Can methods of artificial intelligence aid in optimizing patient selection in patients undergoing intrauterine inseminations?

Machine learning in predicting infertility treatment success: A systematic literature review of techniques

Assisted reproductive technology (ART) is one of the major developments that has had a significant impact on infertility treatment. A predictive model of ART success based on machine learning (ML) techniques can provide a robust basis for estimating treatment success. This study aimed to identify predictive models of ART success and their determinants. A systematic search was conducted in PubMed, Web of Science, Scopus, and Embase. Data extraction involved collecting data in studies on dataset characteristics, ML techniques, and predictive model performance indicators. The search resulted in 3655 records, of which 27 papers were selected for analysis. ML publications in ART prediction have been in the past 5 years. In general, 107 various features were reported in all reviewed studies. Female age was the most common feature used in all identified studies. Most studies (96.3%) applied a supervised approach to develop predictive models. Among all, support vector machine (SVM) was the most frequently applied technique (44.44%). Nineteen different indicators have been used in studies to evaluate the model performance. 74.07% of the reviewed papers reported area under the receiver operating characteristic (ROC) curve (AUC) as their performance indicator. Accuracy (55.55%), sensitivity (40.74%), and specificity (25.92%) were also commonly reported. ML has the potential to bring hope to infertile couples and to facilitate making challenging decisions. Considering relevant contributing factors and ML techniques is critical for reliable predictive modeling.

Artificial Intelligence, Clinical Decision Support Algorithms, Mathematical Models, Calculators Applications in Infertility: Systematic Review and Hands-On Digital Applications

The aim of this systematic review was to identify clinical decision support algorithms (CDSAs) proposed for assisted reproductive technologies (ARTs) and to evaluate their effectiveness in improving ART cycles at every stage vs traditional methods, thereby providing an evidence-based guidance for their use in ART practice. A literature search on PubMed and Embase of articles published between 1 January 2013 and 31 January 2024 was performed to identify relevant articles. Prospective and retrospective studies in English on the use of CDSA for ART were included. Out of 1746 articles screened, 116 met the inclusion criteria. The selected articles were categorized into 3 areas: prognosis and patient counseling, clinical management, and embryo assessment. After screening, 11 CDSAs were identified as potentially valuable for clinical management and laboratory practices. Our findings highlight the potential of automated decision aids to improve in vitro fertilization outcomes. However, the main limitation of this review was the lack of standardization in validation methods across studies. Further validation and clinical trials are needed to establish the effectiveness of these tools in the clinical setting.

Artificial Intelligence for Clinical Management of Male Infertility, a Scoping Review

PURPOSE OF REVIEW

Infertility impacts one in six couples worldwide, with male infertility contributing to approximately half of these cases. However, the causes of infertility remain incompletely understood, and current methods of clinical management are cost-restrictive, time-intensive, and have limited success. Artificial intelligence (AI) may help address some of these challenges. In this review, we synthesize recent literature in AI with implications for the clinical management of male infertility.

RECENT FINDINGS

Artificial intelligence may offer opportunities for proactive, cost-effective, and efficient management of male infertility, specifically in the areas of hypogonadism, semen analysis, and interventions such as assisted reproductive technology. Patients may benefit from the integration of AI into a male infertility specialist's clinical workflow. The ability of AI to integrate large volumes of data into predictive models could help clinicians guide conversations with patients on the value of various treatment options in infertility, but caution must be taken to ensure the quality of care being delivered remains high.

Comparative study of machine learning approaches integrated with genetic algorithm for IVF success prediction

INTRODUCTION

IVF is a widely-used assisted reproductive technology with a consistent success rate of around 30%, and improving this rate is crucial due to emotional, financial, and health-related implications for infertile couples. This study aimed to develop a model for predicting IVF outcome by comparing five machine-learning techniques.

METHOD

The research approached five prominent machine learning algorithms, including Random Forest, Artificial Neural Network (ANN), Support Vector Machine (SVM), Recursive Partitioning and Regression Trees (RPART), and AdaBoost, in the context of IVF success prediction. The study also incorporated GA as a feature selection method to enhance the predictive models' robustness.

RESULTS

Findings demonstrate that AdaBoost, particularly when combined with GA feature selection, achieved the highest accuracy rate of 89.8%. Using GA, Random Forest also demonstrated strong performance, achieving an accuracy rate of 87.4%. Genetic Algorithm significantly improved the performance of all classifiers, emphasizing the importance of feature selection. Ten crucial features, including female age, AMH, endometrial thickness, sperm count, and various indicators of oocyte and embryo quality, were identified as key determinants of IVF success.

CONCLUSION

These findings underscore the potential of machine learning and feature selection techniques to assist IVF clinicians in providing more accurate predictions, enabling tailored treatment plans for each patient. Future research and validation can further enhance the practicality and reliability of these predictive models in clinical IVF practice.

Development of a machine learning-based prediction model for clinical pregnancy of intrauterine insemination in a large Chinese population

PURPOSE

This study aimed to evaluate the effectiveness of a random forest (RF) model in predicting clinical pregnancy outcomes from intrauterine insemination (IUI) and identifying significant factors affecting IUI pregnancy in a large Chinese population.

METHODS

RESULTS: A total of 11 variables, including eight from female (age, body mass index, duration of infertility, prior miscarriage, and spontaneous abortion), hormone levels (anti-Müllerian hormone, follicle-stimulating hormone, luteinizing hormone), and three from male (smoking, semen volume, and sperm concentration), were identified as the significant variables associated with IUI clinical pregnancy in our Chinese dataset. The RF-based prediction model presents an area under the receiver operating characteristic curve (AUC) of 0.716 (95% confidence interval, 0.6914-0.7406), an accuracy rate of 0.6081, a sensitivity rate of 0.7113, and a specificity rate of 0.505. Importance analysis indicated that semen volume was the most vital variable in predicting IUI clinical pregnancy.

CONCLUSIONS

The machine learning-based IUI clinical pregnancy prediction model showed a promising predictive efficacy that could provide a potent tool to guide selecting targeted infertile couples beneficial from IUI treatment, and also identify which parameters are most relevant in IUI clinical pregnancy.

DTLR-CS: Deep tensor low rank channel cross fusion neural network for reproductive cell segmentation

In recent years, with the development of deep learning technology, deep neural networks have been widely used in the field of medical image segmentation. U-shaped Network(U-Net) is a segmentation network proposed for medical images based on full-convolution and is gradually becoming the most commonly used segmentation architecture in the medical field. The encoder of U-Net is mainly used to capture the context information in the image, which plays an important role in the performance of the semantic segmentation algorithm. However, it is unstable for U-Net with simple skip connection to perform unstably in global multi-scale modelling, and it is prone to semantic gaps in feature fusion. Inspired by this, in this work, we propose a Deep Tensor Low Rank Channel Cross Fusion Neural Network (DTLR-CS) to replace the simple skip connection in U-Net. To avoid space compression and to solve the high rank problem, we designed a tensor low-ranking module to generate a large number of low-rank tensors containing context features. To reduce semantic differences, we introduced a cross-fusion connection module, which consists of a channel cross-fusion sub-module and a feature connection sub-module. Based on the proposed network, experiments have shown that our network has accurate cell segmentation performance.

Current trends in artificial intelligence in reproductive endocrinology

PURPOSE OF REVIEW

Artificial Intelligence, a tool that integrates computer science and machine learning to mimic human decision-making processes, is transforming the world and changing the way we live. Recently, the healthcare industry has gradually adopted artificial intelligence in many applications and obtained some degree of success. In this review, we summarize the current applications of artificial intelligence in Reproductive Endocrinology, in both laboratory and clinical settings.

RECENT FINDINGS

Artificial Intelligence has been used to select the embryos with high implantation potential, proper ploidy status, to predict later embryo development, and to increase pregnancy and live birth rates. Some studies also suggested that artificial intelligence can help improve infertility diagnosis and patient management. Recently, it has been demonstrated that artificial intelligence also plays a role in effective laboratory quality control and performance.

SUMMARY

In this review, we discuss various applications of artificial intelligence in different areas of reproductive medicine. We summarize the current findings with their potentials and limitations, and also discuss the future direction for research and clinical applications.