Development and evaluation of an artificial intelligence system for COVID-19 diagnosis
Early detection of COVID-19 based on chest CT enables timely treatment of patients and helps control the spread of the disease. We proposed an artificial intelligence (AI) system for rapid COVID-19 detection and performed extensive statistical analysis of CTs of COVID-19 based on the AI system. We d...
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| Published in | Nature communications Vol. 11; no. 1; pp. 5088 - 14 |
|---|---|
| Main Authors | , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
09.10.2020
Nature Portfolio |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Early detection of COVID-19 based on chest CT enables timely treatment of patients and helps control the spread of the disease. We proposed an artificial intelligence (AI) system for rapid COVID-19 detection and performed extensive statistical analysis of CTs of COVID-19 based on the AI system. We developed and evaluated our system on a large dataset with more than 10 thousand CT volumes from COVID-19, influenza-A/B, non-viral community acquired pneumonia (CAP) and non-pneumonia subjects. In such a difficult multi-class diagnosis task, our deep convolutional neural network-based system is able to achieve an area under the receiver operating characteristic curve (AUC) of 97.81% for multi-way classification on test cohort of 3,199 scans, AUC of 92.99% and 93.25% on two publicly available datasets, CC-CCII and MosMedData respectively. In a reader study involving five radiologists, the AI system outperforms all of radiologists in more challenging tasks at a speed of two orders of magnitude above them. Diagnosis performance of chest x-ray (CXR) is compared to that of CT. Detailed interpretation of deep network is also performed to relate system outputs with CT presentations. The code is available at
https://github.com/ChenWWWeixiang/diagnosis_covid19
.
In some contexts, rapid detection of COVID-19 from CT scans can be crucial for optimal patient management. Here, the authors present a Deep Learning system for this task with multi-center data, human reader comparison and age stratified results. |
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| AbstractList | Early detection of COVID-19 based on chest CT enables timely treatment of patients and helps control the spread of the disease. We proposed an artificial intelligence (AI) system for rapid COVID-19 detection and performed extensive statistical analysis of CTs of COVID-19 based on the AI system. We developed and evaluated our system on a large dataset with more than 10 thousand CT volumes from COVID-19, influenza-A/B, non-viral community acquired pneumonia (CAP) and non-pneumonia subjects. In such a difficult multi-class diagnosis task, our deep convolutional neural network-based system is able to achieve an area under the receiver operating characteristic curve (AUC) of 97.81% for multi-way classification on test cohort of 3,199 scans, AUC of 92.99% and 93.25% on two publicly available datasets, CC-CCII and MosMedData respectively. In a reader study involving five radiologists, the AI system outperforms all of radiologists in more challenging tasks at a speed of two orders of magnitude above them. Diagnosis performance of chest x-ray (CXR) is compared to that of CT. Detailed interpretation of deep network is also performed to relate system outputs with CT presentations. The code is available at https://github.com/ChenWWWeixiang/diagnosis_covid19 .Early detection of COVID-19 based on chest CT enables timely treatment of patients and helps control the spread of the disease. We proposed an artificial intelligence (AI) system for rapid COVID-19 detection and performed extensive statistical analysis of CTs of COVID-19 based on the AI system. We developed and evaluated our system on a large dataset with more than 10 thousand CT volumes from COVID-19, influenza-A/B, non-viral community acquired pneumonia (CAP) and non-pneumonia subjects. In such a difficult multi-class diagnosis task, our deep convolutional neural network-based system is able to achieve an area under the receiver operating characteristic curve (AUC) of 97.81% for multi-way classification on test cohort of 3,199 scans, AUC of 92.99% and 93.25% on two publicly available datasets, CC-CCII and MosMedData respectively. In a reader study involving five radiologists, the AI system outperforms all of radiologists in more challenging tasks at a speed of two orders of magnitude above them. Diagnosis performance of chest x-ray (CXR) is compared to that of CT. Detailed interpretation of deep network is also performed to relate system outputs with CT presentations. The code is available at https://github.com/ChenWWWeixiang/diagnosis_covid19 . Early detection of COVID-19 based on chest CT enables timely treatment of patients and helps control the spread of the disease. We proposed an artificial intelligence (AI) system for rapid COVID-19 detection and performed extensive statistical analysis of CTs of COVID-19 based on the AI system. We developed and evaluated our system on a large dataset with more than 10 thousand CT volumes from COVID-19, influenza-A/B, non-viral community acquired pneumonia (CAP) and non-pneumonia subjects. In such a difficult multi-class diagnosis task, our deep convolutional neural network-based system is able to achieve an area under the receiver operating characteristic curve (AUC) of 97.81% for multi-way classification on test cohort of 3,199 scans, AUC of 92.99% and 93.25% on two publicly available datasets, CC-CCII and MosMedData respectively. In a reader study involving five radiologists, the AI system outperforms all of radiologists in more challenging tasks at a speed of two orders of magnitude above them. Diagnosis performance of chest x-ray (CXR) is compared to that of CT. Detailed interpretation of deep network is also performed to relate system outputs with CT presentations. The code is available at https://github.com/ChenWWWeixiang/diagnosis_covid19 . In some contexts, rapid detection of COVID-19 from CT scans can be crucial for optimal patient management. Here, the authors present a Deep Learning system for this task with multi-center data, human reader comparison and age stratified results. In some contexts, rapid detection of COVID-19 from CT scans can be crucial for optimal patient management. Here, the authors present a Deep Learning system for this task with multi-center data, human reader comparison and age stratified results. Early detection of COVID-19 based on chest CT enables timely treatment of patients and helps control the spread of the disease. We proposed an artificial intelligence (AI) system for rapid COVID-19 detection and performed extensive statistical analysis of CTs of COVID-19 based on the AI system. We developed and evaluated our system on a large dataset with more than 10 thousand CT volumes from COVID-19, influenza-A/B, non-viral community acquired pneumonia (CAP) and non-pneumonia subjects. In such a difficult multi-class diagnosis task, our deep convolutional neural network-based system is able to achieve an area under the receiver operating characteristic curve (AUC) of 97.81% for multi-way classification on test cohort of 3,199 scans, AUC of 92.99% and 93.25% on two publicly available datasets, CC-CCII and MosMedData respectively. In a reader study involving five radiologists, the AI system outperforms all of radiologists in more challenging tasks at a speed of two orders of magnitude above them. Diagnosis performance of chest x-ray (CXR) is compared to that of CT. Detailed interpretation of deep network is also performed to relate system outputs with CT presentations. The code is available at https://github.com/ChenWWWeixiang/diagnosis_covid19 . Early detection of COVID-19 based on chest CT enables timely treatment of patients and helps control the spread of the disease. We proposed an artificial intelligence (AI) system for rapid COVID-19 detection and performed extensive statistical analysis of CTs of COVID-19 based on the AI system. We developed and evaluated our system on a large dataset with more than 10 thousand CT volumes from COVID-19, influenza-A/B, non-viral community acquired pneumonia (CAP) and non-pneumonia subjects. In such a difficult multi-class diagnosis task, our deep convolutional neural network-based system is able to achieve an area under the receiver operating characteristic curve (AUC) of 97.81% for multi-way classification on test cohort of 3,199 scans, AUC of 92.99% and 93.25% on two publicly available datasets, CC-CCII and MosMedData respectively. In a reader study involving five radiologists, the AI system outperforms all of radiologists in more challenging tasks at a speed of two orders of magnitude above them. Diagnosis performance of chest x-ray (CXR) is compared to that of CT. Detailed interpretation of deep network is also performed to relate system outputs with CT presentations. The code is available at https://github.com/ChenWWWeixiang/diagnosis_covid19 . |
| ArticleNumber | 5088 |
| Author | Cao, Yukun Zhang, Xin Zhou, Jie Deng, Lei Chen, Weixiang Zheng, Chuansheng Shi, Heshui Xu, Zhanwei Tan, Zimeng Jin, Cheng Feng, Jianjiang |
| Author_xml | – sequence: 1 givenname: Cheng surname: Jin fullname: Jin, Cheng organization: Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University – sequence: 2 givenname: Weixiang surname: Chen fullname: Chen, Weixiang organization: Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University – sequence: 3 givenname: Yukun orcidid: 0000-0002-8391-841X surname: Cao fullname: Cao, Yukun organization: Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Hubei Province Key Laboratory of Molecular Imaging – sequence: 4 givenname: Zhanwei surname: Xu fullname: Xu, Zhanwei organization: Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University – sequence: 5 givenname: Zimeng surname: Tan fullname: Tan, Zimeng organization: Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University – sequence: 6 givenname: Xin surname: Zhang fullname: Zhang, Xin organization: Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Hubei Province Key Laboratory of Molecular Imaging – sequence: 7 givenname: Lei surname: Deng fullname: Deng, Lei organization: Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University – sequence: 8 givenname: Chuansheng orcidid: 0000-0002-2435-1417 surname: Zheng fullname: Zheng, Chuansheng organization: Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Hubei Province Key Laboratory of Molecular Imaging – sequence: 9 givenname: Jie surname: Zhou fullname: Zhou, Jie organization: Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University – sequence: 10 givenname: Heshui orcidid: 0000-0002-0877-3054 surname: Shi fullname: Shi, Heshui email: heshuishi@hust.edu.cn organization: Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Hubei Province Key Laboratory of Molecular Imaging – sequence: 11 givenname: Jianjiang orcidid: 0000-0002-5940-0063 surname: Feng fullname: Feng, Jianjiang email: jfeng@tsinghua.edu.cn organization: Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33037212$$D View this record in MEDLINE/PubMed |
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LiLUsing Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic AccuracyRadiology2020296E65E7110.1148/radiol.2020200905 BaiHXAI augmentation of radiologist performance in distinguishing COVID-19 from pneumonia of other etiology on chest CTRadiology2020296E156E16510.1148/radiol.2020201491 EstevaAA guide to deep learning in healthcareNat. 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| Snippet | Early detection of COVID-19 based on chest CT enables timely treatment of patients and helps control the spread of the disease. We proposed an artificial... In some contexts, rapid detection of COVID-19 from CT scans can be crucial for optimal patient management. Here, the authors present a Deep Learning system for... |
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| Title | Development and evaluation of an artificial intelligence system for COVID-19 diagnosis |
| URI | https://link.springer.com/article/10.1038/s41467-020-18685-1 https://www.ncbi.nlm.nih.gov/pubmed/33037212 https://www.proquest.com/docview/2449958169 https://pubmed.ncbi.nlm.nih.gov/PMC7547659 https://doaj.org/article/d4872dc51bfb45c682ed26ba2c2d2654 |
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