Сontemporary基于人工智能的数字乳房x线影像分析医疗决策支持系统

V. Solodkiy, A. Kaprin, N. V. Nudnov, N. V. Kharchenko, O. Khodorovich, G. Zapirov, T. Sherstneva, Sh. M. Dibirova, L. Kanakhina
{"title":"Сontemporary基于人工智能的数字乳房x线影像分析医疗决策支持系统","authors":"V. Solodkiy, A. Kaprin, N. V. Nudnov, N. V. Kharchenko, O. Khodorovich, G. Zapirov, T. Sherstneva, Sh. M. Dibirova, L. Kanakhina","doi":"10.20862/0042-4676-2023-104-2-151-162","DOIUrl":null,"url":null,"abstract":"The relevance of implementing artificial intelligence (AI) technologies in the diagnosis of breast cancer (BC) is associated with a continuing high increase in BC incidence among women and its leading position in the structure of cancer incidence. Theoretically, using AI technologies is possible both at the stage of screening and in clarifying BC diagnosis. The article provides a brief overview of AI systems used in clinical practice and discusses their prospects in BC diagnosis. Advances in machine learning can be effective to improve the accuracy of mammography screening by reducing missed cancer cases and false positives.","PeriodicalId":34090,"journal":{"name":"Vestnik rentgenologii i radiologii","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Сontemporary Medical Decision Support Systems Based on Artificial Intelligence for the Analysis of Digital Mammographic Images\",\"authors\":\"V. Solodkiy, A. Kaprin, N. V. Nudnov, N. V. Kharchenko, O. Khodorovich, G. Zapirov, T. Sherstneva, Sh. M. Dibirova, L. Kanakhina\",\"doi\":\"10.20862/0042-4676-2023-104-2-151-162\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The relevance of implementing artificial intelligence (AI) technologies in the diagnosis of breast cancer (BC) is associated with a continuing high increase in BC incidence among women and its leading position in the structure of cancer incidence. Theoretically, using AI technologies is possible both at the stage of screening and in clarifying BC diagnosis. The article provides a brief overview of AI systems used in clinical practice and discusses their prospects in BC diagnosis. Advances in machine learning can be effective to improve the accuracy of mammography screening by reducing missed cancer cases and false positives.\",\"PeriodicalId\":34090,\"journal\":{\"name\":\"Vestnik rentgenologii i radiologii\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-08-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Vestnik rentgenologii i radiologii\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.20862/0042-4676-2023-104-2-151-162\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Vestnik rentgenologii i radiologii","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.20862/0042-4676-2023-104-2-151-162","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

在诊断癌症(BC)中实施人工智能(AI)技术的相关性与女性BC发病率的持续高增长及其在癌症发病率结构中的领先地位有关。从理论上讲,无论是在筛查阶段还是在明确BC诊断时,使用人工智能技术都是可能的。本文简要概述了人工智能系统在临床实践中的应用,并讨论了其在BC诊断中的前景。机器学习的进步可以有效地通过减少癌症漏诊病例和假阳性来提高乳腺X线筛查的准确性。
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
Сontemporary Medical Decision Support Systems Based on Artificial Intelligence for the Analysis of Digital Mammographic Images
The relevance of implementing artificial intelligence (AI) technologies in the diagnosis of breast cancer (BC) is associated with a continuing high increase in BC incidence among women and its leading position in the structure of cancer incidence. Theoretically, using AI technologies is possible both at the stage of screening and in clarifying BC diagnosis. The article provides a brief overview of AI systems used in clinical practice and discusses their prospects in BC diagnosis. Advances in machine learning can be effective to improve the accuracy of mammography screening by reducing missed cancer cases and false positives.
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
24
审稿时长
36 weeks
期刊最新文献
Comparative Reproducibility Analysis of Thoracic Aorta Morphometric Parameters According to Computed Tomography and Magnetic Resonance Angiography Radiological Features of Changes in the Lungs Caused by Fast- or Slow-Growing Nontuberculous Mycobacteria To Help the Practitioner: Imaging of Ovarian Masses According to the O-RADS MRI Ovarian Malignancy Categorical Risk Scale Experience in Using Breast Single-Photon Emission Computed Tomography with <sup>99m</sup>Tc-MIBI Differential Diagnosis of Fibrotic Hypersensitivity Pneumonitis with Its Non-Fibrotic Phenotype and Usual Interstitial Pneumonia During High-Resolution Computed Tomography
×
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