• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

机器学习在 COVID-19 肺炎 CT 图像和 X 射线中的应用。

Application of machine learning in CT images and X-rays of COVID-19 pneumonia.

机构信息

College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China.

出版信息

Medicine (Baltimore). 2021 Sep 10;100(36):e26855. doi: 10.1097/MD.0000000000026855.

DOI:10.1097/MD.0000000000026855
PMID:34516488
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8428739/
Abstract

Coronavirus disease (COVID-19) has spread worldwide. X-ray and computed tomography (CT) are 2 technologies widely used in image acquisition, segmentation, diagnosis, and evaluation. Artificial intelligence can accurately segment infected parts in X-ray and CT images, assist doctors in improving diagnosis efficiency, and facilitate the subsequent assessment of the severity of the patient infection. The medical assistant platform based on machine learning can help radiologists make clinical decisions and helper in screening, diagnosis, and treatment. By providing scientific methods for image recognition, segmentation, and evaluation, we summarized the latest developments in the application of artificial intelligence in COVID-19 lung imaging, and provided guidance and inspiration to researchers and doctors who are fighting the COVID-19 virus.

摘要

冠状病毒病（COVID-19）已在全球范围内传播。X 射线和计算机断层扫描（CT）是两种广泛应用于图像采集、分割、诊断和评估的技术。人工智能可以准确地分割 X 射线和 CT 图像中的感染部位，帮助医生提高诊断效率，并便于后续评估患者感染的严重程度。基于机器学习的医疗辅助平台可以帮助放射科医生做出临床决策，并在筛选、诊断和治疗方面提供帮助。通过为图像识别、分割和评估提供科学方法，我们总结了人工智能在 COVID-19 肺部成像中的最新应用进展，为抗击 COVID-19 病毒的研究人员和医生提供了指导和启示。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/067a/8428739/5dd1a76aa495/medi-100-e26855-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/067a/8428739/b982eb82a877/medi-100-e26855-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/067a/8428739/c6478ea32459/medi-100-e26855-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/067a/8428739/31f5dcc0d16d/medi-100-e26855-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/067a/8428739/1461f3363485/medi-100-e26855-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/067a/8428739/e4c9afdefff7/medi-100-e26855-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/067a/8428739/42404dc3e171/medi-100-e26855-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/067a/8428739/5dd1a76aa495/medi-100-e26855-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/067a/8428739/b982eb82a877/medi-100-e26855-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/067a/8428739/c6478ea32459/medi-100-e26855-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/067a/8428739/31f5dcc0d16d/medi-100-e26855-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/067a/8428739/1461f3363485/medi-100-e26855-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/067a/8428739/e4c9afdefff7/medi-100-e26855-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/067a/8428739/42404dc3e171/medi-100-e26855-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/067a/8428739/5dd1a76aa495/medi-100-e26855-g007.jpg

相似文献

1
Application of machine learning in CT images and X-rays of COVID-19 pneumonia.机器学习在 COVID-19 肺炎 CT 图像和 X 射线中的应用。
Medicine (Baltimore). 2021 Sep 10;100(36):e26855. doi: 10.1097/MD.0000000000026855.
2
COVID-DSNet: A novel deep convolutional neural network for detection of coronavirus (SARS-CoV-2) cases from CT and Chest X-Ray images.COVID-DSNet：一种新型深度卷积神经网络，用于从 CT 和胸部 X 光图像中检测冠状病毒（SARS-CoV-2）病例。
Artif Intell Med. 2022 Dec;134:102427. doi: 10.1016/j.artmed.2022.102427. Epub 2022 Oct 17.
3
Thorax computed tomography (CTX) guided ground truth annotation of CHEST radiographs (CXR) for improved classification and detection of COVID-19.胸部 CT 引导的 CHEST 射线照片（CXR）标注以提高 COVID-19 的分类和检测。
Int J Numer Method Biomed Eng. 2024 Jun;40(6):e3823. doi: 10.1002/cnm.3823. Epub 2024 Apr 8.
4
COVID-19 Pneumonia Diagnosis Using a Simple 2D Deep Learning Framework With a Single Chest CT Image: Model Development and Validation.使用具有单张胸部CT图像的简单二维深度学习框架诊断COVID-19肺炎：模型开发与验证
J Med Internet Res. 2020 Jun 29;22(6):e19569. doi: 10.2196/19569.
5
From community-acquired pneumonia to COVID-19: a deep learning-based method for quantitative analysis of COVID-19 on thick-section CT scans.从社区获得性肺炎到 COVID-19：一种基于深度学习的 CT 厚层扫描 COVID-19 定量分析方法。
Eur Radiol. 2020 Dec;30(12):6828-6837. doi: 10.1007/s00330-020-07042-x. Epub 2020 Jul 18.
6
An Overview of Deep Learning Techniques on Chest X-Ray and CT Scan Identification of COVID-19.深度学习技术在胸部 X 光和 CT 扫描识别 COVID-19 中的应用概述。
Comput Math Methods Med. 2021 Jun 4;2021:5528144. doi: 10.1155/2021/5528144. eCollection 2021.
7
CT radiomics facilitates more accurate diagnosis of COVID-19 pneumonia: compared with CO-RADS.CT 放射组学有助于更准确地诊断 COVID-19 肺炎：与 CO-RADS 相比。
J Transl Med. 2021 Jan 7;19(1):29. doi: 10.1186/s12967-020-02692-3.
8
Dual-branch combination network (DCN): Towards accurate diagnosis and lesion segmentation of COVID-19 using CT images.双分支组合网络（DCN）：用于使用 CT 图像对 COVID-19 进行准确诊断和病变分割。
Med Image Anal. 2021 Jan;67:101836. doi: 10.1016/j.media.2020.101836. Epub 2020 Oct 8.
9
SOM-LWL method for identification of COVID-19 on chest X-rays.基于 SOM-LWL 算法的胸部 X 光片 COVID-19 识别方法。
PLoS One. 2021 Feb 24;16(2):e0247176. doi: 10.1371/journal.pone.0247176. eCollection 2021.
10
CT Manifestations of Coronavirus Disease (COVID-19) Pneumonia and Influenza Virus Pneumonia: A Comparative Study.COVID-19 肺炎与流感病毒肺炎的 CT 表现：一项对比研究。
AJR Am J Roentgenol. 2021 Jan;216(1):71-79. doi: 10.2214/AJR.20.23304. Epub 2020 Jul 9.

引用本文的文献

1
A multi-output network with U-net enhanced class activation map and robust classification performance for medical imaging analysis.一种用于医学影像分析的具有U型网络增强类激活映射和强大分类性能的多输出网络。
Discov Artif Intell. 2023;3(1):1. doi: 10.1007/s44163-022-00045-1. Epub 2023 Jan 3.
2
Healthcare-Associated Infections: The Role of Microbial and Environmental Factors in Infection Control-A Narrative Review.医疗保健相关感染：微生物和环境因素在感染控制中的作用——一篇叙述性综述
Infect Dis Ther. 2025 May;14(5):933-971. doi: 10.1007/s40121-025-01143-0. Epub 2025 Apr 10.
3
Optimization of Soft X-Ray Fresnel Zone Plate Fabrication Through Joint Electron Beam Lithography and Cryo-Etching Techniques.

本文引用的文献

1
COVID-19 open source data sets: a comprehensive survey.新冠病毒病开源数据集：一项全面调查。
Appl Intell (Dordr). 2021;51(3):1296-1325. doi: 10.1007/s10489-020-01862-6. Epub 2020 Sep 21.
2
A review of mathematical modeling, artificial intelligence and datasets used in the study, prediction and management of COVID-19.对用于新型冠状病毒肺炎（COVID-19）研究、预测和管理的数学建模、人工智能及数据集的综述。
Appl Intell (Dordr). 2020;50(11):3913-3925. doi: 10.1007/s10489-020-01770-9. Epub 2020 Jul 6.
3
Predicting Mechanical Ventilation and Mortality in COVID-19 Using Radiomics and Deep Learning on Chest Radiographs: A Multi-Institutional Study.
通过联合电子束光刻和低温蚀刻技术优化软X射线菲涅耳波带片制造
Nanomaterials (Basel). 2024 Nov 26;14(23):1898. doi: 10.3390/nano14231898.
4
Artificial Intelligence in Pediatric Emergency Medicine: Applications, Challenges, and Future Perspectives.人工智能在儿科急诊医学中的应用、挑战及未来展望
Biomedicines. 2024 May 30;12(6):1220. doi: 10.3390/biomedicines12061220.
5
Detection and characterization of spike architecture based on deep learning and X-ray computed tomography in barley.基于深度学习和X射线计算机断层扫描技术对大麦穗形态结构的检测与表征
Plant Methods. 2023 Oct 27;19(1):115. doi: 10.1186/s13007-023-01096-w.
6
A survey of machine learning-based methods for COVID-19 medical image analysis.基于机器学习的 COVID-19 医学图像分析方法研究综述。
Med Biol Eng Comput. 2023 Jun;61(6):1257-1297. doi: 10.1007/s11517-022-02758-y. Epub 2023 Jan 28.
7
Predicting Genetic Disorder and Types of Disorder Using Chain Classifier Approach.使用链式分类器方法预测遗传疾病和疾病类型。
Genes (Basel). 2022 Dec 26;14(1):71. doi: 10.3390/genes14010071.
8
Answering Clinical Questions Using Machine Learning: Should We Look at Diastolic Blood Pressure When Tailoring Blood Pressure Control?使用机器学习回答临床问题：在定制血压控制方案时，我们是否应该关注舒张压？
J Clin Med. 2022 Dec 15;11(24):7454. doi: 10.3390/jcm11247454.
9
Asthma Hospital Admission and Readmission Spikes, Advancing Accurate Classification to Advance Understanding of Causes.哮喘住院和再入院人数激增，推进准确分类以增进对病因的理解。
Diagnostics (Basel). 2022 Oct 10;12(10):2445. doi: 10.3390/diagnostics12102445.
10
Image Recognition of Pediatric Pneumonia Based on Fusion of Texture Features and Depth Features.基于纹理特征和深度特征融合的小儿肺炎图像识别。
Comput Math Methods Med. 2022 Aug 26;2022:1973508. doi: 10.1155/2022/1973508. eCollection 2022.
利用胸部X光片的放射组学和深度学习预测COVID-19患者的机械通气和死亡率：一项多机构研究
Diagnostics (Basel). 2021 Sep 30;11(10):1812. doi: 10.3390/diagnostics11101812.
4
Predicting the Disease Outcome in COVID-19 Positive Patients Through Machine Learning: A Retrospective Cohort Study With Brazilian Data.通过机器学习预测新冠病毒阳性患者的疾病结局：一项基于巴西数据的回顾性队列研究。
Front Artif Intell. 2021 Aug 13;4:579931. doi: 10.3389/frai.2021.579931. eCollection 2021.
5
Improving effectiveness of different deep learning-based models for detecting COVID-19 from computed tomography (CT) images.提高不同基于深度学习的模型从计算机断层扫描（CT）图像中检测新型冠状病毒肺炎（COVID-19）的有效性。
Neural Comput Appl. 2021;33(24):17589-17609. doi: 10.1007/s00521-021-06344-5. Epub 2021 Jul 29.
6
MIDCAN: A multiple input deep convolutional attention network for Covid-19 diagnosis based on chest CT and chest X-ray.MIDCAN：一种基于胸部CT和胸部X光的用于新冠病毒肺炎诊断的多输入深度卷积注意力网络。
Pattern Recognit Lett. 2021 Oct;150:8-16. doi: 10.1016/j.patrec.2021.06.021. Epub 2021 Jul 14.
7
Risk-Aware Identification of Highly Suspected COVID-19 Cases in Social IoT: A Joint Graph Theory and Reinforcement Learning Approach.社交物联网中高疑似新冠肺炎病例的风险感知识别：一种联合图论与强化学习方法
IEEE Access. 2020 Jun 19;8:115655-115661. doi: 10.1109/ACCESS.2020.3003750. eCollection 2020.
8
Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks.使用X射线图像和深度卷积神经网络自动检测冠状病毒病（COVID-19）。
Pattern Anal Appl. 2021;24(3):1207-1220. doi: 10.1007/s10044-021-00984-y. Epub 2021 May 9.
9
ai-corona: Radiologist-assistant deep learning framework for COVID-19 diagnosis in chest CT scans.人工智能-新冠病毒：用于胸部 CT 扫描中 COVID-19 诊断的放射科医生助手深度学习框架。
PLoS One. 2021 May 7;16(5):e0250952. doi: 10.1371/journal.pone.0250952. eCollection 2021.
10
Automated Assessment and Tracking of COVID-19 Pulmonary Disease Severity on Chest Radiographs using Convolutional Siamese Neural Networks.使用卷积暹罗神经网络对胸部X光片上的COVID-19肺部疾病严重程度进行自动评估和跟踪。
Radiol Artif Intell. 2020 Jul 22;2(4):e200079. doi: 10.1148/ryai.2020200079. eCollection 2020 Jul.