnRH-a) in combination with an human chorionic gonadotropin (HCG) trigger.

METHODS

A total of 132 fresh cycles were selected for this study, which were treated with IVF/ICSI in our hospital from March 2022 to December 2022. Observations were made according to different subgroups and the effects of different triggering methods on the number of oocytes obtained, embryo quality, and clinical outcomes.

RESULTS

The initial gonadotropin (Gn) dose, the number of oocytes, and the number of MII oocytes were higher in group A than in group B (p < .05), and the clinical pregnancy rate was 29.41% in group A. Group B had a clinical pregnancy rate of 27.5%. The double-trigger group was superior to the HCG-trigger group in terms of the number of 2PN, the number of viable embryos, and the number of high-quality embryos (p < .05). The use of a double-trigger regimen (OR = 0.667, 95%CI (0.375, 1.706), p = .024) was a protective factor for the clinical pregnancy rate, whereas AFC (OR = 0.925, 95%CI (0.867, 0.986), p = .017) was an independent factor for the clinical pregnancy rate.

CONCLUSIONS

The use of a dual-trigger regimen of GnRH-a in combination with HCG using an appropriate antagonist improves pregnancy outcomes in fresh embryo transfer cycles in older patients.

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.

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.

Prediction of Mismatch Repair Status in Endometrial Cancer from Histological Slide Images Using Various Deep Learning-Based Algorithms

The application of deep learning algorithms to predict the molecular profiles of various cancers from digital images of hematoxylin and eosin (H&E)-stained slides has been reported in recent years, mainly for gastric and colon cancers. In this study, we investigated the potential use of H&E-stained endometrial cancer slide images to predict the associated mismatch repair (MMR) status. H&E-stained slide images were collected from 127 cases of the primary lesion of endometrial cancer. After digitization using a Nanozoomer virtual slide scanner (Hamamatsu Photonics), we segmented the scanned images into 5397 tiles of 512 × 512 pixels. The MMR proteins (PMS2, MSH6) were immunohistochemically stained, classified into MMR proficient/deficient, and annotated for each case and tile. We trained several neural networks, including convolutional and attention-based networks, using tiles annotated with the MMR status. Among the tested networks, ResNet50 exhibited the highest area under the receiver operating characteristic curve (AUROC) of 0.91 for predicting the MMR status. The constructed prediction algorithm may be applicable to other molecular profiles and useful for pre-screening before implementing other, more costly genetic profiling tests.

Deep multiple instance learning versus conventional deep single instance learning for interpretable oral cancer detection

The current medical standard for setting an oral cancer (OC) diagnosis is histological examination of a tissue sample taken from the oral cavity. This process is time-consuming and more invasive than an alternative approach of acquiring a brush sample followed by cytological analysis. Using a microscope, skilled cytotechnologists are able to detect changes due to malignancy; however, introducing this approach into clinical routine is associated with challenges such as a lack of resources and experts. To design a trustworthy OC detection system that can assist cytotechnologists, we are interested in deep learning based methods that can reliably detect cancer, given only per-patient labels (thereby minimizing annotation bias), and also provide information regarding which cells are most relevant for the diagnosis (thereby enabling supervision and understanding). In this study, we perform a comparison of two approaches suitable for OC detection and interpretation: (i) conventional single instance learning (SIL) approach and (ii) a modern multiple instance learning (MIL) method. To facilitate systematic evaluation of the considered approaches, we, in addition to a real OC dataset with patient-level ground truth annotations, also introduce a synthetic dataset-PAP-QMNIST. This dataset shares several properties of OC data, such as image size and large and varied number of instances per bag, and may therefore act as a proxy model of a real OC dataset, while, in contrast to OC data, it offers reliable per-instance ground truth, as defined by design. PAP-QMNIST has the additional advantage of being visually interpretable for non-experts, which simplifies analysis of the behavior of methods. For both OC and PAP-QMNIST data, we evaluate performance of the methods utilizing three different neural network architectures. Our study indicates, somewhat surprisingly, that on both synthetic and real data, the performance of the SIL approach is better or equal to the performance of the MIL approach. Visual examination by cytotechnologist indicates that the methods manage to identify cells which deviate from normality, including malignant cells as well as those suspicious for dysplasia. We share the code as open source.

Micro/Nanorobotics in In Vitro Fertilization: A Paradigm Shift in Assisted Reproductive Technologies

In vitro fertilization (IVF) has transformed the sector of assisted reproductive technology (ART) by presenting hope to couples facing infertility challenges. However, conventional IVF strategies include their own set of problems such as success rates, invasive procedures, and ethical issues. The integration of micro/nanorobotics into IVF provides a prospect to address these challenging issues. This article provides an outline of the use of micro/nanorobotics in IVF specializing in advancing sperm manipulation, egg retrieval, embryo culture, and capacity future improvements in this swiftly evolving discipline. The article additionally explores the challenges and obstacles associated with the integration of micro/nanorobotics into IVF, in addition to the ethical concerns and regulatory elements related to the usage of advanced technologies in ART. A comprehensive discussion of the risk and safety considerations related to using micro/nanorobotics in IVF techniques is likewise presented. Through this exploration, we delve into the core principles, benefits, challenges, and potential impact of micro/nanorobotics in revolutionizing IVF procedures and enhancing affected person outcomes.

Experiences of men undergoing assisted reproductive technology: A qualitative systematic review

BACKGROUND

Many infertile couples undergo assisted reproductive technology (ART) to increase pregnancy chances, with many of them experiencing psychosocial distress. Although research has been performed on women's experiences of ART, there is limited focus on men.

OBJECTIVE

This systematic review consolidated and synthesized men's experiences with ART to better understand their needs and challenges to support them.

SEARCH STRATEGY

Nine electronic databases were searched from the inception date until November 2022.

SELECTION CRITERIA

This review included published and unpublished primary studies with qualitative methodologies exploring men's experiences with ART.

DATA COLLECTION AND ANALYSIS

The screening of studies, methodological assessment, data extraction, and analysis were conducted by two reviewers independently. The data were thematically synthesized.

MAIN RESULTS

Fifteen studies were included. An overarching theme of "despair to destiny" was identified, with four synthesized themes: (1) "the roller coaster ride," (2) "what made it from bad to worse?", (3) "what kept men going?", and (4) "hopeful for the future."

CONCLUSION

Men undergoing ART experienced struggles, a transition of emotions, and a need for support as they attempted to cope with unknowns while remaining hopeful for future outcomes. There is a need for health care interventions and policies to address the issue to improve the well-being of male ART patients. Interventions should be tailored to the specific support groups that cater to the emotional and informational needs of male ART patients. Future research should focus on the influence of cultural sensitivities on men's ART experiences, to tailor support programs to address their psychological needs during ART.

Estradiol Decline Before hCG Administration in COH Has a Negative Effect on IVF Outcomes in Patients Without OC Pretreatment

Purpose

Together with ultrasound measurement of follicle size, serum estradiol (E2) provides guidance for controlled ovarian hyperstimulation (COH). However, during the COH process, some patients experience decreased serum E2 level, especially before human chorionic gonadotropin (hCG) trigger. In order to elucidate the effect of E2 reduction as well as the role of oral contraceptive pretreatment, a retrospective study was performed in our center from 2013 to 2019.

Patients and Methods

In total, 333 patients who experienced an E2 decrease prior to hCG administration were recruited as E2 decline group, while 333 patients with continuously E2 increase during COH were considered as control group. Based on pretreatment strategy, the two groups were further categorized into oral contraceptive (OC) and non-OC sub-groups, and IVF and clinical outcomes were compared between paired groups.

Results

Number of dominant follicles on hCG day and normally fertilized zygotes were significantly decreased in E2 decline group, and the significantly reduced live birth rate in E2 decline group indicated the close relationship between E2 decline and clinical outcomes. To analyse further, we found that in patients without OC pretreatment, the pregnancy rate and live birth rate of E2 decline group (n = 141) were significantly lower than control group (n = 136) (56.3% versus 68.0%, 50.8% versus 63.5%, respectively). However, for patients with OC pretreatment, no difference was detected between two groups, suggesting a potential effect of OC pretreatment on clinical outcomes.

Conclusion

E2 decline prior to hCG-triggering day adversely affects IVF and clinical outcomes in patients without OC pretreatment, especially fertilization rate and live birth rate.

Preimplantation genetic testing for aneuploidy optimizes reproductive outcomes in recurrent reproductive failure: a systematic review

Introduction

Recurrent reproductive failure (RRF) is a common pregnancy complication, imposing great physical, emotional and financial burden for the suffered couples. The leading cause of RRF is believed to be aneuploid embryo, which could be solved by preimplantation genetic testing for aneuploidy (PGT-A) in theory. With molecular genetic development, PGT-A based on comprehensive chromosomal screening (CCS) procedures and blastocyst biopsy is widely applied in clinical practice. However, its effects in RRF were not defined yet.

Methods

A systematic bibliographical search was conducted without temporal limits up to June, 2023. Studies about the effects of PGT-A based on CCS procedures and blastocyst biopsy in RRF were included.

Results

Twenty studies about the effects of PGT-A based on CCS procedures and blastocyst biopsy in RRF were included. It revealed that PGT-A could optimise the reproductive outcomes of RRF sufferers, especially in those with advanced age. However, in patients with multiple occurrences of pregnancy losses, the benefits of PGT-A were limited.

Discussion

More randomized controlled trials with large sample size are required to evaluate the benefits of PGT-A in RRF sufferers and identify which population would benefit the most.

The mosaic embryo: what it means for the doctor and the patient

INTRODUCTION

Mosaic embryos are embryos that on preimplantation genetic analysis are found to be composed of euploid and aneuploid cells. Although most of these embryos do not implant when transferred into the uterus following IVF treatment, some may implant and are capable of giving rise to babies.

EVIDENCE ACQUISITION

There is currently an increasing number of reports of live births following the transfer of mosaic embryos. Compared to euploid, mosaic embryos have lower implantation rates and higher rates of miscarriage, and occasionally aneuploid component persists. However, their outcome is better than that obtained after the transfer of embryos consisting entirely of aneuploid cells. After implantation, the ability to develop into a full-term pregnancy is influenced by the amount and type of chromosomal mosaicism present in a mosaic embryo. Nowadays many experts in the reproductive field consider mosaic transfers as an option when no euploid embryos are available. Genetic counseling is an important part of educating patients about the likelihood of having a pregnancy with healthy baby but also on the risk that mosaicism could persist and result in liveborn with chromosomal abnormality. Each situation needs to be assessed on a case-by-case basis and counseled accordingly.

EVIDENCE SYNTHESIS

So far, the transfers of 2155 mosaic embryos have been documented and 440 live births resulting in healthy babies have been reported. In addition, in the literature to date, there are 6 cases in which embryonic mosaicism persisted.

CONCLUSIONS

In conclusion, the available data indicate that mosaic embryos have the potential to implant and develop into healthy babies, albeit with lower success rates than euploids. Further clinical outcomes should be collected to better establish a refined ranking of embryos to transfer.

Stem Cells and Infertility: A Review of Clinical Applications and Legal Frameworks

Infertility is a condition defined by the failure to establish a clinical pregnancy after 12 months of regular, unprotected sexual intercourse or due to an impairment of a person's capacity to reproduce either as an individual or with their partner. The authors have set out to succinctly investigate, explore, and assess infertility treatments, harnessing the potential of stem cells to effectively and safely treat infertility; in addition, this paper will present the legal and regulatory complexities at the heart of stem cell research, with an overview of the legislative state of affairs in six major European countries. For couples who cannot benefit from assisted reproductive technologies (ART) to treat their infertility, stem-cells-based approaches have been shown to be a highly promising approach. Nonetheless, lingering ethical and immunological uncertainties require more conclusive findings and data before such treatment avenues can become mainstream and be applied on a large scale. The isolation of human embryonic stem cells (ESCs) is ethically controversial, since their collection involves the destruction of human embryonic tissue. Overall, stem cell research has resulted in important new breakthroughs in the treatment of infertility. The effort to untangle the complex web of ethical and legal issues associated with such therapeutic approaches will have to rely on evidence-based, broadly shared standards, guidelines, and best practices to make sure that the procreative rights of patients can be effectively reconciled with the core values at the heart of medical ethics.

Attitude towards assisted reproductive technology: acceptance of donors eggs, sperms, and embryos as treatment of human infertility: a systematic review and meta-analysis

INTRODUCTION

Assisted Reproductive Technology utilizes human sperm, eggs, or embryos in vitro to produce pregnancy. However, there is no evidence of the acceptance of these technologies by the community.

OBJECTIVE

This study aimed to determine the pooled prevalence of positive attitudes toward the acceptance of donor eggs, embryos, and sperm.

METHODS

The protocol was registered in PROSPERO (number: CRD42022348036). The Condition, Context and Population (CoCoPop) protocol of the systematic review was used to address the relevant questions regarding the objective of the study. Data were extracted into Excel and pooled estimates were calculated using STATA Version 16.

RESULTS

The pooled prevalence of positive attitudes toward accepting donor eggs, embryos, and sperms was 38.63%, 33.20%, and 31.34%, respectively. Subgroup analysis revealed that the pooled prevalence of positive attitudes toward accepting donor eggs was high in non-Asian countries (47.78%) and among infertile men (38.60%). Similarly, the pooled prevalence of positive attitudes toward accepting donor eggs was high in non-Asian countries (47.78%) and among infertile men (28.67%). However, the pooled prevalence of positive attitudes toward accepting donor sperm was high in non-Asian countries (37.6%) and among infertile women (28.19%).

CONCLUSION

The pooled estimate of the prevalence of positive attitudes toward accepting donor eggs was higher than the prevalence of positive attitudes toward accepting donor embryos and sperm. Infertile men and non-Asian countries have a higher prevalence of positive attitudes toward accepting eggs and embryos, whereas non-Asian countries and infertile women present a higher prevalence of positive attitudes toward accepting donor sperm. Therefore, regulatory bodies and policymakers should modify their rules and regulations to ensure the availability of minimum standards for the ethical and safe practice of donor conception as a treatment for infertility at national and international levels.

Effect of Elevated Progesterone Levels on hCG Trigger Day on Clinical Pregnancy Outcome in Short-Acting GnRHa Downregulated Cycles

Background

Previous studies suggested higher serum progesterone (P) levels were strongly associated with a lower clinical pregnancy rate (CPR) for in vitro fertilization-embryo transfer (IVF-ET). However, the effect of increased serum P levels on the day of human chorionic gonadotropin (hCG) administration on clinical outcomes in short-acting gonadotropin-releasing hormone agonist (GnRHa) downregulated IVF-ET cycles remains unclear.

Methods

We conducted a retrospective cohort study from January 2017 to December 2021, which included a total of 1664 patients receiving their first short-acting GnRHa IVF-ET cycles at our reproductive medicine centre of Nanjing Drum Tower Hospital. The smooth curve fitting and interaction analysis were employed to analyse the association between the CPR and the serum P levels with different embryo types (cleavage-stage embryo or blastocyst). In addition, total cycles were grouped according to different P levels on the trigger day of hCG administration for further analysis.

Results

The CPR of patients with increased serum P level (higher than 1.5 ng/mL) on the hCG day did not decrease. A smoothing curve fitting showed that the CPR did not change obviously with the increase in serum P levels. Subgroup analysis of different types of embryos transferred showed that no correlation was observed between the CPR and serum P levels on the day of hCG administration in cleavage-stage embryo transfer cycles. However, the CPR of patients receiving blastocyst transfer showed a downward trend with the increase in serum P levels. At the same time, an interaction analysis also confirmed that the CPR of blastocyst transfer was more likely to be affected by elevated serum P levels on the hCG day.

Conclusion

In the luteal phase short-acting GnRHa downregulated IVF-ET cycles, the elevated serum P levels on the hCG day did not affect the CPR of cleavage-stage embryo transfer but reduced the CPR of blastocyst transfer.

Development of a Predictive Model for Optimization of Embryo Transfer Timing Using Blood-Based microRNA Expression Profile

MicroRNAs (miRNAs) can regulate the expression of genes involved in the establishment of the window of implantation (WOI) in the endometrium. Recent studies indicated that cell-free miRNAs in uterine fluid and blood samples could act as alternative and non-invasive sample types for endometrial receptivity analysis. In this study, we attempt to systematically evaluate whether the expression levels of cell-free microRNAs in blood samples could be used as non-invasive biomarkers for assessing endometrial receptivity status. We profiled the miRNA expression levels of 111 blood samples using next-generation sequencing to establish a predictive model for the assessment of endometrial receptivity status. This model was validated with an independent dataset (n = 73). The overall accuracy is 95.9%. Specifically, we achieved accuracies of 95.9%, 95.9%, and 100.0% for the pre-receptive group, the receptive group, and the post-respective group, respectively. Additionally, we identified a set of differentially expressed miRNAs between different endometrial receptivity statuses using the following criteria: p-value < 0.05 and fold change greater than 1.5 or less than -1.5. In conclusion, the expression levels of cell-free miRNAs in blood samples can be utilized in a non-invasive manner to distinguish different endometrial receptivity statuses.

A hybrid feature selection algorithm to determine effective factors in predictive model of success rate for in vitro fertilization/intracytoplasmic sperm injection treatment: A cross-sectional study

Background

Previous research has identified key factors affecting in vitro fertilization or intracytoplasmic sperm injection success, yet the lack of a standardized approach for various treatments remains a challenge.

Objective

The objective of this study is to utilize a machine learning approach to identify the principal predictors of success in in vitro fertilization and intracytoplasmic sperm injection treatments.

Materials and Methods

We collected data from 734 individuals at 2 infertility centers in Mashhad, Iran between November 2016 and March 2017. We employed feature selection methods to reduce dimensionality in a random forest model, guided by hesitant fuzzy sets (HFSs). A hybrid approach enhanced predictor identification and accuracy (ACC), as assessed using machine learning metrics such as Matthew's correlation coefficient, runtime, ACC, area under the receiver operating characteristic curve, precision or positive predictive value, recall, and F-Score, demonstrating the effectiveness of combining feature selection methods.

Results

Our hybrid feature selection method excelled with the highest ACC (0.795), area under the receiver operating characteristic curve (0.72), and F-Score (0.8), while selecting only 7 features. These included follicle-stimulation hormone (FSH), 16Cells, FAge, oocytes, quality of transferred embryos (GIII), compact, and unsuccessful.

Conclusion

We introduced HFSs in our novel method to select influential features for predicting infertility success rates. Using a multi-center dataset, HFSs improved feature selection by reducing the number of features based on standard deviation among criteria. Results showed significant differences between pregnant and non-pregnant groups for selected features, including FSH, FAge, 16Cells, oocytes, GIII, and compact. We also found a significant correlation between FAge and fetal heart rate and clinical pregnancy rate, with the highest FSH level (31.87%) observed for doses ranging from 10-13 (mIU/ml).

Global, regional and national burden of male infertility in 204 countries and territories between 1990 and 2019: an analysis of global burden of disease study

BACKGROUND

Many countries and regions have experienced male fertility problems due to various influencing factors, especially in less developed countries. Unlike female infertility, male infertility receives insufficient attention. Understanding the changing patterns of male infertility in the world, different regions and different countries is crucial for assessing the global male fertility and reproductive health.

METHODS

We obtained data on prevalence, years of life lived with disability (YLD), age-standardized rates of prevalence (ASPR) and age-standardized YLD rate (ASYR) from the Global Burden of Disease Study 2019. We analyzed the burden of male infertility at all levels, including global, regional, national, age stratification and Socio-demographic Index (SDI).

RESULTS

In 2019, the global prevalence of male infertility was estimated to be 56,530.4 thousand (95% UI: 31,861.5-90,211.7), reflecting a substantial 76.9% increase since 1990. Furthermore, the global ASPR stood at 1,402.98 (95% UI: 792.24-2,242.45) per 100,000 population in 2019, representing a 19% increase compared to 1990. The regions with the highest ASPR and ASYR for male infertility in 2019 were Western Sub-Saharan Africa, Eastern Europe, and East Asia. Notably, the prevalence and YLD related to male infertility peaked in the 30-34 year age group worldwide. Additionally, the burden of male infertility in the High-middle SDI and Middle SDI regions exceeded the global average in terms of both ASPR and ASYR.

CONCLUSION

The global burden of male infertility has exhibited a steady increase from 1990 to 2019, as evidenced by the rising trends in ASPR and ASYR, particularly in the High-middle and Middle SDI regions. Notably, the burden of male infertility in these regions far exceeds the global average. Additionally, since 2010, there has been a notable upward trend in the burden of male infertility in Low and Middle-low SDI regions. Given these findings, it is imperative to prioritize efforts aimed at improving male fertility and reproductive health.

The influence of lifestyle interventions and overweight on infertility: a systematic review, meta-analysis, and meta-regression of randomized controlled trials

This study aimed to investigate the effect of lifestyle intervention (LSI) on diagnosed infertility in overweight and obese women. A systematic review and meta-analysis were conducted. A literature search was performed on the following databases from September 2022 to December 2022: PubMed, Web of Science, and SPORTDiscus. The inclusion criteria were the following: women between 18 and 45 years of age, BMI over 25.0 kg/m2, diagnosed with infertility, a weight loss intervention, and control group part of RCTs. In total, 15 studies were identified and included. The meta-analysis shows a beneficial effect of LSI on reducing weight, waist circumference, and BMI and increasing infertility. A significantly beneficial effect of lifestyle intervention on weight reduction was observed for participants who initially had a higher BMI, while a non-significant effect was observed for individuals with a BMI above 35 kg/m2. The meta-analysis showed a beneficial effect of lifestyle intervention on ovulation incidence and sex hormone-binding globulin. The lifestyle intervention group had 11.23 times more ovulatory incidence than the control group, which in turn increased the ability to conceive. As robust evidence for the effect of lifestyle interventions on infertility in obese and overweight women was found, it is advised to integrate similar interventions into future infertility treatment processes.

Investigating the biochemical association of gestational diabetes mellitus with dyslipidemia and hemoglobin

Aims

To investigate the biochemical correlation of hemoglobin (Hb), dyslipidemia, and HbA1c with gestational diabetes mellitus (GDM).

Background

GDM is a condition that develops during pregnancy and is characterized by high blood sugar levels. Biochemical parameters such as hemoglobin (Hb), dyslipidemia, and HbA1c have been implicated in the development of GDM. Understanding the correlation between these biochemical parameters and GDM can provide insights into the underlying mechanisms and potential diagnostic markers for the condition.

Objective

The objective of this study was to evaluate the correlation of various biochemical parameters, including Hb, dyslipidemia, and HbA1c, in pregnant women with and without GDM.

Method

A cross-sectional study design was used. Pregnant females attending a tertiary care hospital in Faisalabad between September 1st, 2021, and June 25th, 2022, were included in the study. The participants were divided into two groups: those with GDM (GDM group) and those without GDM (non-GDM group). Blood glucose, Hb, and lipid levels were compared between the two groups using statistical tests, including chi-square, independent sample t-test, and Pearson's correlation.

Result

Out of the 500 participants, 261 were in the 2nd trimester and 239 in the 3rd trimester. Maternal age showed a significant difference between the GDM and non-GDM groups. The levels of Hb, TC, HDL, LDL, and HbA1c significantly differed (p < 0.05) between the two groups. TC (r = 0.397), TG (r = 0.290), and LDL (r = 0.509) showed a statistically significant and moderately positive correlation with GDM. HDL (r = -0.394) and Hb (r = -0.294) showed a moderate negative correlation with GDM.

Conclusion

Increased levels of HbA1c, TC, and LDL, along with decreased levels of HDL and Hb, were identified as contributing factors to GDM. The levels of TC, TG, and LDL were positively correlated with GDM, while HDL and Hb were negatively correlated. The findings of this study suggest that monitoring and managing hemoglobin, dyslipidemia, and HbA1c levels during pregnancy may be important in identifying and potentially preventing or managing GDM. Further research is needed to explore the underlying mechanisms and potential interventions targeting these biochemical parameters in relation to GDM.

Potential factors result in diminished ovarian reserve: a comprehensive review

The ovarian reserve is defined as the quantity of oocytes stored in the ovary or the number of oocytes that can be recruited. Ovarian reserve can be affected by many factors, including hormones, metabolites, initial ovarian reserve, environmental problems, diseases, and medications, among others. With the trend of postponing of pregnancy in modern society, diminished ovarian reserve (DOR) has become one of the most common challenges in current clinical reproductive medicine. Attributed to its unclear mechanism and complex clinical features, it is difficult for physicians to administer targeted treatment. This review focuses on the factors associated with ovarian reserve and discusses the potential influences and pathogenic factors that may explain the possible mechanisms of DOR, which can be improved or built upon by subsequent researchers to verify, replicate, and establish further study findings, as well as for scientists to find new treatments.

Combined oral antibiotics and intrauterine perfusion can improve in vitro fertilization and embryo transfer pregnancy outcomes in patients with chronic endometritis and repeated embryo implantation failure

BACKGROUND

The aim of this retrospective study was to investigate whether oral antibiotics (doxycycline and metronidazole) combined with intrauterine perfusion (gentamicin and dexamethasone) are beneficial for patients with repeated implantation failure (RIF) and chronic endometritis (CE) to improve clinical pregnancy outcomes.

METHODS

Patients with RIF and CE were diagnosed using hysteroscopy and histology together. A total of 42 patients were enrolled in the study. All patients received oral antibiotics (doxycycline combined with metronidazole) and 22 patients underwent intrauterine perfusion (gentamicin combined with dexamethasone) immediately after the end of oral antibiotic therapy. Pregnancy outcomes were evaluated during the first in vitro fertilization (IVF) and embryo transfer (ET) cycle.

RESULTS

For the first D3 ET after treatment with oral antibiotics (doxycycline and metronidazole) combined with intrauterine perfusion (gentamicin and dexamethasone), higher embryo implantation rate (30.95% vs. 26.67%, P = 0.0308), clinical pregnancy rate (30% vs. 50%, P < 0.001), live birth rate (33.33% vs. 45.45%, P < 0.0001). No fetal malformations or ectopic pregnancies were observed.

CONCLUSION

We report oral antibiotics (doxycycline and metronidazole) combined with intrauterine perfusion (gentamicin and dexamethasone) as a novel treatment for CE to improve the outcomes of successful pregnancy compared with those of oral antibiotics alone.

University students' fertility awareness and its influencing factors: a systematic review

INTRODUCTION

In recent years, a growing number of researchers have begun to study fertility awareness (FA). Evidence suggests that college students in their reproductive years have a common understanding of fertility, risk factors for infertility, and assisted reproductive technologies. Therefore, this systematic review summarizes these studies and explores the factors affecting college students' fertility awareness.

METHODS

A systematic literature search of databases (PUBMED/MEDLINE, Cochrane, Web of Science, Embase, and EBSCO) was conducted from inception to September 2022. Studies that assessed the levels of fertility awareness and factors influencing college students were considered for the review. The qualities of the included studies were evaluated using the Strengthening the Reporting of Observational Studies in Epidemiology guidelines. This systematic review is reported according to the preferred reporting items for systematic review (PRISMA) guidelines.

RESULTS

Twenty-one articles met the eligibility criteria and were included. The preliminary results showed that participants reported low to moderate FA. Female medical students demonstrated higher levels of fertility awareness. The association between age, years of education, and FA was insufficient.

CONCLUSION

The results of the current study suggest that increased FA interventions are warranted, especially for the male, non-medical student population. Governments and educational institutions should strengthen education programs for young students on reproductive health to help them raise awareness about childbirth, and society should provide family support for young people.

Machine learning applied in maternal and fetal health: a narrative review focused on pregnancy diseases and complications

Introduction

Machine learning (ML) corresponds to a wide variety of methods that use mathematics, statistics and computational science to learn from multiple variables simultaneously. By means of pattern recognition, ML methods are able to find hidden correlations and accomplish accurate predictions regarding different conditions. ML has been successfully used to solve varied problems in different areas of science, such as psychology, economics, biology and chemistry. Therefore, we wondered how far it has penetrated into the field of obstetrics and gynecology.

Aim

To describe the state of art regarding the use of ML in the context of pregnancy diseases and complications.

Methodology

Publications were searched in PubMed, Web of Science and Google Scholar. Seven subjects of interest were considered: gestational diabetes mellitus, preeclampsia, perinatal death, spontaneous abortion, preterm birth, cesarean section, and fetal malformations.

Current state

ML has been widely applied in all the included subjects. Its uses are varied, the most common being the prediction of perinatal disorders. Other ML applications include (but are not restricted to) biomarker discovery, risk estimation, correlation assessment, pharmacological treatment prediction, drug screening, data acquisition and data extraction. Most of the reviewed articles were published in the last five years. The most employed ML methods in the field are non-linear. Except for logistic regression, linear methods are rarely used.

Future challenges

To improve data recording, storage and update in medical and research settings from different realities. To develop more accurate and understandable ML models using data from cutting-edge instruments. To carry out validation and impact analysis studies of currently existing high-accuracy ML models.

Conclusion

The use of ML in pregnancy diseases and complications is quite recent, and has increased over the last few years. The applications are varied and point not only to the diagnosis, but also to the management, treatment, and pathophysiological understanding of perinatal alterations. Facing the challenges that come with working with different types of data, the handling of increasingly large amounts of information, the development of emerging technologies, and the need of translational studies, it is expected that the use of ML continue growing in the field of obstetrics and gynecology.

Unboxing Industry-Standard AI Models for Male Fertility Prediction with SHAP

Infertility is a social stigma for individuals, and male factors cause approximately 30% of infertility. Despite this, male infertility is underrecognized and underrepresented as a disease. According to the World Health Organization (WHO), changes in lifestyle and environmental factors are the prime reasons for the declining rate of male fertility. Artificial intelligence (AI)/machine learning (ML) models have become an effective solution for early fertility detection. Seven industry-standard ML models are used: support vector machine, random forest (RF), decision tree, logistic regression, naïve bayes, adaboost, and multi-layer perception to detect male fertility. Shapley additive explanations (SHAP) are vital tools that examine the feature's impact on each model's decision making. On these, we perform a comprehensive comparative study to identify good and poor classification models. While dealing with the all-above-mentioned models, the RF model achieves an optimal accuracy and area under curve (AUC) of 90.47% and 99.98%, respectively, by considering five-fold cross-validation (CV) with the balanced dataset. Furthermore, we provide the SHAP explanations of existing models that attain good and poor performance. The findings of this study show that decision making (based on ML models) with SHAP provides thorough explanations for detecting male fertility, as well as a reference for clinicians for further treatment planning.

Precision Medicine for Chronic Endometritis: Computer-Aided Diagnosis Using Deep Learning Model

Chronic endometritis (CE) is a localized mucosal infectious and inflammatory disorder marked by infiltration of CD138(+) endometrial stromal plasmacytes (ESPC). CE is drawing interest in the field of reproductive medicine because of its association with female infertility of unknown etiology, endometriosis, repeated implantation failure, recurrent pregnancy loss, and multiple maternal/newborn complications. The diagnosis of CE has long relied on somewhat painful endometrial biopsy and histopathologic examinations combined with immunohistochemistry for CD138 (IHC-CD138). With IHC-CD138 only, CE may be potentially over-diagnosed by misidentification of endometrial epithelial cells, which constitutively express CD138, as ESPCs. Fluid hysteroscopy is emerging as an alternative, less-invasive diagnostic tool that can visualize the whole uterine cavity in real-time and enables the detection of several unique mucosal findings associated with CE. The biases in the hysteroscopic diagnosis of CE; however, are the inter-observer and intra-observer disagreements on the interpretation of the endoscopic findings. Additionally, due to the variances in the study designs and adopted diagnostic criteria, there exists some dissociation in the histopathologic and hysteroscopic diagnosis of CE among researchers. To address these questions, novel dual immunohistochemistry for CD138 and another plasmacyte marker multiple myeloma oncogene 1 are currently being tested. Furthermore, computer-aided diagnosis using a deep learning model is being developed for more accurate detection of ESPCs. These approaches have the potential to contribute to the reduction in human errors and biases, the improvement of the diagnostic performance of CE, and the establishment of unified diagnostic criteria and standardized clinical guidelines for the disease.

Polymorphic Variants of Genes Encoding Angiogenesis-Related Factors in Infertile Women with Recurrent Implantation Failure

Recurrent implantation failure (RIF) is a global health issue affecting a significant number of infertile women who undergo in vitro fertilization (IVF) cycles. Extensive vasculogenesis and angiogenesis occur in both maternal and fetal placental tissues, and vascular endothelial growth factor (VEGF) and fibroblast growth factor (FGF) family molecules and their receptors are potent angiogenic mediators in the placenta. Five single nucleotide polymorphisms (SNPs) in the genes encoding angiogenesis-related factors were selected and genotyped in 247 women who had undergone the ART procedure and 120 healthy controls. Genotyping was conducted by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). A variant of the kinase insertion domain receptor (KDR) gene (rs2071559) was associated with an increased risk of infertility after adjusting for age and BMI (OR = 0.64; 95% CI: 0.45-0.91, p = 0.013 in a log-additive model). Vascular endothelial growth factor A (VEGFA) rs699947 was associated with an increased risk of recurrent implantation failures under a dominant (OR = 2.34; 95% CI: 1.11-4.94, p_adj. = 0.022) and a log-additive model (OR = 0.65; 95% CI 0.43-0.99, p_adj. = 0.038). Variants of the KDR gene (rs1870377, rs2071559) in the whole group were in linkage equilibrium (D' = 0.25, r² = 0.025). Gene-gene interaction analysis showed the strongest interactions between the KDR gene SNPs rs2071559-rs1870377 (p = 0.004) and KDR rs1870377-VEGFA rs699947 (p = 0.030). Our study revealed that the KDR gene rs2071559 variant may be associated with infertility and rs699947 VEGFA with an increased risk of recurrent implantation failures in infertile ART treated Polish women.

Unraveling the harmful effect of oxidative stress on male fertility: A mechanistic insight

Male infertility is a widely debated issue that affects males globally. There are several mechanisms involved. Oxidative stress is accepted to be the main contributing factor, with sperm quality and quantity affected by the overproduction of free radicals. Excess reactive oxygen species (ROS) cannot be controlled by the antioxidant system and, thus, potentially impact male fertility and hamper sperm quality parameters. Mitochondria are the driving force of sperm motility; irregularities in their function may lead to apoptosis, alterations to signaling pathway function, and, ultimately, compromised fertility. Moreover, it has been observed that the prevalence of inflammation may arrest sperm function and the production of cytokines triggered by the overproduction of ROS. Further, oxidative stress interacts with seminal plasma proteomes that influence male fertility. Enhanced ROS production disturbs the cellular constituents, particularly DNA, and sperms are unable to impregnate the ovum. Here, we review the latest information to better understand the relationship between oxidative stress and male infertility, the role of mitochondria, the cellular response, inflammation and fertility, and the interaction of seminal plasma proteomes with oxidative stress, as well as highlight the influence of oxidative stress on hormones; collectively, all of these factors are assumed to be important for the regulation of male infertility. This article may help improve our understanding of male infertility and the strategies to prevent it.