Xiaoxiao Guo, Hao Zhan, Xianghui Zhang, Yiwei Pang, Huishu Xu, Baolin Zhang, Kaixue Lao, Peihui Ding, Yanlin Wang, Lei Han
{"title":"Predictive models for starting dose of gonadotropin in controlled ovarian hyperstimulation: review and progress update.","authors":"Xiaoxiao Guo, Hao Zhan, Xianghui Zhang, Yiwei Pang, Huishu Xu, Baolin Zhang, Kaixue Lao, Peihui Ding, Yanlin Wang, Lei Han","doi":"10.1080/14647273.2023.2285937","DOIUrl":null,"url":null,"abstract":"<p><p>Controlled ovarian hyperstimulation (COH) is an essential for in vitro fertilization-embryo transfer (IVF-ET) and an important aspect of assisted reproductive technology (ART). Individual starting doses of gonadotropin (Gn) is a critical decision in the process of COH. It has a crucial impact on the number of retrieved oocytes, the cancelling rate of ART cycles, and complications such as ovarian hyperstimulation syndrome (OHSS), as well as pregnancy outcomes. How to make clinical team more standardized and accurate in determining the starting dose of Gn is an important issue in reproductive medicine. In the past 20 years, research teams worldwide have explored prediction models for Gn starting doses. With the integration of artificial intelligence (AI) and deep learning, it is hoped that there will be more suitable predictive model for Gn starting dose in the future.</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/10.1080/14647273.2023.2285937","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/24 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"OBSTETRICS & GYNECOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Controlled ovarian hyperstimulation (COH) is an essential for in vitro fertilization-embryo transfer (IVF-ET) and an important aspect of assisted reproductive technology (ART). Individual starting doses of gonadotropin (Gn) is a critical decision in the process of COH. It has a crucial impact on the number of retrieved oocytes, the cancelling rate of ART cycles, and complications such as ovarian hyperstimulation syndrome (OHSS), as well as pregnancy outcomes. How to make clinical team more standardized and accurate in determining the starting dose of Gn is an important issue in reproductive medicine. In the past 20 years, research teams worldwide have explored prediction models for Gn starting doses. With the integration of artificial intelligence (AI) and deep learning, it is hoped that there will be more suitable predictive model for Gn starting dose in the future.
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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