AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system

Wang, Bo; Jin, Shuangshuang; Yan, Qingsen; Xu, Haibo; Luo, Chuan; Wei, Lai; Zhao, Wei; Hou, Xuexue; Ma, Wenshuo; Xu, Zhengqing; Zheng, Zhuozhao; Sun, Wenbo; Lan, Lan; Zhang, Wei; Mu, Xin; Shi, Chenxi; Wang, Zhongxiao; Lee, Jihae; Jin, Zijian; Lin, Minggui; Jin, Hongbo; Zhang, Liang; Guo, Jun; Zhao, Benqi; Ren, Zhizhong; Wang, Shuhao; Xu, Wei; Wang, Xinghuan; Wang, Jianming; You, Zheng; Dong, Jiahong

doi:10.1016/j.asoc.2020.106897

Cited by 288 publications

(142 citation statements)

References 27 publications

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“…Most of the proposed deep learning systems for CT-based COVID-19 detection make use of pre-existing network architectures which were originally designed for other image analysis tasks. For example, Ardakani et al ( 34 ) compared the performance of 10 existing convolutional neural network (CNN) architectures in distinguishing COVID-19 pneumonia from non-COVID-19 pneumonia, and Jin et al ( 38 ) empirically selected an existing CNN architecture for use in a segmentation-classification system. Additionally, many studies add custom components to pre-existing architectures in order to better tailor them to COVID-19 detection.…”

Section: Discussionmentioning

confidence: 99%

COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases From Chest CT Images

2020

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The coronavirus disease 2019 (COVID-19) pandemic continues to have a tremendous impact on patients and healthcare systems around the world. In the fight against this novel disease, there is a pressing need for rapid and effective screening tools to identify patients infected with COVID-19, and to this end CT imaging has been proposed as one of the key screening methods which may be used as a complement to RT-PCR testing, particularly in situations where patients undergo routine CT scans for non-COVID-19 related reasons, patients have worsening respiratory status or developing complications that require expedited care, or patients are suspected to be COVID-19-positive but have negative RT-PCR test results. Early studies on CT-based screening have reported abnormalities in chest CT images which are characteristic of COVID-19 infection, but these abnormalities may be difficult to distinguish from abnormalities caused by other lung conditions. Motivated by this, in this study we introduce COVIDNet-CT, a deep convolutional neural network architecture that is tailored for detection of COVID-19 cases from chest CT images via a machine-driven design exploration approach. Additionally, we introduce COVIDx-CT, a benchmark CT image dataset derived from CT imaging data collected by the China National Center for Bioinformation comprising 104,009 images across 1,489 patient cases. Furthermore, in the interest of reliability and transparency, we leverage an explainability-driven performance validation strategy to investigate the decision-making behavior of COVIDNet-CT, and in doing so ensure that COVIDNet-CT makes predictions based on relevant indicators in CT images. Both COVIDNet-CT and the COVIDx-CT dataset are available to the general public in an open-source and open access manner as part of the COVID-Net initiative. While COVIDNet-CT is not yet a production-ready screening solution, we hope that releasing the model and dataset will encourage researchers, clinicians, and citizen data scientists alike to leverage and build upon them.

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Section: Discussionmentioning

confidence: 99%

COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases From Chest CT Images

2020

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“…Therefore, it is urgent to develop a more objective approach for improving the current diagnostic accuracy of COVID-19 pneumonia. Recently, artificial intelligence (AI) using deep learning technology has demonstrated good performance to improve the diagnosis of COVID-19, with sensitivities ranging from 0.67 to 0.97 and specificities from 0.83 to 0.96 [25][26][27][28]. With more COVID-19 cases involved, the AI system can achieve more accurate segmentation of COVID-19 pneumonia lesions after training [29].…”

Section: Discussionmentioning

confidence: 99%

“…With more COVID-19 cases involved, the AI system can achieve more accurate segmentation of COVID-19 pneumonia lesions after training [29]. Additionally, it was reported that the automatic segmentation and classification of AI system would save 30%-40% of detection time for physicians, which is promising in reducing the workload of healthcare system [28]. However, the large amount of data to be trained for deep learning model construction limited its timely application and generalization based on the sporadic COVID-19 cases in most parts of China during the early stage of COVID-19 pandemic.…”

Section: Discussionmentioning

confidence: 99%

CT radiomics facilitates more accurate diagnosis of COVID-19 pneumonia: compared with CO-RADS

Liu

Ren

et al. 2021

J Transl Med

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Background Limited data was available for rapid and accurate detection of COVID-19 using CT-based machine learning model. This study aimed to investigate the value of chest CT radiomics for diagnosing COVID-19 pneumonia compared with clinical model and COVID-19 reporting and data system (CO-RADS), and develop an open-source diagnostic tool with the constructed radiomics model. Methods This study enrolled 115 laboratory-confirmed COVID-19 and 435 non-COVID-19 pneumonia patients (training dataset, n = 379; validation dataset, n = 131; testing dataset, n = 40). Key radiomics features extracted from chest CT images were selected to build a radiomics signature using least absolute shrinkage and selection operator (LASSO) regression. Clinical and clinico-radiomics combined models were constructed. The combined model was further validated in the viral pneumonia cohort, and compared with performance of two radiologists using CO-RADS. The diagnostic performance was assessed by receiver operating characteristics curve (ROC) analysis, calibration curve, and decision curve analysis (DCA). Results Eight radiomics features and 5 clinical variables were selected to construct the combined radiomics model, which outperformed the clinical model in diagnosing COVID-19 pneumonia with an area under the ROC (AUC) of 0.98 and good calibration in the validation cohort. The combined model also performed better in distinguishing COVID-19 from other viral pneumonia with an AUC of 0.93 compared with 0.75 (P = 0.03) for clinical model, and 0.69 (P = 0.008) or 0.82 (P = 0.15) for two trained radiologists using CO-RADS. The sensitivity and specificity of the combined model can be achieved to 0.85 and 0.90. The DCA confirmed the clinical utility of the combined model. An easy-to-use open-source diagnostic tool was developed using the combined model. Conclusions The combined radiomics model outperformed clinical model and CO-RADS for diagnosing COVID-19 pneumonia, which can facilitate more rapid and accurate detection.

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“…To date, many DL and radiomics models were developed since the outbreak of COVID-19, focusing on screening, diagnosis, and prognosis of COVID-19 15 . However, due to limited medical labor resources and diffused lesion distribution across multiple sections, ROI annotations remained challenging in many of the current studies 8,9,11 . In our study, we utilized a DL segmentation algorithm that was trained with 507 sets of coarse annotated suspected COVID-19 CT scans.…”

Section: Discussionmentioning

confidence: 99%

“…www.nature.com/scientificreports/ lesions, assessing disease severity, and predicting disease prognosis of COVID-19 have been developed [6][7][8][9][10][11][12][13] . Wang et al developed a DL model to provide clinical diagnosis before the pathogenic examinations by extracting radiographical features of COVID-19 8 .…”

mentioning

confidence: 99%

A deep learning integrated radiomics model for identification of coronavirus disease 2019 using computed tomography

Zhang

Wang

Shao

et al. 2021

Sci Rep

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Since its first outbreak, Coronavirus Disease 2019 (COVID-19) has been rapidly spreading worldwide and caused a global pandemic. Rapid and early detection is essential to contain COVID-19. Here, we first developed a deep learning (DL) integrated radiomics model for end-to-end identification of COVID-19 using CT scans and then validated its clinical feasibility. We retrospectively collected CT images of 386 patients (129 with COVID-19 and 257 with other community-acquired pneumonia) from three medical centers to train and externally validate the developed models. A pre-trained DL algorithm was utilized to automatically segment infected lesions (ROIs) on CT images which were used for feature extraction. Five feature selection methods and four machine learning algorithms were utilized to develop radiomics models. Trained with features selected by L1 regularized logistic regression, classifier multi-layer perceptron (MLP) demonstrated the optimal performance with AUC of 0.922 (95% CI 0.856–0.988) and 0.959 (95% CI 0.910–1.000), the same sensitivity of 0.879, and specificity of 0.900 and 0.887 on internal and external testing datasets, which was equivalent to the senior radiologist in a reader study. Additionally, diagnostic time of DL-MLP was more efficient than radiologists (38 s vs 5.15 min). With an adequate performance for identifying COVID-19, DL-MLP may help in screening of suspected cases.

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AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system

Cited by 288 publications

References 27 publications

COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases From Chest CT Images

COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases From Chest CT Images

CT radiomics facilitates more accurate diagnosis of COVID-19 pneumonia: compared with CO-RADS

A deep learning integrated radiomics model for identification of coronavirus disease 2019 using computed tomography

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