{"title":"Application of artificial neural networks in reproductive medicine.","authors":"Guanghui Yuan, Bohan Lv, Cuifang Hao","doi":"10.1080/14647273.2022.2156301","DOIUrl":null,"url":null,"abstract":"<p><p>With the emergence of the age of information, the data on reproductive medicine has improved immensely. Nonetheless, healthcare workers who wish to utilise the relevance and implied value of the various data available to aid clinical decision-making encounter the difficulty of statistically analysing such large data. The application of artificial intelligence becoming widespread in recent years has emerged as a turning point in this regard. Artificial neural networks (ANNs) exhibit beneficial characteristics of comprehensive analysis and autonomous learning, owing to which these are being applied to disease diagnosis, embryo quality assessment, and prediction of pregnancy outcomes. The present report aims to summarise the application of ANNs in the field of reproduction and analyse its further application potential.</p>","PeriodicalId":13006,"journal":{"name":"Human Fertility","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Human Fertility","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1080/14647273.2022.2156301","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/1/11 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"OBSTETRICS & GYNECOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
With the emergence of the age of information, the data on reproductive medicine has improved immensely. Nonetheless, healthcare workers who wish to utilise the relevance and implied value of the various data available to aid clinical decision-making encounter the difficulty of statistically analysing such large data. The application of artificial intelligence becoming widespread in recent years has emerged as a turning point in this regard. Artificial neural networks (ANNs) exhibit beneficial characteristics of comprehensive analysis and autonomous learning, owing to which these are being applied to disease diagnosis, embryo quality assessment, and prediction of pregnancy outcomes. The present report aims to summarise the application of ANNs in the field of reproduction and analyse its further application potential.
期刊介绍：
Human Fertility is a leading international, multidisciplinary journal dedicated to furthering research and promoting good practice in the areas of human fertility and infertility. Topics included span the range from molecular medicine to healthcare delivery, and contributions are welcomed from professionals and academics from the spectrum of disciplines concerned with human fertility. It is published on behalf of the British Fertility Society.
The journal also provides a forum for the publication of peer-reviewed articles arising out of the activities of the Association of Biomedical Andrologists, the Association of Clinical Embryologists, the Association of Irish Clinical Embryologists, the British Andrology Society, the British Infertility Counselling Association, the Irish Fertility Society and the Royal College of Nursing Fertility Nurses Group.
All submissions are welcome. Articles considered include original papers, reviews, policy statements, commentaries, debates, correspondence, and reports of sessions at meetings. The journal also publishes refereed abstracts from the meetings of the constituent organizations.
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