Future of Male Infertility Evaluation and Treatment: Brief Review of Emerging Technology

Chu, Kevin Y.; Diaz, Parris; Dullea, Alexandra; Zizzo, John; Loloi, Justin; Reddy, Rohit; Li, Philip S.; Ramasamy, Ranjith

doi:10.1016/j.urology.2022.06.036

Cited by 10 publications

(4 citation statements)

References 46 publications

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“…In terms of CASA, the potential for advancements in result accuracy and improved portability is an avenue for AI to be involved in, which will hopefully decrease inter-and intra-operative errors [32]. The financial aspect of CASA also becomes a barrier of entry for many patients looking for solutions for their male infertility, so further efforts to reduce costs and increase affordability are warranted [63]. Another avenue for semen analysis that needs to be further explored is performing semen analyses in the comfort of the home.…”

Section: Future Directionsmentioning

confidence: 99%

The Role of Artificial Intelligence in Male Infertility: Evaluation and Treatment: A Narrative Review

Venishetty,

Alkassis,

Raheem

2024

Uro

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Male infertility has affected an increasingly large population over the past few decades, affecting over 186 million people globally. The advent of assisted reproductive technologies (ARTs) and artificial intelligence (AI) has changed the landscape of diagnosis and treatment of male infertility. Through an extensive literature review encompassing the PubMed, Google Scholar, and Scopus databases, various AI techniques such as machine learning (ML), artificial neural networks (ANNs), deep learning (DL), and natural language processing (NLP) were examined in the context of evaluating seminal quality, predicting fertility potential, and improving semen analysis. Research indicates that AI models can accurately estimate the quality of semen, diagnose problems with sperm, and provide guidance on reproductive health decisions. In addition, developments in smartphone-based semen analyzers and computer-assisted semen analysis (CASA) are indicative of initiatives to improve the price, portability, and accuracy of results. Future directions point to possible uses for AI in ultrasonography assessment, microsurgical testicular sperm extraction (microTESE), and home-based semen analysis. Overall, AI holds significant promise in revolutionizing the diagnosis and treatment of male infertility, offering standardized, objective, and efficient approaches to addressing this global health challenge.

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Section: Future Directionsmentioning

confidence: 99%

The Role of Artificial Intelligence in Male Infertility: Evaluation and Treatment: A Narrative Review

Venishetty,

Alkassis,

Raheem

2024

Uro

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“…Currently, in the scientific literature, there are not many review documents that have specifically addressed the use of machine learning models for predicting male infertility. A recent review [24] addressed the topic in a non-systematic and narrative way. The authors [24] drew up examples of possible applications of ML algorithms in a hospital setting, providing indications on robots and mechanics in vogue for specialized analyses, such as Advancing Computer-Assisted Semen Analyses (CASA), in the investigation of preoperative images before surgical sperm retrieval, for intra-operative advancement to identify genetic material at a microscopic level and sperm identification and analysis.…”

Section: Introductionmentioning

confidence: 99%

“…A recent review [24] addressed the topic in a non-systematic and narrative way. The authors [24] drew up examples of possible applications of ML algorithms in a hospital setting, providing indications on robots and mechanics in vogue for specialized analyses, such as Advancing Computer-Assisted Semen Analyses (CASA), in the investigation of preoperative images before surgical sperm retrieval, for intra-operative advancement to identify genetic material at a microscopic level and sperm identification and analysis. This review, although concise and well argued, has the limitation of being focused on assisted reproductive technologies without any specific reference to machine learning models.…”

Section: Introductionmentioning

confidence: 99%

Predicting Male Infertility Using Artificial Neural Networks: A Review of the Literature

Schmeis Arroyo,

Iosa,

Antonucci

et al. 2024

Healthcare

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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%.

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“…Analysis of sperm kinetic parameters using computer-assisted sperm analysis (CASA) are widely used to evaluate the quality of sperm from wild species (Van der Horst, 2021), stallion, bull, boar (Amann and Waberski, 2014), human (Diaz et al, 2022), and fishes (Gallego and Asturiano, 2018). Nowadays, sperm motility is the best quality biomarker of fish spermatozoa (Gallego and Asturiano, 2019).…”

Section: Discussionmentioning

confidence: 99%

Development and application of low-cost and environment-friendly techniques for fish sperm cryopreservation

de Souza França

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Fish sperm cryopreservation is a technique that can increase the reproduction in captive efficiency of freshwater and marine fishes. The technique is applied in environmental conservation programs and aquaculture. Over the last few decades, protocols for sperm cryopreservation from many fishes have been established. However, the researchers' main focus was successfully freezing and thawing sperm, neglecting the period in which the gametes remain in contact with the cryoprotective solution until fertilization. Exposure of sperm to cryoprotectant solutions after thawing can be harmful to the quality of the gametes since they can be toxic.The vast majority of the established protocols use ultra-resistant plastic containers to store sperm during the cryopreservation process. These containers usually are not reused, generating highly polluting waste for the environment. Furthermore, in some countries, the containers usually used are sold by a few industries, which makes acquisition difficult and increases the product's price. Thus, the main objective of the thesis was to create and test low-cost methodologies that enhance the use of fish post-thaw sperm and make the sperm cryopreservation process more environmentally friendly.The experiments in the Chapters 1 and 2 were developed in Brazil. We used the South American silver catfish Rhamdia quelen, a species considered an experimental model for native South American fishes. In vitro fertilization and sperm cryopreservation protocols for the species were already established; thus, we based our work on these protocols to apply innovations.In Chapter 1, we tested the use of post-thawing dilution to reduce the toxicity of the cryoprotectant solution. This technique is commonly used in mammalian sperm cryopreservation protocols but has never before been applied to post-thaw sperm of South American fishes. South American silver catfish post-thaw sperm samples were diluted in a saline extender (1.1% NaCl -325 mOsm kg -1 ; pH 7.6). The post-thaw sperm diluted samples showed higher velocities, straightness, progression, and flagellar beat frequency than SUMMARY 2 the cells of undiluted samples (control). The post-thawing dilution also provided higher fertilization and hatching rates than the control group. Thus, the post-thawing sperm dilution proved to be a simple, cheap, and efficient methodology that should be included in the silver catfish sperm cryopreservation protocol.

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Future of Male Infertility Evaluation and Treatment: Brief Review of Emerging Technology

Cited by 10 publications

References 46 publications

The Role of Artificial Intelligence in Male Infertility: Evaluation and Treatment: A Narrative Review

The Role of Artificial Intelligence in Male Infertility: Evaluation and Treatment: A Narrative Review

Predicting Male Infertility Using Artificial Neural Networks: A Review of the Literature

Development and application of low-cost and environment-friendly techniques for fish sperm cryopreservation

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