Jiyun Pang, Wei Hou, Yuxiang Nong, Ang Bian, Wenming Xu
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引用次数: 0
Abstract
Infertility is a global health issue, and more and more people are hoping to have babies by means of assisted reproductive technology. However, there are still many challenges in fertilization and pregnancy outcomes. Sperm quality is a key factor affecting the success rate of assisted reproduction. Therefore, sperm quality screening is crucial for achieving breakthroughs in assisted reproduction technology. At present, with its capabilities in the field of image recognition, artificial intelligence (AI) is providing new ideas and methods for sperm screening. Various attempts have been made with AI-based models to evaluate indicators such as sperm morphology, DNA quality, and motility level, and some results have been achieved. Herein, we reviewed the application of AI in sperm quality analysis and selection, providing support for the future development of AI and the improvement in the fertilization rate and outcomes of assisted reproductive technology.
四川大学学报(医学版)Biochemistry, Genetics and Molecular Biology-Molecular Biology
CiteScore
0.70
自引率
0.00%
发文量
8695
期刊介绍：
"Journal of Sichuan University (Medical Edition)" is a comprehensive medical academic journal sponsored by Sichuan University, a higher education institution directly under the Ministry of Education of the People's Republic of China. It was founded in 1959 and was originally named "Journal of Sichuan Medical College". In 1986, it was renamed "Journal of West China University of Medical Sciences". In 2003, it was renamed "Journal of Sichuan University (Medical Edition)" (bimonthly).
"Journal of Sichuan University (Medical Edition)" is a Chinese core journal and a Chinese authoritative academic journal (RCCSE). It is included in the retrieval systems such as China Science and Technology Papers and Citation Database (CSTPCD), China Science Citation Database (CSCD) (core version), Peking University Library's "Overview of Chinese Core Journals", the U.S. "Index Medica" (IM/Medline), the U.S. "PubMed Central" (PMC), the U.S. "Biological Abstracts" (BA), the U.S. "Chemical Abstracts" (CA), the U.S. EBSCO, the Netherlands "Abstracts and Citation Database" (Scopus), the Japan Science and Technology Agency Database (JST), the Russian "Abstract Magazine", the Chinese Biomedical Literature CD-ROM Database (CBMdisc), the Chinese Biomedical Periodical Literature Database (CMCC), the China Academic Journal Network Full-text Database (CNKI), the Chinese Academic Journal (CD-ROM Edition), and the Wanfang Data-Digital Journal Group.
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