Semen parameters can be predicted from environmental factors and lifestyle using artificial intelligence methods

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.

Prediction of childbearing tendency in women on the verge of marriage using machine learning techniques

The declining fertility rate and increasing marriage age among girls pose challenges for policymakers, leading to issues such as population decline, higher social and economic costs, and reduced labor productivity. Using machine learning (ML) techniques to predict the desire to have children can offer a promising solution to address these challenges. Therefore, this study aimed to predict the childbearing tendency in women on the verge of marriage using ML techniques. Data from 252 participants (203 expressing a "desire to have children" and 49 indicating "reluctance to have children") in Abadan, and Khorramshahr cities (Khuzestan Province, Iran) was analyzed. Seven ML algorithms, including multilayer perceptron (MLP), support vector machine (SVM), logistic regression (LR), random forest (RF), J48 decision tree, Naive Bayes (NB), and K-nearest neighbors (KNN), were employed. The performance of these algorithms was assessed using metrics derived from the confusion matrix. The RF algorithm showed superior performance, with the highest sensitivity (99.5%), specificity (95.6%), and receiver operating characteristic curve (90.1%) values. Meanwhile, MLP emerged as the top-performing algorithm, showcasing the best overall performance in accuracy (77.75%) and precision (81.8%) compared to other algorithms. Factors such as age of marriage, place of residence, and strength of the family center with the birth of a child were the most effective predictors of a woman's desire to have children. Conversely, the number of daughters, the wife's ethnicity, and the spouse's ownership of assets such as cars and houses were among the least important factors in predicting this desire. ML algorithms exhibit excellent predictive capabilities for childbearing tendencies in women on the verge of marriage, highlighting their remarkable effectiveness. This capacity to offer accurate prognoses holds significant promise for advancing research in this field.

The prediction of semen quality based on lifestyle behaviours by the machine learning based models

PURPOSE

To find the machine learning (ML) method that has the highest accuracy in predicting the semen quality of men based on basic questionnaire data about lifestyle behavior.

METHODS

The medical records of men whose semen was analyzed for any reason were collected. Those who had data about their lifestyle behaviors were included in the study. All semen analyses of the men included were evaluated according to the WHO 2021 guideline. All semen analyses were categorized as normozoospermia, oligozoospermia, teratozoospermia, and asthenozoospermia. The Extra Trees Classifier, Average (AVG) Blender, Light Gradient Boosting Machine (LGBM) Classifier, eXtreme Gradient Boosting (XGB) Classifier, Logistic Regression, and Random Forest Classifier techniques were used as ML algorithms.

RESULTS

Seven hundred thirty-four men who met the inclusion criteria and had data about lifestyle behavior were included in the study. 356 men (48.5%) had abnormal semen results, 204 (27.7%) showed the presence of oligozoospermia, 193 (26.2%) asthenozoospermia, and 265 (36.1%) teratozoospermia according to the WHO 2021. The AVG Blender model had the highest accuracy and AUC for predicting normozoospermia and teratozoospermia. The Extra Trees Classifier and Random Forest Classifier models achieved the best performance for predicting oligozoospermia and asthenozoospermia, respectively.

CONCLUSION

The ML models have the potential to predict semen quality based on lifestyles.

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%.

Current Updates on Involvement of Artificial Intelligence and Machine Learning in Semen Analysis

Background and Objectives: Infertility rates and the number of couples undergoing reproductive care have both increased substantially during the last few decades. Semen analysis is a crucial step in both the diagnosis and the treatment of male infertility. The accuracy of semen analysis results remains quite poor despite years of practice and advancements. Artificial intelligence (AI) algorithms, which can analyze and synthesize large amounts of data, can address the unique challenges involved in semen analysis due to the high objectivity of current methodologies. This review addresses recent AI advancements in semen analysis. Materials and Methods: A systematic literature search was performed in the PubMed database. Non-English articles and studies not related to humans were excluded. We extracted data related to AI algorithms or models used to evaluate semen parameters from the original studies, excluding abstracts, case reports, and meeting reports. Results: Of the 306 articles identified, 225 articles were rejected in the preliminary screening. The evaluation of the full texts of the remaining 81 publications resulted in the exclusion of another 48 articles, with a final inclusion of 33 original articles in this review. Conclusions: AI and machine learning are becoming increasingly popular in biomedical applications. The examination and selection of sperm by andrologists and embryologists may benefit greatly from using these algorithms. Furthermore, when bigger and more reliable datasets become accessible for training, these algorithms may improve over time.

Aflatoxin B1 induces infertility, fetal deformities, and potential therapies

Aflatoxin B1 (AFB1) is a subsidiary poisonous metabolite, archetypally spawned by Aspergillus flavus and A. parasiticus, which are often isolated in warm or tropical countries across the world. AFB1 is capable of disrupting the functioning of several reproductive endocrine glands by interrupting the enzymes and their substrates that are liable for the synthesis of various hormones in both males and females. In men, AFB1 is capable of hindering testicular development, testicular degeneration, and reduces reproductive capabilities. In women, a direct antagonistic interaction of AFB1 with steroid hormone receptors influencing gonadal hormone production of estrogen and progesterone was responsible for AFB1-associated infertility. AFB1 is potentially teratogenic and is responsible for the development of malformation in humans and animals. Soft-tissue anomalies such as internal hydrocephalus, microphthalmia, cardiac defects, augmented liver lobes, reproductive changes, immune modifications, behavioral changes and predisposition of animals and humans to neoplasm development are AFB1-associated anomalies. Substances such as esculin, selenium, gynandra extract, vitamins C and E, oltipraz, and CDDO-Im are potential therapies for AFB1. Thus, this review elucidates the pivotal pathogenic roles of AFB1 in infertility, fetal deformities, and potential therapies because AFB1 toxicity is a key problem globally.

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.

Can in vitro embryo production be estimated from semen variables in Senepol breed by using artificial intelligence?

Thoroughly analyzing the sperm and exploring the information obtained using artificial intelligence (AI) could be the key to improving fertility estimation. Artificial neural networks have already been applied to calculate zootechnical indices in animals and predict fertility in humans. This method of estimating the results of reproductive biotechnologies, such as in vitro embryo production (IVEP) in cattle, could be valuable for livestock production. This study was developed to model IVEP estimates in Senepol animals based on various sperm attributes, through retrospective data from 290 IVEP routines performed using 38 commercial doses of semen from Senepol bulls. All sperm samples that had undergone the same procedure during sperm selection for in vitro fertilization were evaluated using a computer-assisted sperm analysis (CASA) system to define sperm subpopulations. Sperm morphology was also analyzed in a wet preparation, and the integrity of the plasma and acrosomal membranes, mitochondrial potential, oxidative status, and chromatin resistance were evaluated using flow cytometry. A previous study identified three sperm subpopulations in such samples and the information used in tandem with other sperm quality variables to perform an AI analysis. AI analysis generated models that estimated IVEP based on the season, donor, percentage of viable oocytes, and 18 other sperm predictor variables. The accuracy of the results obtained for the three best AI models for predicting the IVEP was 90.7, 75.3, and 79.6%, respectively. Therefore, applying this AI technique would enable the estimation of high or low embryo production for individual bulls based on the sperm analysis information.

Trend of change of sperm count and concentration over the last two decades: A systematic review and meta-regression analysis

BACKGROUND

Since the 1970s, several studies found that sperm concentration (SC) and total sperm count (TSC) constantly worsened over time, mainly in high-income countries.

OBJECTIVES

To evaluate whether the decreasing trend in sperm count is continuing in Western European countries and USA, we performed a systematic review and meta-regression analysis.

MATERIALS AND METHODS

Embase and Pubmed/Medline were searched papers published in English in the 2000-2020 period limiting the search to data collected in the USA and Western European countries.

RESULTS

We identified 62 articles and pooled information on 24,196 men (range 10-2,523), collected from 1993 to 2018. Considering all the studies, random-effects meta-regression analyses showed no significant trend for SC (slope per year -0.07 mil/mL, p-value = 0.86). Negative trends of SC were detected in Scandinavian countries (slope per year -1.11 mil/mL, 95% CI: -2.40 to +0.19; p-value = 0.09), but the findings were statistically not significant. No significant trends of SC were detected in Central Europe (slope per year +0.23, 95% CI -2.51 to +2.96; p-value = 0.87), the USA (slope per year +1.08, 95% CI -0.42 to +2.57; p-value = 0.16), and Southern Europe (slope per year +0.19, 95% CI -0.99 to +1.37; p-value = 0.75). We have analyzed separately findings from studies including sperm donors, fertile men, young unselected men (unselected men, study mean age < 25 years) and unselected men (unselected men, study mean age ≥ 25 years). No significant trends of SC were observed among sperm donors (slope per year -2.80, 95% CI -6.76 to +1.17; p-value 0.16), unselected men (slope per year -0.23, 95% CI -1.58 to +1.12; p-value 0.73), young unselected men (slope per year -0.49, 95% CI -1.76 to +0.79; p-value 0.45), fertile men (slope per year +0.29, 95% CI -1.09 to +1.67; p-value 0.68).

DISCUSSION AND CONCLUSION

The results of this analysis show no significant trends in SC, in USA, and selected Western European countries.

AI Tools for Assessing Human Fertility Using Risk Factors: A State-of-the-Art Review

Infertility has massively disrupted social and marital life, resulting in stressful emotional well-being. Early diagnosis is the utmost need for faster adaption to respond to these changes, which makes possible via AI tools. Our main objective is to comprehend the role of AI in fertility detection since we have primarily worked to find biomarkers and related risk factors associated with infertility. This paper aims to vividly analyse the role of AI as an effective method in screening, predicting for infertility and related risk factors. Three scientific repositories: PubMed, Web of Science, and Scopus, are used to gather relevant articles via technical terms: (human infertility OR human fertility) AND risk factors AND (machine learning OR artificial intelligence OR intelligent system). In this way, we systematically reviewed 42 articles and performed a meta-analysis. The significant findings and recommendations are discussed. These include the rising importance of data augmentation, feature extraction, explainability, and the need to revisit the meaning of an effective system for fertility analysis. Additionally, the paper outlines various mitigation actions that can be employed to tackle infertility and its related risk factors. These insights contribute to a better understanding of the role of AI in fertility analysis and the potential for improving reproductive health outcomes.

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.

Temporal trends in sperm count: a systematic review and meta-regression analysis of samples collected globally in the 20th and 21st centuries

BACKGROUND

Numerous studies have reported declines in semen quality and other markers of male reproductive health. Our previous meta-analysis reported a significant decrease in sperm concentration (SC) and total sperm count (TSC) among men from North America-Europe-Australia (NEA) based on studies published during 1981-2013. At that time, there were too few studies with data from South/Central America-Asia-Africa (SAA) to reliably estimate trends among men from these continents.

OBJECTIVE AND RATIONALE

The aim of this study was to examine trends in sperm count among men from all continents. The broader implications of a global decline in sperm count, the knowledge gaps left unfilled by our prior analysis and the controversies surrounding this issue warranted an up-to-date meta-analysis.

SEARCH METHODS

We searched PubMed/MEDLINE and EMBASE to identify studies of human SC and TSC published during 2014-2019. After review of 2936 abstracts and 868 full articles, 44 estimates of SC and TSC from 38 studies met the protocol criteria. Data were extracted on semen parameters (SC, TSC, semen volume), collection year and covariates. Combining these new data with data from our previous meta-analysis, the current meta-analysis includes results from 223 studies, yielding 288 estimates based on semen samples collected 1973-2018. Slopes of SC and TSC were estimated as functions of sample collection year using simple linear regression as well as weighted meta-regression. The latter models were adjusted for predetermined covariates and examined for modification by fertility status (unselected by fertility versus fertile), and by two groups of continents: NEA and SAA. These analyses were repeated for data collected post-2000. Multiple sensitivity analyses were conducted to examine assumptions, including linearity.

OUTCOMES

Overall, SC declined appreciably between 1973 and 2018 (slope in the simple linear model: -0.87 million/ml/year, 95% CI: -0.89 to -0.86; P < 0.001). In an adjusted meta-regression model, which included two interaction terms [time × fertility group (P = 0.012) and time × continents (P = 0.058)], declines were seen among unselected men from NEA (-1.27; -1.78 to -0.77; P < 0.001) and unselected men from SAA (-0.65; -1.29 to -0.01; P = 0.045) and fertile men from NEA (-0.50; -1.00 to -0.01; P = 0.046). Among unselected men from all continents, the mean SC declined by 51.6% between 1973 and 2018 (-1.17: -1.66 to -0.68; P < 0.001). The slope for SC among unselected men was steeper in a model restricted to post-2000 data (-1.73: -3.23 to -0.24; P = 0.024) and the percent decline per year doubled, increasing from 1.16% post-1972 to 2.64% post-2000. Results were similar for TSC, with a 62.3% overall decline among unselected men (-4.70 million/year; -6.56 to -2.83; P < 0.001) in the adjusted meta-regression model. All results changed only minimally in multiple sensitivity analyses.

WIDER IMPLICATIONS

This analysis is the first to report a decline in sperm count among unselected men from South/Central America-Asia-Africa, in contrast to our previous meta-analysis that was underpowered to examine those continents. Furthermore, data suggest that this world-wide decline is continuing in the 21st century at an accelerated pace. Research on the causes of this continuing decline and actions to prevent further disruption of male reproductive health are urgently needed.

The use of machine learning methods to predict sperm quality in Holstein bulls

The aim of this study was to develop prediction models for total sperm motility, morphological abnormalities and sperm output based on 1,551 ejaculate records of 58 Holstein bulls. The data was collected from September 2019 to November 2020 in a single artificial insemination (AI) center located in Eastern Germany. Factors considered for the prediction models include barn climate conditions, semen collector, number of false mounts, libido, semen collection frequency, breed and age (10-74 months). In this study, the prediction models Lasso, Group Lasso and Gradient Boosting were evaluated. The best model for each sperm quality parameter was chosen using cross validation. The models were estimated with five algorithms for sperm motility and sperm morphology and three algorithms for the number of total sperm per ejaculate (sperm output). For sperm motility and morphology a binary classification algorithm was applied, reaching an accuracy of over 80% for all models. For sperm output, no such classification was used and the only variable selected by all three algorithms was age. Furthermore, for sperm morphology, climate variables were frequently selected. Additionally, network diagrams from Group Lasso show the interdependencies between the major variable groups influencing sperm motility and morphology. In conclusion, the implementation of such prediction tools could help AI centers to optimize management factors and stabilize bull semen production in the future.

Artificial Intelligence in Reproductive Medicine - An Ethical Perspective

Artificial intelligence is steadily being integrated into all areas of medicine. In reproductive medicine, artificial intelligence methods can be utilized to improve the selection and prediction of sperm cells, oocytes, and embryos and to generate better predictive models for in vitro fertilization. The use of artificial intelligence in this field is justified by the suffering of persons or couples who wish to have children but are unable to conceive. However, research into the use of artificial intelligence in reproductive medicine is still in the early experimental stage and furthermore raises complex normative questions. There are ethical research challenges because evidence of the efficacy of certain pertinent systems is often lacking and because of the increased difficulty of ensuring informed consent on the part of the affected persons. Other ethically relevant issues include the potential risks for offspring and the difficulty of providing sufficient information. The opportunity to fulfill the desire to have children affects the welfare of patients and their reproductive autonomy. Ultimately, ensuring more accurate predictions and allowing physicians to devote more time to their patients will have a positive effect. Nevertheless, clinicians must be able to process patient data conscientiously. When using artificial intelligence, numerous actors are involved in making the diagnosis and deciding on the appropriate therapy, raising questions about who is ultimately responsible when mistakes occur. Questions of fairness arise with regard to resource allocation and cost reimbursement. Thus, before implementing artificial intelligence in clinical practice, it is necessary to critically examine the quantity and quality of the data used and to address issues of transparency. In the medium and long term, it would be necessary to confront the undesirable impact and social dynamics that may accompany the use of artificial intelligence in reproductive medicine.

Preliminary prediction of semen quality based on modifiable lifestyle factors by using the XGBoost algorithm

Introduction

Semen quality has decreased gradually in recent years, and lifestyle changes are among the primary causes for this issue. Thus far, the specific lifestyle factors affecting semen quality remain to be elucidated.

Materials and methods

In this study, data on the following factors were collected from 5,109 men examined at our reproductive medicine center: 10 lifestyle factors that potentially affect semen quality (smoking status, alcohol consumption, staying up late, sleeplessness, consumption of pungent food, intensity of sports activity, sedentary lifestyle, working in hot conditions, sauna use in the last 3 months, and exposure to radioactivity); general factors including age, abstinence period, and season of semen examination; and comprehensive semen parameters [semen volume, sperm concentration, progressive and total sperm motility, sperm morphology, and DNA fragmentation index (DFI)]. Then, machine learning with the XGBoost algorithm was applied to establish a primary prediction model by using the collected data. Furthermore, the accuracy of the model was verified via multiple logistic regression following k-fold cross-validation analyses.

Results

The results indicated that for semen volume, sperm concentration, progressive and total sperm motility, and DFI, the area under the curve (AUC) values ranged from 0.648 to 0.697, while the AUC for sperm morphology was only 0.506. Among the 13 factors, smoking status was the major factor affecting semen volume, sperm concentration, and progressive and total sperm motility. Age was the most important factor affecting DFI. Logistic combined with cross-validation analysis revealed similar results. Furthermore, it showed that heavy smoking (>20 cigarettes/day) had an overall negative effect on semen volume and sperm concentration and progressive and total sperm motility (OR = 4.69, 6.97, 11.16, and 10.35, respectively), while age of >35 years was associated with increased DFI (OR = 5.47).

Conclusion

The preliminary lifestyle-based model developed for semen quality prediction by using the XGBoost algorithm showed potential for clinical application and further optimization with larger training datasets.

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.

A systematic review of the applications of Expert Systems (ES) and machine learning (ML) in clinical urology

BACKGROUND

Testing a hypothesis for 'factors-outcome effect' is a common quest, but standard statistical regression analysis tools are rendered ineffective by data contaminated with too many noisy variables. Expert Systems (ES) can provide an alternative methodology in analysing data to identify variables with the highest correlation to the outcome. By applying their effective machine learning (ML) abilities, significant research time and costs can be saved. The study aims to systematically review the applications of ES in urological research and their methodological models for effective multi-variate analysis. Their domains, development and validity will be identified.

METHODS

The PRISMA methodology was applied to formulate an effective method for data gathering and analysis. This study search included seven most relevant information sources: WEB OF SCIENCE, EMBASE, BIOSIS CITATION INDEX, SCOPUS, PUBMED, Google Scholar and MEDLINE. Eligible articles were included if they applied one of the known ML models for a clear urological research question involving multivariate analysis. Only articles with pertinent research methods in ES models were included. The analysed data included the system model, applications, input/output variables, target user, validation, and outcomes. Both ML models and the variable analysis were comparatively reported for each system.

RESULTS

The search identified n = 1087 articles from all databases and n = 712 were eligible for examination against inclusion criteria. A total of 168 systems were finally included and systematically analysed demonstrating a recent increase in uptake of ES in academic urology in particular artificial neural networks with 31 systems. Most of the systems were applied in urological oncology (prostate cancer = 15, bladder cancer = 13) where diagnostic, prognostic and survival predictor markers were investigated. Due to the heterogeneity of models and their statistical tests, a meta-analysis was not feasible.

CONCLUSION

ES utility offers an effective ML potential and their applications in research have demonstrated a valid model for multi-variate analysis. The complexity of their development can challenge their uptake in urological clinics whilst the limitation of the statistical tools in this domain has created a gap for further research studies. Integration of computer scientists in academic units has promoted the use of ES in clinical urological research.

A novel fitness function in genetic programming for medical data classification

In the last decade, machine learning (ML) techniques have been widely applied to identify different diseases. This facilitates an early diagnosis and increases the chance of survival. The majority of medical data-sets are unbalanced. Due to this, ML classification techniques give biased classification over the majority class. In this paper, a novel fitness function in Genetic Programming, for medical data classification has been proposed that handles the problem of unbalanced data. Four benchmark medical data-sets named chronic kidney disease (CKD), fertility, BUPA liver disorder, and Wisconsin diagnostic breast cancer (WDBC) have been taken from the University of California (UCI) machine learning repository. Classification is done using the proposed technique. The proposed technique achieved the best accuracy for CKD, WDBC, Fertility, and BUPA dataset as 100%, 99.12%, 85.0%, and 75.36% respectively, and the best AUC as 1.0, 0.99, 0.92, and 0.75 respectively. The result outcomes show an improvement over other GP and SVM methods that confirm the efficiency of our proposed algorithm.

Artificial intelligence in the IVF laboratory: overview through the application of different types of algorithms for the classification of reproductive data

Over the past years, the assisted reproductive technologies (ARTs) have been accompanied by constant innovations. For instance, intracytoplasmic sperm injection (ICSI), time-lapse monitoring of the embryonic morphokinetics, and PGS are innovative techniques that increased the success of the ART. In the same trend, the use of artificial intelligence (AI) techniques is being intensively researched whether in the embryo or spermatozoa selection. Despite several studies already published, the use of AI within assisted reproduction clinics is not yet a reality. This is largely due to the different AI techniques that are being proposed to be used in the daily routine of the clinics, which causes some uncertainty in their use. To shed light on this complex scenario, this review briefly describes some of the most frequently used AI algorithms, their functionalities, and their potential use. Several databases were analyzed in search of articles where applied artificial intelligence algorithms were used on reproductive data. Our focus was on the classification of embryonic cells and semen samples. Of a total of 124 articles analyzed, 32 were selected for this review. From the proposed algorithms, most have achieved a satisfactory precision, demonstrating the potential of a wide range of AI techniques. However, the evaluation of these studies suggests the need for more standardized research to validate the proposed models and their algorithms. Routine use of AI in assisted reproduction clinics is just a matter of time. However, the choice of AI technique to be used is supported by a better understanding of the principles subjacent to each technique, that is, its robustness, pros, and cons. We provide some current (although incipient) and potential uses of AI on the clinic routine, discussing how accurate and friendly it could be. Finally, we propose some standards for AI research on the selection of the embryo to be transferred and other future hints. For us, the imminence of its use is evident, providing a revolutionary milestone that will impact the ART.

A Machine Learning and Integration Based Architecture for Cognitive Disorder Detection Used for Early Autism Screening

About 15% of the world’s population suffers from some form of disability. In developed countries, about 1.5% of children are diagnosed with autism. Autism is a developmental disorder distinguished mainly by impairments in social interaction and communication and by restricted and repetitive behavior. Since the cause of autism is still unknown, there have been many studies focused on screening for autism based on behavioral features. Thus, the main purpose of this paper is to present an architecture focused on data integration and analytics, allowing the distributed processing of input data. Furthermore, the proposed architecture allows the identification of relevant features as well as of hidden correlations among parameters. To this end, we propose a methodology able to integrate diverse data sources, even data that are collected separately. This methodology increases the data variety which can lead to the identification of more correlations between diverse parameters. We conclude the paper with a case study that used autism data in order to validate our proposed architecture, which showed very promising results.

Artificial intelligence in reproductive medicine

Artificial intelligence (AI) has experienced rapid growth over the past few years, moving from the experimental to the implementation phase in various fields, including medicine. Advances in learning algorithms and theories, the availability of large datasets and improvements in computing power have contributed to breakthroughs in current AI applications. Machine learning (ML), a subset of AI, allows computers to detect patterns from large complex datasets automatically and uses these patterns to make predictions. AI is proving to be increasingly applicable to healthcare, and multiple machine learning techniques have been used to improve the performance of assisted reproductive technology (ART). Despite various challenges, the integration of AI and reproductive medicine is bound to give an essential direction to medical development in the future. In this review, we discuss the basic aspects of AI and machine learning, and we address the applications, potential limitations and challenges of AI. We also highlight the prospects and future directions in the context of reproductive medicine.

Big data analytics for preventive medicine

Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations.

The Effect of Green Software: A Study of Impact Factors on the Correctness of Software

Unfortunately, sustainability is an issue very poorly used when developing software and hardware systems. Lately, and in order to contribute to the earth sustainability, a new concept emerged named Green software which is computer software that can be developed and used efficiently and effectively with minimal or no impact to the environment. Currently, new teaching methods based on students’ learning process are being developed in the European Higher Education Area. Most of them are oriented to promote students’ interest in the course’s contents and offer personalized feedback. Online judging is a promising method for encouraging students’ participation in the e-learning process, although it still has to be researched and developed to be widely used and in a more efficient way. The great amount of data available in an online judging tool provides the possibility of exploring some of the most indicative attributes (e.g., running time, memory) for learning programming concepts, techniques and languages. So far, the most applied methods for automatically gathering information from the judging systems are based on statistical methods and, although providing reasonable correlations, these methods have not been proven to provide enough information for predicting grades when dealing with a huge amount of data. Therefore, the great novelty of this paper is to develop a data mining approach to predict program correctness as well as the grades of the students’ practices. For this purpose, powerful data mining technologies taken from the artificial intelligence domain have been used. In particular, in this study, we have used logistic regression, decision trees, artificial neural network and support vector machines; which have been properly identified as the most suitable ones for predicting activities in the e-learning domains. The results have achieved an accuracy of around 74%, both in the prediction of the program correctness as well as in the practice grades’ prediction. Another relevant issue provided in this paper is a comparison among these four techniques to obtain the best accuracy in predicting grades based on the availability of data as well as their taxonomy. The Decision Trees classifier has obtained the best confusion matrix, and time and memory efficiency were identified as the most important predictor variables. In view of these results, we can conclude that the development of green software leads programmers to implement correct software.

Temporal trends in sperm count: a systematic review and meta-regression analysis

BACKGROUND

Reported declines in sperm counts remain controversial today and recent trends are unknown. A definitive meta-analysis is critical given the predictive value of sperm count for fertility, morbidity and mortality.

OBJECTIVE AND RATIONALE

To provide a systematic review and meta-regression analysis of recent trends in sperm counts as measured by sperm concentration (SC) and total sperm count (TSC), and their modification by fertility and geographic group.

SEARCH METHODS

PubMed/MEDLINE and EMBASE were searched for English language studies of human SC published in 1981-2013. Following a predefined protocol 7518 abstracts were screened and 2510 full articles reporting primary data on SC were reviewed. A total of 244 estimates of SC and TSC from 185 studies of 42 935 men who provided semen samples in 1973-2011 were extracted for meta-regression analysis, as well as information on years of sample collection and covariates [fertility group ('Unselected by fertility' versus 'Fertile'), geographic group ('Western', including North America, Europe Australia and New Zealand versus 'Other', including South America, Asia and Africa), age, ejaculation abstinence time, semen collection method, method of measuring SC and semen volume, exclusion criteria and indicators of completeness of covariate data]. The slopes of SC and TSC were estimated as functions of sample collection year using both simple linear regression and weighted meta-regression models and the latter were adjusted for pre-determined covariates and modification by fertility and geographic group. Assumptions were examined using multiple sensitivity analyses and nonlinear models.

OUTCOMES

SC declined significantly between 1973 and 2011 (slope in unadjusted simple regression models -0.70 million/ml/year; 95% CI: -0.72 to -0.69; P < 0.001; slope in adjusted meta-regression models = -0.64; -1.06 to -0.22; P = 0.003). The slopes in the meta-regression model were modified by fertility (P for interaction = 0.064) and geographic group (P for interaction = 0.027). There was a significant decline in SC between 1973 and 2011 among Unselected Western (-1.38; -2.02 to -0.74; P < 0.001) and among Fertile Western (-0.68; -1.31 to -0.05; P = 0.033), while no significant trends were seen among Unselected Other and Fertile Other. Among Unselected Western studies, the mean SC declined, on average, 1.4% per year with an overall decline of 52.4% between 1973 and 2011. Trends for TSC and SC were similar, with a steep decline among Unselected Western (-5.33 million/year, -7.56 to -3.11; P < 0.001), corresponding to an average decline in mean TSC of 1.6% per year and overall decline of 59.3%. Results changed minimally in multiple sensitivity analyses, and there was no statistical support for the use of a nonlinear model. In a model restricted to data post-1995, the slope both for SC and TSC among Unselected Western was similar to that for the entire period (-2.06 million/ml, -3.38 to -0.74; P = 0.004 and -8.12 million, -13.73 to -2.51, P = 0.006, respectively).

WIDER IMPLICATIONS

This comprehensive meta-regression analysis reports a significant decline in sperm counts (as measured by SC and TSC) between 1973 and 2011, driven by a 50-60% decline among men unselected by fertility from North America, Europe, Australia and New Zealand. Because of the significant public health implications of these results, research on the causes of this continuing decline is urgently needed.

Protein profile screening: reduced expression of Sord in the mouse epididymis induced by nicotine inhibits tyrosine phosphorylation level in capacitated spermatozoa

Many studies have revealed the hazardous effects of cigarette smoking and nicotine exposure on male fertility, but the actual, underlying molecular mechanism remains relatively unclear. To evaluate the detrimental effects of nicotine exposure on the sperm maturation process, two-dimensional gel electrophoresis and mass spectrometry analyses were performed to screen and identify differentially expressed proteins from the epididymal tissue of mice exposed to nicotine. Data mining analysis indicated that 15 identified proteins were mainly involved in the molecular transportation process and the polyol pathway, indicating impaired epididymal secretory functions. Experiments in vitro confirmed that nicotine inhibited tyrosine phosphorylation levels in capacitated spermatozoa via the downregulated seminal fructose concentration. Sord, a key gene encoding sorbitol dehydrogenase, was further investigated to reveal that nicotine induced hyper-methylation of the promoter region of this gene. Nicotine-induced reduced expression of Sord could be involved in impaired secretory functions of the epididymis and thus prevent the sperm from undergoing proper maturation and capacitation, although further experiments are needed to confirm this hypothesis.