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

Abstract

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.

Exploring altered bovine sperm trajectories by sperm tracking in unconfined conditions

“…The aim was to provide additional information on sperm motility in contexts different from the standard semen analysis, supporting the CASA system outcome. As a matter of fact, automated analysis -like artificial intelligence (AI) approaches- is becoming increasingly popular in fertility research ( 58 ). Therefore, having a larger dataset of descriptive sperm parameters could be beneficial to further characterize sperm behavior.…”

Diagnostic value of oxidation-reduction potential for male infertility: a systematic review and meta-analysis

“…Despite some progress made with semen analysis techniques, like computer-assisted sperm analysis (CASA), the results of these new techniques are highly variable, the investment in equipment maintenance and manual training is expensive, and the accuracy is not significantly improved compared to traditional manual semen analysis ( 34 - 36 ). In addition, the predictive power of single semen analysis is relatively poor, mainly due to differences in individual semen parameters ( 37 , 38 ). In addition to the vulnerability of semen measurements to laboratory methods and subjective human error ( 9 , 15 , 39 ), semen parameters are associated with external environmental and lifestyle factors that may change over time in individuals, making single semen analysis an unreliable indicator ( 40 , 41 ).…”

ChatGPT’s Efficacy in Queries Regarding Polycystic Ovary Syndrome and Treatment Strategies for Women Experiencing Infertility

“…It offers significant potential in aggregating and analyzing vast research data, thereby identifying effective treatment paradigms and novel therapeutic targets. Specifically, in the context of ovulation induction, it can play a pivotal role in investigating treatment protocols for individual patient profiles, thus maximizing the chances of successful pregnancy while minimizing the risks of treatment-related complications [ 12 , 13 ].…”