A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19)

Wang, Shuai; Kang, Bo Kyeong; Ma, Jinlu; Zeng, Xiaoyang; Xiao, Mingming; Guo, Jia; Cai, Mingjun; Yang, Jingyi; Li, Yaodong; Meng, Xiangyi; Xu, Bo

doi:10.1007/s00330-021-07715-1

Cited by 895 publications

(578 citation statements)

References 22 publications

(4 reference statements)

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“…Medical image segmentation is widely used in infectious disease detection. For example, deep learning-based methods were introduced to identify COVID-19 infected patients using their CT images [95,96]. However, these methods need to improve their predicting accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Some of the application areas where the power of deep learning has started to be unleashed are in drug discovery, disease detection through image segmentation, outbreak prediction, etc. [95,133]. We believe deep learning components can play an important role in understanding pandemics like COVID-19, which will further improve our decision-making.…”

Section: Deep Learningmentioning

confidence: 99%

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iResponse: An AI and IoT-Enabled Framework for Autonomous COVID-19 Pandemic Management

et al. 2021

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SARS-CoV-2, a tiny virus, is severely affecting the social, economic, and environmental sustainability of our planet, causing infections and deaths (2,674,151 deaths, as of 17 March 2021), relationship breakdowns, depression, economic downturn, riots, and much more. The lessons that have been learned from good practices by various countries include containing the virus rapidly; enforcing containment measures; growing COVID-19 testing capability; discovering cures; providing stimulus packages to the affected; easing monetary policies; developing new pandemic-related industries; support plans for controlling unemployment; and overcoming inequalities. Coordination and multi-term planning have been found to be the key among the successful national and global endeavors to fight the pandemic. The current research and practice have mainly focused on specific aspects of COVID-19 response. There is a need to automate the learning process such that we can learn from good and bad practices during pandemics and normal times. To this end, this paper proposes a technology-driven framework, iResponse, for coordinated and autonomous pandemic management, allowing pandemic-related monitoring and policy enforcement, resource planning and provisioning, and data-driven planning and decision-making. The framework consists of five modules: Monitoring and Break-the-Chain, Cure Development and Treatment, Resource Planner, Data Analytics and Decision Making, and Data Storage and Management. All modules collaborate dynamically to make coordinated and informed decisions. We provide the technical system architecture of a system based on the proposed iResponse framework along with the design details of each of its five components. The challenges related to the design of the individual modules and the whole system are discussed. We provide six case studies in the paper to elaborate on the different functionalities of the iResponse framework and how the framework can be implemented. These include a sentiment analysis case study, a case study on the recognition of human activities, and four case studies using deep learning and other data-driven methods to show how to develop sustainability-related optimal strategies for pandemic management using seven real-world datasets. A number of important findings are extracted from these case studies.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Deep Learningmentioning

confidence: 99%

iResponse: An AI and IoT-Enabled Framework for Autonomous COVID-19 Pandemic Management

et al. 2021

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“…The use of artificial intelligence (AI) in reading CT scans to detect COVID-19 has also been investigated in order to improve the efficiency of diagnosis [72]. After the assessment of 1065 CT scans of COVID-19 patients, the AI algorithm had scored an 89.5% detection accuracy, while a human-lead external panel had scored a 79.3% detection accuracy.…”

Section: Computed Tomography Scanning-an Alternative To Laboratory-based Testingmentioning

confidence: 99%

“…After the assessment of 1065 CT scans of COVID-19 patients, the AI algorithm had scored an 89.5% detection accuracy, while a human-lead external panel had scored a 79.3% detection accuracy. This indicates that this testing method has potential to be revolutionised digitally and aid in stopping the spreading of the disease through fast diagnosis [72].…”

Section: Computed Tomography Scanning-an Alternative To Laboratory-based Testingmentioning

confidence: 99%

Misdiagnosis of SARS-CoV-2: A Critical Review of the Influence of Sampling and Clinical Detection Methods

et al. 2021

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SARS-CoV-2 infection has generated the biggest pandemic since the influenza outbreak of 1918–1919. One clear difference between these pandemics has been the ability to test for the presence of the virus or for evidence of infection. This review examined the performance characteristics of sample types via PCR detection of the virus, of antibody testing, of rapid viral antigen detection kits and computerised tomography (CT) scanning. It was found that combined detection approaches, such as the incorporation of CT scans, may reduce the levels of false negatives obtained by PCR detection in both symptomatic and asymptomatic patients, while sputum and oral throat washing sample types should take precedence over swabbing when available. Rt-PCR assays for detection of the virus remain the gold-standard method for SARS-CoV-2 diagnosis and can be used effectively on pooled samples for widespread screening. The novel Oxford antibody assay was found to have the highest sensitivity and specificity of four currently available commercial antibody kits but should only be used during a specific timeframe post-symptom onset. Further research into transmission modes between symptomatic and asymptomatic patients is needed. Analysis of the performance characteristics of different sampling and detection methods for SARS-CoV-2 showed that timing of sampling and testing methods used can greatly influence the rate of false-positive and false-negative test results, thereby influencing viral spread.

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“…The low-specificity of CT can deter disease detection in non-COVID-19 cases. In addition, ionizing radiation from the CT scanner can cause problems to patients who require multiple CT scans during the course of their disease [12][13][14][15][16]. In the past decade, numerous computer-based methods have been employed for improving the efficiency of medical imaging techniques.…”

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Network–Based Computer-Aided Detection System for COVID-19 Using Multiple Lung Scans: Design and Implementation Study (Preprint)

Ghaderzadeh¹,

Asadi²,

Jafari³

et al. 2021

Preprint

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BACKGROUND Due to the COVID-19 pandemic and the imminent collapse of healthcare systems following the excessive consumption of financial, hospital, and medicinal resources, the World Health Organization (WHO) changed the alert level on the COVID-19 pandemic from high to very high. Meanwhile, the world began to favor less expensive and more precise COVID-19 detection methods. Machine vision-based COVID-19 detection methods especially Deep learning as a diagnostic technique in the early stages of the disease have found great importance during the pandemic. OBJECTIVE This study aimed to design a highly efficient Computer-Aided Detection (CAD) system for COVID-19 by using a NASNet-based algorithm. n images of 190 persons suspected of COVID-19, was used. METHODS A state-of-the-art pre-trained CNN network for image feature extraction, called NASNet, was adopted to identify patients with COVID-19 in the first stages of the disease. A local dataset, comprising 10153 CT scan images of 190 persons suspected of COVID-19, was used. RESULTS After fitting on the training dataset, hyper-parameter tuning and finally topological alterations of the classifier block, the proposed NASNet-based model was evaluated on the test dataset and yielded remarkable results. The proposed model's performance achieved a detection sensitivity, specificity, and accuracy of 0.999, 0.986, and 0.996, respectively. CONCLUSIONS The proposed model achieved acceptable results in the categorization of two data classes. Therefore, a CAD system was designed based on this model for COVID-19 detection using multiple lung CT scans. The system managed to differentiate all the COVID-19 cases from non-COVID-19 ones without any error in the application phase. Overall, the proposed deep learning-based CAD system can greatly aid radiologists in the detection of COVID-19 in its early stages. During the COVID-19 pandemic, the use of CAD system as a screening tool accelerates the process of disease detection and prevents the loss of healthcare resources.

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A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19)

Cited by 895 publications

References 22 publications

iResponse: An AI and IoT-Enabled Framework for Autonomous COVID-19 Pandemic Management

iResponse: An AI and IoT-Enabled Framework for Autonomous COVID-19 Pandemic Management

Misdiagnosis of SARS-CoV-2: A Critical Review of the Influence of Sampling and Clinical Detection Methods

Deep Convolutional Neural Network–Based Computer-Aided Detection System for COVID-19 Using Multiple Lung Scans: Design and Implementation Study (Preprint)

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