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.