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人工智能和机器学习在精液分析中的应用的最新进展。

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

机构信息

Department of Urology, Tulane University School of Medicine, New Orleans, LA 70112, USA.

Redox Biology & Proteomics Laboratory, Department of Zoology, School of Life Sciences, Ravenshaw University, Cuttack 753003, Odisha, India.

出版信息

Medicina (Kaunas). 2024 Feb 6;60(2):279. doi: 10.3390/medicina60020279.

Abstract

: 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. : 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. : 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. : 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.

摘要

: 在过去的几十年中,不孕率和接受生殖护理的夫妇数量都大幅增加。精液分析是男性不育症诊断和治疗的关键步骤。尽管经过多年的实践和进步,精液分析结果的准确性仍然很差。人工智能 (AI) 算法可以分析和综合大量数据,可以解决由于当前方法学的高度客观性而导致的精液分析中涉及的独特挑战。这篇综述介绍了精液分析中的最新 AI 进展。 : 在 PubMed 数据库中进行了系统的文献检索。排除了非英文文章和与人类无关的研究。我们从原始研究中提取了与用于评估精液参数的 AI 算法或模型相关的数据,排除了摘要、病例报告和会议报告。 : 在确定的 306 篇文章中,初步筛选中排除了 225 篇文章。对其余 81 篇出版物全文的评估又排除了另外 48 篇,最终纳入了这篇综述中的 33 篇原始文章。 : AI 和机器学习在生物医学应用中越来越受欢迎。使用这些算法可以极大地促进男科医生和胚胎学家对精子的检查和选择。此外,随着更大、更可靠的数据集可用于训练,这些算法可能会随着时间的推移而不断改进。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce95/10890589/b465e121bed7/medicina-60-00279-g001.jpg

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