人工智能在感染和炎症分子成像中的作用。

IF 1.7 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING European Journal of Hybrid Imaging Pub Date : 2022-09-01 DOI:10.1186/s41824-022-00138-1
Johannes Schwenck, Manfred Kneilling, Niels P Riksen, Christian la Fougère, Douwe J Mulder, Riemer J H A Slart, Erik H J G Aarntzen
{"title":"人工智能在感染和炎症分子成像中的作用。","authors":"Johannes Schwenck,&nbsp;Manfred Kneilling,&nbsp;Niels P Riksen,&nbsp;Christian la Fougère,&nbsp;Douwe J Mulder,&nbsp;Riemer J H A Slart,&nbsp;Erik H J G Aarntzen","doi":"10.1186/s41824-022-00138-1","DOIUrl":null,"url":null,"abstract":"<p><p>The detection of occult infections and low-grade inflammation in clinical practice remains challenging and much depending on readers' expertise. Although molecular imaging, like [<sup>18</sup>F]FDG PET or radiolabeled leukocyte scintigraphy, offers quantitative and reproducible whole body data on inflammatory responses its interpretation is limited to visual analysis. This often leads to delayed diagnosis and treatment, as well as untapped areas of potential application. Artificial intelligence (AI) offers innovative approaches to mine the wealth of imaging data and has led to disruptive breakthroughs in other medical domains already. Here, we discuss how AI-based tools can improve the detection sensitivity of molecular imaging in infection and inflammation but also how AI might push the data analysis beyond current application toward predicting outcome and long-term risk assessment.</p>","PeriodicalId":36160,"journal":{"name":"European Journal of Hybrid Imaging","volume":" ","pages":"17"},"PeriodicalIF":1.7000,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9433558/pdf/","citationCount":"3","resultStr":"{\"title\":\"A role for artificial intelligence in molecular imaging of infection and inflammation.\",\"authors\":\"Johannes Schwenck,&nbsp;Manfred Kneilling,&nbsp;Niels P Riksen,&nbsp;Christian la Fougère,&nbsp;Douwe J Mulder,&nbsp;Riemer J H A Slart,&nbsp;Erik H J G Aarntzen\",\"doi\":\"10.1186/s41824-022-00138-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The detection of occult infections and low-grade inflammation in clinical practice remains challenging and much depending on readers' expertise. Although molecular imaging, like [<sup>18</sup>F]FDG PET or radiolabeled leukocyte scintigraphy, offers quantitative and reproducible whole body data on inflammatory responses its interpretation is limited to visual analysis. This often leads to delayed diagnosis and treatment, as well as untapped areas of potential application. Artificial intelligence (AI) offers innovative approaches to mine the wealth of imaging data and has led to disruptive breakthroughs in other medical domains already. Here, we discuss how AI-based tools can improve the detection sensitivity of molecular imaging in infection and inflammation but also how AI might push the data analysis beyond current application toward predicting outcome and long-term risk assessment.</p>\",\"PeriodicalId\":36160,\"journal\":{\"name\":\"European Journal of Hybrid Imaging\",\"volume\":\" \",\"pages\":\"17\"},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2022-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9433558/pdf/\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"European Journal of Hybrid Imaging\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1186/s41824-022-00138-1\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Journal of Hybrid Imaging","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/s41824-022-00138-1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 3

摘要

隐匿性感染和低度炎症的检测在临床实践中仍然具有挑战性,很大程度上取决于读者的专业知识。虽然分子成像,如[18F]FDG PET或放射性标记的白细胞闪烁成像,提供了炎症反应的定量和可重复的全身数据,但其解释仅限于视觉分析。这往往导致诊断和治疗延迟,以及潜在应用的未开发领域。人工智能(AI)为挖掘丰富的成像数据提供了创新的方法,并已经在其他医疗领域带来了颠覆性的突破。在这里,我们讨论了基于人工智能的工具如何提高感染和炎症分子成像的检测灵敏度,以及人工智能如何将数据分析从当前应用推向预测结果和长期风险评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

摘要图片

摘要图片

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
A role for artificial intelligence in molecular imaging of infection and inflammation.

The detection of occult infections and low-grade inflammation in clinical practice remains challenging and much depending on readers' expertise. Although molecular imaging, like [18F]FDG PET or radiolabeled leukocyte scintigraphy, offers quantitative and reproducible whole body data on inflammatory responses its interpretation is limited to visual analysis. This often leads to delayed diagnosis and treatment, as well as untapped areas of potential application. Artificial intelligence (AI) offers innovative approaches to mine the wealth of imaging data and has led to disruptive breakthroughs in other medical domains already. Here, we discuss how AI-based tools can improve the detection sensitivity of molecular imaging in infection and inflammation but also how AI might push the data analysis beyond current application toward predicting outcome and long-term risk assessment.

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
European Journal of Hybrid Imaging
European Journal of Hybrid Imaging Computer Science-Computer Science (miscellaneous)
CiteScore
3.40
自引率
0.00%
发文量
29
审稿时长
17 weeks
期刊最新文献
Phosphaturic mesenchymal tumor demonstrated by 68Ga-DOTATATE PET/CT in a patient: a case report Carcinoid crisis in Lutetium-177-Dotatate therapy of neuroendocrine tumors: an overview of pathophysiology, risk factors, recognition, and treatment Four-dimensional computed tomography as first-line imaging in primary hyperparathyroidism, a retrospective comparison to conventional imaging in a predominantly single adenoma population Clinical value of semi-quantitative parameters in 68Ga-DOTANOC PET/CT in treatment and diagnostics of cranial meningioma in a single-center retrospective analysis Cardiac transplant rejection assessment with 18F-FDG PET-CT: initial single-centre experience for diagnosis and management
×
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