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

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

STUDY QUESTION

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

SUMMARY ANSWER

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

WHAT IS KNOWN ALREADY

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

STUDY DESIGN SIZE DURATION

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

PARTICIPANTS/MATERIALS SETTING METHODS

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

MAIN RESULTS AND THE ROLE OF CHANCE

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

LIMITATIONS REASONS FOR CAUTION

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

WIDER IMPLICATIONS OF THE FINDINGS

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

STUDY FUNDING/COMPETING INTERESTS

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

REGISTRATION NUMBER

N/A.

Artificial intelligence in andrology - fact or fiction: essential takeaway for busy clinicians

ABSTRACT

Artificial intelligence (AI) is revolutionizing the current approach to medicine. AI uses machine learning algorithms to predict the success of therapeutic procedures or assist the clinician in the decision-making process. To date, machine learning studies in the andrological field have mainly focused on prostate cancer imaging and management. However, an increasing number of studies are documenting the use of AI to assist clinicians in decision-making and patient management in andrological diseases such as varicocele or sexual dysfunction. Additionally, machine learning applications are being employed to enhance success rates in assisted reproductive techniques (ARTs). This article offers the clinicians as well as the researchers with a brief overview of the current use of AI in andrology, highlighting the current state-of-the-art scientific evidence, the direction in which the research is going, and the strengths and limitations of this approach.

Intelligent alert system for predicting invasive mechanical ventilation needs via noninvasive parameters: employing an integrated machine learning method with integration of multicenter databases

The use of invasive mechanical ventilation (IMV) is crucial in rescuing patients with respiratory dysfunction. Accurately predicting the demand for IMV is vital for clinical decision-making. However, current techniques are invasive and challenging to implement in pre-hospital and emergency rescue settings. To address this issue, a real-time prediction method utilizing only non-invasive parameters was developed to forecast IMV demand in this study. The model introduced the concept of real-time warning and leveraged the advantages of machine learning and integrated methods, achieving an AUC value of 0.935 (95% CI 0.933-0.937). The AUC value for the multi-center validation using the AmsterdamUMCdb database was 0.727, surpassing the performance of traditional risk adjustment algorithms (OSI(oxygenation saturation index): 0.608, P/F(oxygenation index): 0.558). Feature weight analysis demonstrated that BMI, Gcsverbal, and age significantly contributed to the model's decision-making. These findings highlight the substantial potential of a machine learning real-time dynamic warning model that solely relies on non-invasive parameters to predict IMV demand. Such a model can provide technical support for predicting the need for IMV in pre-hospital and disaster scenarios.

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%.

Cardiac disease prediction using AI algorithms with SelectKBest

Atherosclerotic cardiovascular disease (ASCVD), which includes coronary heart disease (CHD) and ischemic stroke, is the leading cause of mortality globally. According to the European Society of Cardiology (ESC), 26 million people worldwide have heart disease, with 3.6 million diagnosed each year. Early detection of heart disease will aid in lowering the mortality rate. The lack of diversity in training data and the difficulty in comprehending the findings of complicated AI models are the key issues in current research for heart disease prediction using artificial intelligence. To overcome this, in this paper, cardiac disease prediction using AI algorithms with SelectKBest has been proposed. Features are standardized, balanced, and selected using the StandardScaler, SMOTE, and SelectKBest techniques. Machine learning models such as support vector machine (SVM), K-nearest neighbor(KNN), decision tree (DT), logistic regression (LR), adaptive boosting (AB), naive Bayes (NB), random forest (RF), and extra tree (ET) and deep learning models such as vanilla long short-term memory (LSTM), bidirectional long short-term memory (LSTM), stacked long short-term memory (LSTM), and deep neural network (DNN) are assessed using Alizadeh Sani, combined (Cleveland, Hungarian, Switzerland, Long Beach VA, and Stalog), and Pakistan heart failure datasets. As a result of the evaluation, the proposed deep neural network (DNN) with SelectKBest predicted heart disease in a promising way. The prediction rate of unweighted accuracy of 99% on Alizadeh Sani, 98% on combined, and 97% on Pakistan are gained in tenfold cross-validation experiments. The suggested approach can be utilized to diagnose heart disease in its early stages.

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.