人工智能在体外受精(IVF):精准和个性化生育治疗的新时代。

IF 1.7 4区 医学 Q3 OBSTETRICS & GYNECOLOGY Journal of gynecology obstetrics and human reproduction Pub Date : 2024-12-27 DOI:10.1016/j.jogoh.2024.102903
David B. Olawade , Jennifer Teke , Khadijat K. Adeleye , Kusal Weerasinghe , Momudat Maidoki , Aanuoluwapo Clement David-Olawade
{"title":"人工智能在体外受精(IVF):精准和个性化生育治疗的新时代。","authors":"David B. Olawade ,&nbsp;Jennifer Teke ,&nbsp;Khadijat K. Adeleye ,&nbsp;Kusal Weerasinghe ,&nbsp;Momudat Maidoki ,&nbsp;Aanuoluwapo Clement David-Olawade","doi":"10.1016/j.jogoh.2024.102903","DOIUrl":null,"url":null,"abstract":"<div><div><em>In-vitro</em> fertilization (IVF) has been a transformative advancement in assisted reproductive technology. However, success rates remain suboptimal, with only about one-third of cycles resulting in pregnancy and fewer leading to live births. This narrative review explores the potential of artificial intelligence (AI), machine learning (ML), and deep learning (DL) to enhance various stages of the IVF process. Personalization of ovarian stimulation protocols, gamete selection, and embryo annotation and selection are critical areas where AI may benefit significantly. AI-driven tools can analyze vast datasets to predict optimal stimulation protocols, potentially improving oocyte quality and fertilization rates. In sperm and oocyte quality assessment, AI can offer precise, objective analyses, reducing subjectivity and standardizing evaluations. In embryo selection, AI can analyze time-lapse imaging and morphological data to support the prediction of embryo viability, potentially aiding implantation outcomes. However, the role of AI in improving clinical outcomes remains to be confirmed by large-scale, well-designed clinical trials. Additionally, AI has the potential to enhance quality control and workflow optimization within IVF laboratories by continuously monitoring key performance indicators (KPIs) and facilitating efficient resource utilization. Ethical considerations, including data privacy, algorithmic bias, and fairness, are paramount for the responsible implementation of AI in IVF. Future research should prioritize validating AI tools in diverse clinical settings, ensuring their applicability and reliability. Collaboration among AI experts, clinicians, and embryologists is essential to drive innovation and improve outcomes in assisted reproduction. AI's integration into IVF holds promise for advancing patient care, but its clinical potential requires careful evaluation and ongoing refinement.</div></div>","PeriodicalId":15871,"journal":{"name":"Journal of gynecology obstetrics and human reproduction","volume":"54 3","pages":"Article 102903"},"PeriodicalIF":1.7000,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial intelligence in in-vitro fertilization (IVF): A new era of precision and personalization in fertility treatments\",\"authors\":\"David B. Olawade ,&nbsp;Jennifer Teke ,&nbsp;Khadijat K. Adeleye ,&nbsp;Kusal Weerasinghe ,&nbsp;Momudat Maidoki ,&nbsp;Aanuoluwapo Clement David-Olawade\",\"doi\":\"10.1016/j.jogoh.2024.102903\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div><em>In-vitro</em> fertilization (IVF) has been a transformative advancement in assisted reproductive technology. However, success rates remain suboptimal, with only about one-third of cycles resulting in pregnancy and fewer leading to live births. This narrative review explores the potential of artificial intelligence (AI), machine learning (ML), and deep learning (DL) to enhance various stages of the IVF process. Personalization of ovarian stimulation protocols, gamete selection, and embryo annotation and selection are critical areas where AI may benefit significantly. AI-driven tools can analyze vast datasets to predict optimal stimulation protocols, potentially improving oocyte quality and fertilization rates. In sperm and oocyte quality assessment, AI can offer precise, objective analyses, reducing subjectivity and standardizing evaluations. In embryo selection, AI can analyze time-lapse imaging and morphological data to support the prediction of embryo viability, potentially aiding implantation outcomes. However, the role of AI in improving clinical outcomes remains to be confirmed by large-scale, well-designed clinical trials. Additionally, AI has the potential to enhance quality control and workflow optimization within IVF laboratories by continuously monitoring key performance indicators (KPIs) and facilitating efficient resource utilization. Ethical considerations, including data privacy, algorithmic bias, and fairness, are paramount for the responsible implementation of AI in IVF. Future research should prioritize validating AI tools in diverse clinical settings, ensuring their applicability and reliability. Collaboration among AI experts, clinicians, and embryologists is essential to drive innovation and improve outcomes in assisted reproduction. AI's integration into IVF holds promise for advancing patient care, but its clinical potential requires careful evaluation and ongoing refinement.</div></div>\",\"PeriodicalId\":15871,\"journal\":{\"name\":\"Journal of gynecology obstetrics and human reproduction\",\"volume\":\"54 3\",\"pages\":\"Article 102903\"},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2024-12-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of gynecology obstetrics and human reproduction\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S246878472400182X\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"OBSTETRICS & GYNECOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of gynecology obstetrics and human reproduction","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S246878472400182X","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"OBSTETRICS & GYNECOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

体外受精(IVF)是辅助生殖技术的革命性进步。然而,成功率仍然不理想,只有大约三分之一的周期导致怀孕,更少的周期导致活产。本文探讨了人工智能(AI)、机器学习(ML)和深度学习(DL)在提高试管婴儿过程各个阶段的潜力。个性化卵巢刺激方案、配子选择、胚胎注释和选择是人工智能可能显著受益的关键领域。人工智能驱动的工具可以分析大量数据集来预测最佳刺激方案,从而有可能提高卵母细胞质量和受精率。在精子和卵母细胞质量评估中,人工智能可以提供精确、客观的分析,减少主观性,规范评估。在胚胎选择中,人工智能可以分析延时成像和形态学数据,以支持胚胎活力的预测,可能有助于植入结果。然而,人工智能在改善临床结果方面的作用仍有待大规模、精心设计的临床试验的证实。此外,人工智能有可能通过持续监测关键绩效指标(kpi)和促进有效的资源利用来加强试管婴儿实验室的质量控制和工作流程优化。伦理考虑,包括数据隐私、算法偏见和公平性,对于在试管婴儿中负责任地实施人工智能至关重要。未来的研究应优先在不同的临床环境中验证人工智能工具,确保其适用性和可靠性。人工智能专家、临床医生和胚胎学家之间的合作对于推动创新和改善辅助生殖的结果至关重要。人工智能与体外受精的结合有望改善患者护理,但其临床潜力需要仔细评估和不断完善。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Artificial intelligence in in-vitro fertilization (IVF): A new era of precision and personalization in fertility treatments
In-vitro fertilization (IVF) has been a transformative advancement in assisted reproductive technology. However, success rates remain suboptimal, with only about one-third of cycles resulting in pregnancy and fewer leading to live births. This narrative review explores the potential of artificial intelligence (AI), machine learning (ML), and deep learning (DL) to enhance various stages of the IVF process. Personalization of ovarian stimulation protocols, gamete selection, and embryo annotation and selection are critical areas where AI may benefit significantly. AI-driven tools can analyze vast datasets to predict optimal stimulation protocols, potentially improving oocyte quality and fertilization rates. In sperm and oocyte quality assessment, AI can offer precise, objective analyses, reducing subjectivity and standardizing evaluations. In embryo selection, AI can analyze time-lapse imaging and morphological data to support the prediction of embryo viability, potentially aiding implantation outcomes. However, the role of AI in improving clinical outcomes remains to be confirmed by large-scale, well-designed clinical trials. Additionally, AI has the potential to enhance quality control and workflow optimization within IVF laboratories by continuously monitoring key performance indicators (KPIs) and facilitating efficient resource utilization. Ethical considerations, including data privacy, algorithmic bias, and fairness, are paramount for the responsible implementation of AI in IVF. Future research should prioritize validating AI tools in diverse clinical settings, ensuring their applicability and reliability. Collaboration among AI experts, clinicians, and embryologists is essential to drive innovation and improve outcomes in assisted reproduction. AI's integration into IVF holds promise for advancing patient care, but its clinical potential requires careful evaluation and ongoing refinement.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of gynecology obstetrics and human reproduction
Journal of gynecology obstetrics and human reproduction Medicine-Obstetrics and Gynecology
CiteScore
3.70
自引率
5.30%
发文量
210
审稿时长
31 days
期刊介绍: Formerly known as Journal de Gynécologie Obstétrique et Biologie de la Reproduction, Journal of Gynecology Obstetrics and Human Reproduction is the official Academic publication of the French College of Obstetricians and Gynecologists (Collège National des Gynécologues et Obstétriciens Français / CNGOF). J Gynecol Obstet Hum Reprod publishes monthly, in English, research papers and techniques in the fields of Gynecology, Obstetrics, Neonatology and Human Reproduction: (guest) editorials, original articles, reviews, updates, technical notes, case reports, letters to the editor and guidelines. Original works include clinical or laboratory investigations and clinical or equipment reports. Reviews include narrative reviews, systematic reviews and meta-analyses.
期刊最新文献
Editorial board Contents New reference charts for fetal ultrasound corpus callosum length with emphasis on the third trimester High-risk patient profiles for ovarian cancer: A new approach using cluster analysis of tumor markers Partners experiences of caesarean deliveries in the operating room
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1