人工智能模型利用子宫内膜分析预测辅助生殖技术的成功率

Ricardo H. Asch Schuff, Jorge Suarez, Nicolas Laugas, Marlene L. Zamora Ramirez, Tamar Alkon
{"title":"人工智能模型利用子宫内膜分析预测辅助生殖技术的成功率","authors":"Ricardo H. Asch Schuff, Jorge Suarez, Nicolas Laugas, Marlene L. Zamora Ramirez, Tamar Alkon","doi":"10.46989/001c.115893","DOIUrl":null,"url":null,"abstract":"This study addresses the development of EndoClassify, an artificial intelligence (AI) model designed to assess endometrial characteristics and enhance embryo receptivity. Utilizing a dataset of 402 endometrial ultrasound images augmented to 14.989, EndoClassify, incorporating Attention U-Net for image segmentation and GoogLeNet Inception for image classification, demonstrated exceptional performance with an accuracy of 95%, loss of 10%, a sensitivity of 93%, and specificity of 93%. The significance of EndoClassify extends beyond its robust metrics. This AI model has transformative potential in clinical settings, offering specialists a reliable, rapid, and accurate tool for endometrial assessment in assisted reproduction technology (ART) cycles. Identifying ‘good endometrium’ with 71% accuracy, corresponding to a 74% pregnancy rate, underscores EndoClassify’s role in significantly improving patient outcomes. In conclusion, the seamless integration of ultrasonographic parameters and AI techniques enhances efficiency in clinical decision-making and signifies a crucial collaboration between advanced technology and clinical expertise. While acknowledging the retrospective design as a limitation, it is imperative to highlight potential biases introduced by this design. Additionally, including fresh and frozen embryo transfers without known ploidy status adds transparency to the study’s limitations. EndoClassify stands as a beacon of progress, poised to revolutionize personalized treatment strategies and bring tangible benefits to specialists and patients in the dynamic landscape of assisted reproductive technology.","PeriodicalId":508169,"journal":{"name":"Journal of IVF-Worldwide","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial intelligence model utilizing endometrial analysis to contribute as a predictor of assisted reproductive technology success\",\"authors\":\"Ricardo H. Asch Schuff, Jorge Suarez, Nicolas Laugas, Marlene L. Zamora Ramirez, Tamar Alkon\",\"doi\":\"10.46989/001c.115893\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study addresses the development of EndoClassify, an artificial intelligence (AI) model designed to assess endometrial characteristics and enhance embryo receptivity. Utilizing a dataset of 402 endometrial ultrasound images augmented to 14.989, EndoClassify, incorporating Attention U-Net for image segmentation and GoogLeNet Inception for image classification, demonstrated exceptional performance with an accuracy of 95%, loss of 10%, a sensitivity of 93%, and specificity of 93%. The significance of EndoClassify extends beyond its robust metrics. This AI model has transformative potential in clinical settings, offering specialists a reliable, rapid, and accurate tool for endometrial assessment in assisted reproduction technology (ART) cycles. Identifying ‘good endometrium’ with 71% accuracy, corresponding to a 74% pregnancy rate, underscores EndoClassify’s role in significantly improving patient outcomes. In conclusion, the seamless integration of ultrasonographic parameters and AI techniques enhances efficiency in clinical decision-making and signifies a crucial collaboration between advanced technology and clinical expertise. While acknowledging the retrospective design as a limitation, it is imperative to highlight potential biases introduced by this design. Additionally, including fresh and frozen embryo transfers without known ploidy status adds transparency to the study’s limitations. EndoClassify stands as a beacon of progress, poised to revolutionize personalized treatment strategies and bring tangible benefits to specialists and patients in the dynamic landscape of assisted reproductive technology.\",\"PeriodicalId\":508169,\"journal\":{\"name\":\"Journal of IVF-Worldwide\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-04-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of IVF-Worldwide\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.46989/001c.115893\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of IVF-Worldwide","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.46989/001c.115893","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

EndoClassify 是一种人工智能(AI)模型,旨在评估子宫内膜特征并提高胚胎受孕率。EndoClassify 采用了用于图像分割的 Attention U-Net 和用于图像分类的 GoogLeNet Inception,利用由 402 幅子宫内膜超声图像扩增到 14.989 幅的数据集,表现出卓越的性能,准确率达 95%,损失率为 10%,灵敏度为 93%,特异性为 93%。EndoClassify 的意义远不止于其强大的指标。这种人工智能模型在临床环境中具有变革潜力,它为专家提供了一种可靠、快速、准确的工具,用于辅助生殖技术(ART)周期中的子宫内膜评估。识别 "良好子宫内膜 "的准确率为 71%,与 74% 的怀孕率相对应,这凸显了 EndoClassify 在显著改善患者预后方面的作用。总之,超声参数与人工智能技术的无缝整合提高了临床决策的效率,标志着先进技术与临床专业知识之间的重要合作。虽然承认回顾性设计是一个局限,但必须强调这种设计可能带来的偏差。此外,将没有已知倍性状态的新鲜和冷冻胚胎移植包括在内,也增加了研究局限性的透明度。EndoClassify是一个进步的灯塔,它将彻底改变个性化治疗策略,为辅助生殖技术领域的专家和患者带来实实在在的好处。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial intelligence model utilizing endometrial analysis to contribute as a predictor of assisted reproductive technology success
This study addresses the development of EndoClassify, an artificial intelligence (AI) model designed to assess endometrial characteristics and enhance embryo receptivity. Utilizing a dataset of 402 endometrial ultrasound images augmented to 14.989, EndoClassify, incorporating Attention U-Net for image segmentation and GoogLeNet Inception for image classification, demonstrated exceptional performance with an accuracy of 95%, loss of 10%, a sensitivity of 93%, and specificity of 93%. The significance of EndoClassify extends beyond its robust metrics. This AI model has transformative potential in clinical settings, offering specialists a reliable, rapid, and accurate tool for endometrial assessment in assisted reproduction technology (ART) cycles. Identifying ‘good endometrium’ with 71% accuracy, corresponding to a 74% pregnancy rate, underscores EndoClassify’s role in significantly improving patient outcomes. In conclusion, the seamless integration of ultrasonographic parameters and AI techniques enhances efficiency in clinical decision-making and signifies a crucial collaboration between advanced technology and clinical expertise. While acknowledging the retrospective design as a limitation, it is imperative to highlight potential biases introduced by this design. Additionally, including fresh and frozen embryo transfers without known ploidy status adds transparency to the study’s limitations. EndoClassify stands as a beacon of progress, poised to revolutionize personalized treatment strategies and bring tangible benefits to specialists and patients in the dynamic landscape of assisted reproductive technology.
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
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信