Computer-assisted system with multiple feature fused support vector machine for sperm morphology diagnosis

Evolution of intracytoplasmic sperm injection: From initial challenges to wider applications

Background

In vitro fertilization (IVF) has revolutionized infertility treatment. Nevertheless, male infertility requires more effective solutions. In 1992, the first-ever case of human birth via intracytoplasmic sperm injection (ICSI) was reported. ICSI involves microscopically injecting a sperm into an ovum. Successful ICSI has become a reliable therapy for couples facing infertility, a significant milestone. However, it has also introduced various challenges. This study also delves into ethical dilemmas arising from widespread ICSI use.

Methods

This review traces the history of ICSI, presenting pioneering attempts, first successful attempts, and critical reports on account of the initial skepticism toward the technology. The review also focuses on chronological progress until ICSI was recognized as effective and became widely applied.

Main findings

The review reveals that ICSI, although transformative, presents challenges. Successes include addressing male infertility and aiding fertilization. However, concerns arise regarding optimal sperm and embryo selection, genetic mutations, and long-term health implications. Ethical considerations surrounding ICSI's broad applications also surface.

Conclusions

Despite its success and effectiveness, ICSI is still evolving as a therapeutic method. By comprehensively evaluating the historical progress and the current status of ICSI and exploring its future prospects, this study highlights the importance of ICSI in infertility treatment.

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.

Intelligent systems in obstetrics and midwifery: Applications of machine learning

INTRODUCTION

Machine learning is increasingly utilized over recent years in order to develop models that represent and solve problems in a variety of domains, including those of obstetrics and midwifery. The aim of this systematic review was to analyze research studies on machine learning and intelligent systems applications in midwifery and obstetrics.

METHODS

A thorough literature review was performed in four electronic databases (PubMed, APA PsycINFO, SCOPUS, ScienceDirect). Only articles that discussed machine learning and intelligent systems applications in midwifery and obstetrics, were considered in this review. Selected articles were critically evaluated as for their relevance and a contextual synthesis was conducted.

RESULTS

Thirty-two articles were included in this systematic review as they met the inclusion and methodological criteria specified in this study. The results suggest that machine learning and intelligent systems have produced successful models and systems in a broad list of midwifery and obstetrics topics, such as diagnosis, pregnancy risk assessment, fetal monitoring, bladder tumor, etc.

CONCLUSIONS

This systematic review suggests that machine learning represents a very promising area of artificial intelligence for the development of practical and highly effective applications that can support human experts, as well the investigation of a wide range of exciting opportunities for further research.

Machine learning for sperm selection

Infertility rates and the number of couples seeking fertility care have increased worldwide over the past few decades. Over 2.5 million cycles of assisted reproductive technologies are being performed globally every year, but the success rate has remained at ~33%. Machine learning, an automated method of data analysis based on patterns and inference, is increasingly being deployed within the health-care sector to improve diagnostics and therapeutics. This technique is already aiding embryo selection in some fertility clinics, and has also been applied in research laboratories to improve sperm analysis and selection. Tremendous opportunities exist for machine learning to advance male fertility treatments. The fundamental challenge of sperm selection - selecting the most promising candidate from 10⁸ gametes - presents a challenge that is uniquely well-suited to the high-throughput capabilities of machine learning algorithms paired with modern data processing capabilities.

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.

Applying data mining techniques for increasing implantation rate by selecting best sperms for intra-cytoplasmic sperm injection treatment

BACKGROUND AND OBJECTIVE

Aspiration of a good-quality sperm during intracytoplasmic sperm injection (ICSI) is one of the main concerns. Understanding the influence of individual sperm morphology on fertilization, embryo quality, and pregnancy probability is one of the most important subjects in male factor infertility. Embryologists need to decide the best sperm for injection in real time during ICSI cycle. Our objective is to predict the quality of zygote, embryo, and implantation outcome before injection of each sperm in an ICSI cycle for male factor infertility with the aim of providing a decision support system on the sperm selection.

METHODS

The information was collected from 219 patients with male factor infertility at the infertility therapy center of Alzahra hospital in Rasht from 2012 through 2014. The prepared dataset included the quality of zygote, embryo, and implantation outcome of 1544 injected sperms into the related oocytes. In our study, embryo transfer was performed at day 3. Each sperm was represented with thirteen clinical features. Data preprocessing was the first step in the proposed data mining algorithm. After applying more than 30 classifiers, 9 successful classifiers were selected and evaluated by 10-fold cross validation technique using precision, recall, F1, and AUC measures. Another important experiment was measuring the effect of each feature in prediction process.

RESULTS

In zygote and embryo quality prediction, IBK and RandomCommittee models provided 79.2% and 83.8% F1, respectively. In implantation outcome prediction, KStar model achieved 95.9% F1, which is even better than prediction of human experts. All these predictions can be done in real time.

CONCLUSIONS

A machine learning-based decision support system would be helpful in sperm selection phase of ICSI cycle to improve the success rate of ICSI treatment.

A linear-RBF multikernel SVM to classify big text corpora

Support vector machine (SVM) is a powerful technique for classification. However, SVM is not suitable for classification of large datasets or text corpora, because the training complexity of SVMs is highly dependent on the input size. Recent developments in the literature on the SVM and other kernel methods emphasize the need to consider multiple kernels or parameterizations of kernels because they provide greater flexibility. This paper shows a multikernel SVM to manage highly dimensional data, providing an automatic parameterization with low computational cost and improving results against SVMs parameterized under a brute-force search. The model consists in spreading the dataset into cohesive term slices (clusters) to construct a defined structure (multikernel). The new approach is tested on different text corpora. Experimental results show that the new classifier has good accuracy compared with the classic SVM, while the training is significantly faster than several other SVM classifiers.

Transcriptome analysis of medaka following epinecidin-1 and TH1-5 treatment of NNV infection

Nervous necrosis virus (NNV) infects a wide range of larval and juvenile fish species, thereby causing enormous economic losses in the aquaculture industry. Possible solutions to this problem include the use of antimicrobial peptides (AMPs), which directly inhibit bacterial growth, and also modulate host signaling mechanisms. The AMPs epinecidin (Epi)-1 and Tilapia hepcidin (TH) 1-5 have been demonstrated to be effective against Nervous necrosis virus infection in medaka (Oryzias latipes). However, the underlying molecular mechanisms are yet to be explored. Here, microarray analyses were performed to examine how NNV infection and/or epinecidin-1 or TH1-5 treatment affects gene expression in medaka; such analyses enabled the prediction of host signaling pathways affected by virus infection and/or regulated by epinecidin-1 and TH1-5. Transcriptome analysis revealed altered expression of genes involved in B cell activation, T cell activation, adipocytokine signaling, and mast cell activation. We subsequently used real-time PCR to analyze expression of key genes involved in these signaling mechanisms. Medaka infected with NNV exhibited up-regulation of PVALB, CEBPA, IFIM, IFN, IL-6ST, NF-kB2, SOC3, SP1, and TGFB1, and such increases were prevented by pre-treatment with epinecidin-1 or TH1-5. Immunohistochemistry using the anti-NNV antibody to stain brain and eye sections revealed that epinecidin-1 treatment during or after infection clears viral load, while TH1-5 treatment only reduces viral numbers if applied during infection. These observations demonstrate that epinecidin-1 and TH1-5 modulate NNV-induced host signaling mechanisms, thereby preventing viral multiplication in host organisms.