Artificial Intelligence in Obstetric and Gynecological MR Imaging.

Tsukasa Saida, Wenchao Gu, Sodai Hoshiai, Toshitaka Ishiguro, Masafumi Sakai, Taishi Amano, Yuta Nakahashi, Ayumi Shikama, Toyomi Satoh, Takahito Nakajima
{"title":"Artificial Intelligence in Obstetric and Gynecological MR Imaging.","authors":"Tsukasa Saida, Wenchao Gu, Sodai Hoshiai, Toshitaka Ishiguro, Masafumi Sakai, Taishi Amano, Yuta Nakahashi, Ayumi Shikama, Toyomi Satoh, Takahito Nakajima","doi":"10.2463/mrms.rev.2024-0077","DOIUrl":null,"url":null,"abstract":"<p><p>This review explores the significant progress and applications of artificial intelligence (AI) in obstetrics and gynecological MRI, charting its development from foundational algorithmic techniques to deep learning strategies and advanced radiomics. This review features research published over the last few years that has used AI with MRI to identify specific conditions such as uterine leiomyosarcoma, endometrial cancer, cervical cancer, ovarian tumors, and placenta accreta. In addition, it covers studies on the application of AI for segmentation and quality improvement in obstetrics and gynecology MRI. The review also outlines the existing challenges and envisions future directions for AI research in this domain. The growing accessibility of extensive datasets across various institutions and the application of multiparametric MRI are significantly enhancing the accuracy and adaptability of AI. This progress has the potential to enable more accurate and efficient diagnosis, offering opportunities for personalized medicine in the field of obstetrics and gynecology.</p>","PeriodicalId":94126,"journal":{"name":"Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2463/mrms.rev.2024-0077","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

This review explores the significant progress and applications of artificial intelligence (AI) in obstetrics and gynecological MRI, charting its development from foundational algorithmic techniques to deep learning strategies and advanced radiomics. This review features research published over the last few years that has used AI with MRI to identify specific conditions such as uterine leiomyosarcoma, endometrial cancer, cervical cancer, ovarian tumors, and placenta accreta. In addition, it covers studies on the application of AI for segmentation and quality improvement in obstetrics and gynecology MRI. The review also outlines the existing challenges and envisions future directions for AI research in this domain. The growing accessibility of extensive datasets across various institutions and the application of multiparametric MRI are significantly enhancing the accuracy and adaptability of AI. This progress has the potential to enable more accurate and efficient diagnosis, offering opportunities for personalized medicine in the field of obstetrics and gynecology.

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
人工智能在妇产科磁共振成像中的应用。
这篇综述探讨了人工智能(AI)在妇产科磁共振成像中的重大进展和应用,描绘了其从基础算法技术到深度学习策略和高级放射组学的发展历程。这篇综述介绍了过去几年发表的研究成果,这些研究将人工智能与核磁共振成像相结合,用于识别特定病症,如子宫肌层肉瘤、子宫内膜癌、宫颈癌、卵巢肿瘤和胎盘早剥。此外,它还涵盖了将人工智能应用于妇产科磁共振成像的分割和质量改进的研究。综述还概述了该领域人工智能研究的现有挑战和未来发展方向。不同机构间广泛数据集的日益普及以及多参数核磁共振成像的应用,大大提高了人工智能的准确性和适应性。这一进步有可能实现更准确、更高效的诊断,为妇产科领域的个性化医疗提供机会。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Association between the Presence of the Parasagittal Cyst-like Structures and Cognitive Function. Image-based Re-evaluation of the JCOG0911 Study Focusing on Tumor Volume and Survival, Disease Progression Diagnosis, and Radiomic Prognostication for Newly Diagnosed Glioblastoma. Improving Vessel Visibility and Applying Artificial Intelligence to Autodetect Brain Metastasis for a 3D MR Imaging Sequence Capable of Simultaneous Images with and without Blood Vessel Suppression. Identification of the Distal Dural Ring Using Three-dimensional Motion-sensitized Driven-equilibrium Prepared T1-weighted Fast Spin Echo Imaging: Application to Paraclinoid Aneurysms. In-vitro Detection of Intramammary-like Macrocalcifications Using Susceptibility-weighted MR Imaging Techniques at 1.5T.
×
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