radcloud -基于人工智能的研究平台,集成了基于机器学习的放射组学、深度学习和数据管理

Geng Yayuan, Zhang Fengyan, Zhang Ran, Chen Ying, Xiang Yuwei, Wang Fang, Yang Xunhong, Zuo Panli, Chai Xiangfei
{"title":"radcloud -基于人工智能的研究平台,集成了基于机器学习的放射组学、深度学习和数据管理","authors":"Geng Yayuan, Zhang Fengyan, Zhang Ran, Chen Ying, Xiang Yuwei, Wang Fang, Yang Xunhong, Zuo Panli, Chai Xiangfei","doi":"10.2991/jaims.d.210617.001","DOIUrl":null,"url":null,"abstract":"Radiomics and artificial intelligence (AI) are two rapidly advancing techniques in precision medicine for the purpose of dis- ease diagnosis, prognosis, surveillance, and personalized therapy. This paper introduces RadCloud, an artificial intelligent (AI) research platform that supports clinical studies. It integrates machine learning (ML)-based radiomics, deep learning (DL), and data management to simplify AI-based research, supporting rapid introduction of AI algorithms across various medical imaging specialties tomeettheever-increasingdemandsoffutureclinical research.Thisplatform hasbeen successfullyappliedfortumor detection, biomarker identification, prognosis, and treatment effect assessment across various image modalities (MR, PET/CT, CTA, US, MG, etc.) and a variety of organs (breast, lung, kidney, liver, rectum, thyroid, bone, etc). The proposed platform has shown great potential in supporting clinical studies for precision medicine.","PeriodicalId":196434,"journal":{"name":"Journal of Artificial Intelligence for Medical Sciences","volume":"52 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"RadCloud—An Artificial Intelligence-Based Research Platform Integrating Machine Learning-Based Radiomics, Deep Learning, and Data Management\",\"authors\":\"Geng Yayuan, Zhang Fengyan, Zhang Ran, Chen Ying, Xiang Yuwei, Wang Fang, Yang Xunhong, Zuo Panli, Chai Xiangfei\",\"doi\":\"10.2991/jaims.d.210617.001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Radiomics and artificial intelligence (AI) are two rapidly advancing techniques in precision medicine for the purpose of dis- ease diagnosis, prognosis, surveillance, and personalized therapy. This paper introduces RadCloud, an artificial intelligent (AI) research platform that supports clinical studies. It integrates machine learning (ML)-based radiomics, deep learning (DL), and data management to simplify AI-based research, supporting rapid introduction of AI algorithms across various medical imaging specialties tomeettheever-increasingdemandsoffutureclinical research.Thisplatform hasbeen successfullyappliedfortumor detection, biomarker identification, prognosis, and treatment effect assessment across various image modalities (MR, PET/CT, CTA, US, MG, etc.) and a variety of organs (breast, lung, kidney, liver, rectum, thyroid, bone, etc). The proposed platform has shown great potential in supporting clinical studies for precision medicine.\",\"PeriodicalId\":196434,\"journal\":{\"name\":\"Journal of Artificial Intelligence for Medical Sciences\",\"volume\":\"52 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Artificial Intelligence for Medical Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2991/jaims.d.210617.001\",\"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 Artificial Intelligence for Medical Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2991/jaims.d.210617.001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

放射组学和人工智能(AI)是精准医学中两种快速发展的技术,用于疾病诊断、预后、监测和个性化治疗。本文介绍了一个支持临床研究的人工智能(AI)研究平台RadCloud。它集成了基于机器学习(ML)的放射组学、深度学习(DL)和数据管理,以简化基于人工智能的研究,支持在各种医学成像专业中快速引入人工智能算法,以满足不断增长的临床研究需求。该平台已成功应用于各种图像方式(MR、PET/CT、CTA、US、MG等)和各种器官(乳腺、肺、肾、肝、直肠、甲状腺、骨等)的肿瘤检测、生物标志物鉴定、预后和治疗效果评估。该平台在支持精准医学临床研究方面显示出巨大的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
RadCloud—An Artificial Intelligence-Based Research Platform Integrating Machine Learning-Based Radiomics, Deep Learning, and Data Management
Radiomics and artificial intelligence (AI) are two rapidly advancing techniques in precision medicine for the purpose of dis- ease diagnosis, prognosis, surveillance, and personalized therapy. This paper introduces RadCloud, an artificial intelligent (AI) research platform that supports clinical studies. It integrates machine learning (ML)-based radiomics, deep learning (DL), and data management to simplify AI-based research, supporting rapid introduction of AI algorithms across various medical imaging specialties tomeettheever-increasingdemandsoffutureclinical research.Thisplatform hasbeen successfullyappliedfortumor detection, biomarker identification, prognosis, and treatment effect assessment across various image modalities (MR, PET/CT, CTA, US, MG, etc.) and a variety of organs (breast, lung, kidney, liver, rectum, thyroid, bone, etc). The proposed platform has shown great potential in supporting clinical studies for precision medicine.
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:604180095
Book学术官方微信