351
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Gunraj H, Wang L, Wong A. COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases From Chest CT Images. Front Med (Lausanne) 2020; 7:608525. [PMID: 33425953 PMCID: PMC7786372 DOI: 10.3389/fmed.2020.608525] [Citation(s) in RCA: 90] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 12/03/2020] [Indexed: 01/08/2023] Open
Abstract
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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Affiliation(s)
- Hayden Gunraj
- Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Linda Wang
- Vision and Image Processing Research Group, University of Waterloo, Waterloo, ON, Canada
- DarwinAI Corp., Waterloo, ON, Canada
| | - Alexander Wong
- Vision and Image Processing Research Group, University of Waterloo, Waterloo, ON, Canada
- DarwinAI Corp., Waterloo, ON, Canada
- Waterloo Artificial Intelligence Institute, University of Waterloo, Waterloo, ON, Canada
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352
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Jiang Y, Chen H, Loew M, Ko H. COVID-19 CT Image Synthesis With a Conditional Generative Adversarial Network. IEEE J Biomed Health Inform 2020; 25:441-452. [PMID: 33275588 PMCID: PMC8545178 DOI: 10.1109/jbhi.2020.3042523] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic that has spread rapidly since December 2019. Real-time reverse transcription polymerase chain reaction (rRT-PCR) and chest computed tomography (CT) imaging both play an important role in COVID-19 diagnosis. Chest CT imaging offers the benefits of quick reporting, a low cost, and high sensitivity for the detection of pulmonary infection. Recently, deep-learning-based computer vision methods have demonstrated great promise for use in medical imaging applications, including X-rays, magnetic resonance imaging, and CT imaging. However, training a deep-learning model requires large volumes of data, and medical staff faces a high risk when collecting COVID-19 CT data due to the high infectivity of the disease. Another issue is the lack of experts available for data labeling. In order to meet the data requirements for COVID-19 CT imaging, we propose a CT image synthesis approach based on a conditional generative adversarial network that can effectively generate high-quality and realistic COVID-19 CT images for use in deep-learning-based medical imaging tasks. Experimental results show that the proposed method outperforms other state-of-the-art image synthesis methods with the generated COVID-19 CT images and indicates promising for various machine learning applications including semantic segmentation and classification.
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353
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Plante TB, Blau AM, Berg AN, Weinberg AS, Jun IC, Tapson VF, Kanigan TS, Adib AB. Development and External Validation of a Machine Learning Tool to Rule Out COVID-19 Among Adults in the Emergency Department Using Routine Blood Tests: A Large, Multicenter, Real-World Study. J Med Internet Res 2020; 22:e24048. [PMID: 33226957 PMCID: PMC7713695 DOI: 10.2196/24048] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 10/14/2020] [Accepted: 11/19/2020] [Indexed: 12/21/2022] Open
Abstract
Background Conventional diagnosis of COVID-19 with reverse transcription polymerase chain reaction (RT-PCR) testing (hereafter, PCR) is associated with prolonged time to diagnosis and significant costs to run the test. The SARS-CoV-2 virus might lead to characteristic patterns in the results of widely available, routine blood tests that could be identified with machine learning methodologies. Machine learning modalities integrating findings from these common laboratory test results might accelerate ruling out COVID-19 in emergency department patients. Objective We sought to develop (ie, train and internally validate with cross-validation techniques) and externally validate a machine learning model to rule out COVID 19 using only routine blood tests among adults in emergency departments. Methods Using clinical data from emergency departments (EDs) from 66 US hospitals before the pandemic (before the end of December 2019) or during the pandemic (March-July 2020), we included patients aged ≥20 years in the study time frame. We excluded those with missing laboratory results. Model training used 2183 PCR-confirmed cases from 43 hospitals during the pandemic; negative controls were 10,000 prepandemic patients from the same hospitals. External validation used 23 hospitals with 1020 PCR-confirmed cases and 171,734 prepandemic negative controls. The main outcome was COVID 19 status predicted using same-day routine laboratory results. Model performance was assessed with area under the receiver operating characteristic (AUROC) curve as well as sensitivity, specificity, and negative predictive value (NPV). Results Of 192,779 patients included in the training, external validation, and sensitivity data sets (median age decile 50 [IQR 30-60] years, 40.5% male [78,249/192,779]), AUROC for training and external validation was 0.91 (95% CI 0.90-0.92). Using a risk score cutoff of 1.0 (out of 100) in the external validation data set, the model achieved sensitivity of 95.9% and specificity of 41.7%; with a cutoff of 2.0, sensitivity was 92.6% and specificity was 59.9%. At the cutoff of 2.0, the NPVs at a prevalence of 1%, 10%, and 20% were 99.9%, 98.6%, and 97%, respectively. Conclusions A machine learning model developed with multicenter clinical data integrating commonly collected ED laboratory data demonstrated high rule-out accuracy for COVID-19 status, and might inform selective use of PCR-based testing.
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Affiliation(s)
- Timothy B Plante
- Larner College of Medicine at the University of Vermont, Colchester, VT, United States.,University of Vermont Medical Center, Burlington, VT, United States
| | - Aaron M Blau
- University of Vermont Medical Center, Burlington, VT, United States
| | - Adrian N Berg
- Larner College of Medicine at the University of Vermont, Burlington, VT, United States.,Biocogniv Inc, South Burlington, VT, United States
| | | | - Ik C Jun
- Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | | | | | - Artur B Adib
- Biocogniv Inc, South Burlington, VT, United States
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354
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Nour M, Cömert Z, Polat K. A Novel Medical Diagnosis model for COVID-19 infection detection based on Deep Features and Bayesian Optimization. Appl Soft Comput 2020; 97:106580. [PMID: 32837453 PMCID: PMC7385069 DOI: 10.1016/j.asoc.2020.106580] [Citation(s) in RCA: 150] [Impact Index Per Article: 37.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 07/21/2020] [Accepted: 07/22/2020] [Indexed: 12/24/2022]
Abstract
A pneumonia of unknown causes, which was detected in Wuhan, China, and spread rapidly throughout the world, was declared as Coronavirus disease 2019 (COVID-19). Thousands of people have lost their lives to this disease. Its negative effects on public health are ongoing. In this study, an intelligence computer-aided model that can automatically detect positive COVID-19 cases is proposed to support daily clinical applications. The proposed model is based on the convolution neural network (CNN) architecture and can automatically reveal discriminative features on chest X-ray images through its convolution with rich filter families, abstraction, and weight-sharing characteristics. Contrary to the generally used transfer learning approach, the proposed deep CNN model was trained from scratch. Instead of the pre-trained CNNs, a novel serial network consisting of five convolution layers was designed. This CNN model was utilized as a deep feature extractor. The extracted deep discriminative features were used to feed the machine learning algorithms, which were k-nearest neighbor, support vector machine (SVM), and decision tree. The hyperparameters of the machine learning models were optimized using the Bayesian optimization algorithm. The experiments were conducted on a public COVID-19 radiology database. The database was divided into two parts as training and test sets with 70% and 30% rates, respectively. As a result, the most efficient results were ensured by the SVM classifier with an accuracy of 98.97%, a sensitivity of 89.39%, a specificity of 99.75%, and an F-score of 96.72%. Consequently, a cheap, fast, and reliable intelligence tool has been provided for COVID-19 infection detection. The developed model can be used to assist field specialists, physicians, and radiologists in the decision-making process. Thanks to the proposed tool, the misdiagnosis rates can be reduced, and the proposed model can be used as a retrospective evaluation tool to validate positive COVID-19 infection cases.
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Affiliation(s)
- Majid Nour
- Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Zafer Cömert
- Department of Software Engineering, Engineering Faculty, Samsun University, Samsun, Turkey
| | - Kemal Polat
- Department of Electrical and Electronics Engineering, Faculty of Engineering, Bolu Abant Izzet Baysal University, 14280, Bolu, Turkey
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355
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Multi-Time-Scale Features for Accurate Respiratory Sound Classification. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10238606] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The COVID-19 pandemic has amplified the urgency of the developments in computer-assisted medicine and, in particular, the need for automated tools supporting the clinical diagnosis and assessment of respiratory symptoms. This need was already clear to the scientific community, which launched an international challenge in 2017 at the International Conference on Biomedical Health Informatics (ICBHI) for the implementation of accurate algorithms for the classification of respiratory sound. In this work, we present a framework for respiratory sound classification based on two different kinds of features: (i) short-term features which summarize sound properties on a time scale of tenths of a second and (ii) long-term features which assess sounds properties on a time scale of seconds. Using the publicly available dataset provided by ICBHI, we cross-validated the classification performance of a neural network model over 6895 respiratory cycles and 126 subjects. The proposed model reached an accuracy of 85%±3% and an precision of 80%±8%, which compare well with the body of literature. The robustness of the predictions was assessed by comparison with state-of-the-art machine learning tools, such as the support vector machine, Random Forest and deep neural networks. The model presented here is therefore suitable for large-scale applications and for adoption in clinical practice. Finally, an interesting observation is that both short-term and long-term features are necessary for accurate classification, which could be the subject of future studies related to its clinical interpretation.
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356
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Al-Antari MA, Hua CH, Bang J, Lee S. "Fast deep learning computer-aided diagnosis of COVID-19 based on digital chest x-ray images". APPL INTELL 2020; 51:2890-2907. [PMID: 34764573 PMCID: PMC7695589 DOI: 10.1007/s10489-020-02076-6] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/09/2020] [Indexed: 11/28/2022]
Abstract
Coronavirus disease 2019 (COVID-19) is a novel harmful respiratory disease that has rapidly spread worldwide. At the end of 2019, COVID-19 emerged as a previously unknown respiratory disease in Wuhan, Hubei Province, China. The world health organization (WHO) declared the coronavirus outbreak a pandemic in the second week of March 2020. Simultaneous deep learning detection and classification of COVID-19 based on the full resolution of digital X-ray images is the key to efficiently assisting patients by enabling physicians to reach a fast and accurate diagnosis decision. In this paper, a simultaneous deep learning computer-aided diagnosis (CAD) system based on the YOLO predictor is proposed that can detect and diagnose COVID-19, differentiating it from eight other respiratory diseases: atelectasis, infiltration, pneumothorax, masses, effusion, pneumonia, cardiomegaly, and nodules. The proposed CAD system was assessed via five-fold tests for the multi-class prediction problem using two different databases of chest X-ray images: COVID-19 and ChestX-ray8. The proposed CAD system was trained with an annotated training set of 50,490 chest X-ray images. The regions on the entire X-ray images with lesions suspected of being due to COVID-19 were simultaneously detected and classified end-to-end via the proposed CAD predictor, achieving overall detection and classification accuracies of 96.31% and 97.40%, respectively. Most test images from patients with confirmed COVID-19 and other respiratory diseases were correctly predicted, achieving average intersection over union (IoU) greater than 90%. Applying deep learning regularizers of data balancing and augmentation improved the COVID-19 diagnostic performance by 6.64% and 12.17% in terms of the overall accuracy and the F1-score, respectively. It is feasible to achieve a diagnosis based on individual chest X-ray images with the proposed CAD system within 0.0093 s. Thus, the CAD system presented in this paper can make a prediction at the rate of 108 frames/s (FPS), which is close to real-time. The proposed deep learning CAD system can reliably differentiate COVID-19 from other respiratory diseases. The proposed deep learning model seems to be a reliable tool that can be used to practically assist health care systems, patients, and physicians.
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Affiliation(s)
- Mugahed A Al-Antari
- Department of Computer Science and Engineering, College of Software, Kyung Hee University, 1732, Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do 17104 Republic of Korea.,Department of Biomedical Engineering, Sana'a Community College, Sana'a, Republic of Yemen
| | - Cam-Hao Hua
- Department of Computer Science and Engineering, College of Software, Kyung Hee University, 1732, Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do 17104 Republic of Korea
| | - Jaehun Bang
- Department of Computer Science and Engineering, College of Software, Kyung Hee University, 1732, Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do 17104 Republic of Korea
| | - Sungyoung Lee
- Department of Computer Science and Engineering, College of Software, Kyung Hee University, 1732, Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do 17104 Republic of Korea
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357
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Albahli S, Albattah W. Deep Transfer Learning for COVID-19 Prediction: Case Study for Limited Data Problems. Curr Med Imaging 2020; 17:973-980. [PMID: 33231160 PMCID: PMC8653418 DOI: 10.2174/1573405616666201123120417] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2020] [Revised: 09/24/2020] [Accepted: 10/06/2020] [Indexed: 11/22/2022]
Abstract
OBJECTIVE Automatic prediction of COVID-19 using deep convolution neural networks based pre-trained transfer models and Chest X-ray images. METHODS This research employs the advantages of computer vision and medical image analysis to develop an automated model that has the clinical potential for early detection of the disease. Using Deep Learning models, the research aims at evaluating the effectiveness and accuracy of different convolutional neural networks models in the automatic diagnosis of COVID-19 from X-ray images as compared to diagnosis performed by experts in the medical community. RESULTS Due to the fact that the dataset available for COVID-19 is still limited, the best model to use is the InceptionNetV3. Performance results show that the InceptionNetV3 model yielded the highest accuracy of 98.63% (with data augmentation) and 98.90% (without data augmentation) among the three models designed. However, as the dataset gets bigger, the Inception ResNetV2 and NASNetlarge will do a better job of classification. All the performed networks tend to over-fit when data augmentation is not used, this is due to the small amount of data used for training and validation. CONCLUSION A deep transfer learning is proposed to detecting the COVID-19 automatically from chest X-ray by training it with X-ray images gotten from both COVID-19 patients and people with normal chest X-rays. The study is aimed at helping doctors in making decisions in their clinical practice due its high performance and effectiveness, the study also gives an insight to how transfer learning was used to automatically detect the COVID-19.
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Affiliation(s)
- Saleh Albahli
- Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia
| | - Waleed Albattah
- Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia
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358
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Chiroma H, Ezugwu AE, Jauro F, Al-Garadi MA, Abdullahi IN, Shuib L. Early survey with bibliometric analysis on machine learning approaches in controlling COVID-19 outbreaks. PeerJ Comput Sci 2020; 6:e313. [PMID: 33816964 PMCID: PMC7924648 DOI: 10.7717/peerj-cs.313] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Accepted: 10/15/2020] [Indexed: 05/09/2023]
Abstract
BACKGROUND AND OBJECTIVE The COVID-19 pandemic has caused severe mortality across the globe, with the USA as the current epicenter of the COVID-19 epidemic even though the initial outbreak was in Wuhan, China. Many studies successfully applied machine learning to fight COVID-19 pandemic from a different perspective. To the best of the authors' knowledge, no comprehensive survey with bibliometric analysis has been conducted yet on the adoption of machine learning to fight COVID-19. Therefore, the main goal of this study is to bridge this gap by carrying out an in-depth survey with bibliometric analysis on the adoption of machine learning-based technologies to fight COVID-19 pandemic from a different perspective, including an extensive systematic literature review and bibliometric analysis. METHODS We applied a literature survey methodology to retrieved data from academic databases and subsequently employed a bibliometric technique to analyze the accessed records. Besides, the concise summary, sources of COVID-19 datasets, taxonomy, synthesis and analysis are presented in this study. It was found that the Convolutional Neural Network (CNN) is mainly utilized in developing COVID-19 diagnosis and prognosis tools, mostly from chest X-ray and chest CT scan images. Similarly, in this study, we performed a bibliometric analysis of machine learning-based COVID-19 related publications in the Scopus and Web of Science citation indexes. Finally, we propose a new perspective for solving the challenges identified as direction for future research. We believe the survey with bibliometric analysis can help researchers easily detect areas that require further development and identify potential collaborators. RESULTS The findings of the analysis presented in this article reveal that machine learning-based COVID-19 diagnose tools received the most considerable attention from researchers. Specifically, the analyses of results show that energy and resources are more dispenses towards COVID-19 automated diagnose tools while COVID-19 drugs and vaccine development remains grossly underexploited. Besides, the machine learning-based algorithm that is predominantly utilized by researchers in developing the diagnostic tool is CNN mainly from X-rays and CT scan images. CONCLUSIONS The challenges hindering practical work on the application of machine learning-based technologies to fight COVID-19 and new perspective to solve the identified problems are presented in this article. Furthermore, we believed that the presented survey with bibliometric analysis could make it easier for researchers to identify areas that need further development and possibly identify potential collaborators at author, country and institutional level, with the overall aim of furthering research in the focused area of machine learning application to disease control.
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Affiliation(s)
- Haruna Chiroma
- Future Technology Research Center, National Yunlin University of Science and Technology, Yulin, Taiwan
| | - Absalom E. Ezugwu
- School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, KwaZulu-Natal, South Africa
| | - Fatsuma Jauro
- Department of Computer Science, Faculty of Science, Ahmadu Bello University, Zaria, Nigeria
| | | | - Idris N. Abdullahi
- Department of Medical Laboratory Science, College of Medical Sciences, Ahmadu Bello University, Zaria, Nigeria
| | - Liyana Shuib
- Department of Information System, Universiti Malaya, Kuala Lumpur, Malaysia
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359
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Rai M, Bonde S, Yadav A, Plekhanova Y, Reshetilov A, Gupta I, Golińska P, Pandit R, Ingle AP. Nanotechnology-based promising strategies for the management of COVID-19: current development and constraints. Expert Rev Anti Infect Ther 2020; 20:1299-1308. [PMID: 33164589 DOI: 10.1080/14787210.2021.1836961] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
INTRODUCTION COVID-19 pandemic has been declared as a global emergency by the World Health Organization which has mounted global pressure on the healthcare system. The design and development of rapid tests for the precise and early detection of infection are urgently needed to detect the disease and also for bulk screening of infected persons. The traditional drugs moderately control the symptoms, but so far, no specific drug has been discovered. The prime concern is to device novel tools for rapid and precise diagnosis, drug delivery, and effective therapies for coronavirus. In this context, nanotechnology offers novel ways to fight against COVID-19. AREA COVERED This review includes the use of nanomaterials for the control of COVID-19. The tools for diagnosis of coronavirus, nano-based vaccines, and nanoparticles as a drug delivery system for the treatment of virus infection have been discussed. The toxicity issues related to nanoparticles have also been addressed. EXPERT OPINION The research on nanotechnology-based diagnosis, drug delivery, and antiviral therapies is at a preliminary stage. The antiviral nanomedicine therapies are cost-effective and with high quality. Nanoparticles are a promising tool for prevention, diagnosis, antiviral drug delivery, and therapeutics, which may open up new avenues in the treatment of COVID-19.
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Affiliation(s)
- Mahendra Rai
- Nanobiotechnology Laboratory, Department of Biotechnology, Sant Gadge Baba Amravati University, Amravati, MH, India.,Department of Microbiology, Nicolaus Copernicus University, Torun, Poland
| | - Shital Bonde
- Nanobiotechnology Laboratory, Department of Biotechnology, Sant Gadge Baba Amravati University, Amravati, MH, India
| | - Alka Yadav
- Nanobiotechnology Laboratory, Department of Biotechnology, Sant Gadge Baba Amravati University, Amravati, MH, India
| | - Yulia Plekhanova
- Laboratory of Biosensors, G.K. Skryabin Institute of Biochemistry and Physiology of Microorganisms, Russian Academy of Sciences, Moscow, Russia
| | - Anatoly Reshetilov
- Laboratory of Biosensors, G.K. Skryabin Institute of Biochemistry and Physiology of Microorganisms, Russian Academy of Sciences, Moscow, Russia
| | - Indarchand Gupta
- Department of Biotechnology, Institute of Science, Aurangabad, MH, India
| | - Patrycja Golińska
- Department of Microbiology, Nicolaus Copernicus University, Torun, Poland
| | - Raksha Pandit
- Nanobiotechnology Laboratory, Department of Biotechnology, Sant Gadge Baba Amravati University, Amravati, MH, India
| | - Avinash P Ingle
- Department of Biotechnology, Engineering School of Lorena, University of Sao Paulo, Lorena, SP, Brazil
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360
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Santana HS, de Souza MRP, Lopes MGM, Souza J, Silva RRO, Palma MSA, Nakano WLV, Lima GAS, Munhoz G, Noriler D, Taranto OP, Silva JL. How chemical engineers can contribute to fight the COVID-19. J Taiwan Inst Chem Eng 2020; 116:67-80. [PMID: 33282011 PMCID: PMC7698668 DOI: 10.1016/j.jtice.2020.11.024] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 11/22/2020] [Accepted: 11/23/2020] [Indexed: 12/22/2022]
Abstract
The SARS-CoV-2 virus, promoter of COVID-19, already infected millions of people around the world, resulting in thousands of fatal victims. Facing this unprecedented crisis in human history, several research groups, industrial companies and governments have been spending efforts to develop vaccines and medications. People from distinct knowledge fields are doing their part in order to overcome this crisis. Chemical Engineers are also contributing in the development of actions to control the SARS-CoV-2 virus. However, many chemical engineers still do not know how to use the knowledge acquired from Chemical Engineering school to collaborate in the fight against the COVID-19. In this context, the present paper aims to discuss several knowledge fields within the Chemical Engineering and correlated areas successfully applied to create innovative and effective solutions in the fight against the COVID-19.
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Affiliation(s)
- Harrson S Santana
- University of Campinas, School of Chemical Engineering, Albert Einstein Av. 500, 13083-852 Campinas, SP, Brazil
| | - Marcos R P de Souza
- Universidade Federal do Amazonas, Faculdade de Ciências Agrárias, Departamento de Engenharia Agrícola e dos Solos, Av. General Rodrigo Otávio, 1200, 69067-005, Manaus, AM, Brazil
| | - Mariana G M Lopes
- University of Campinas, School of Chemical Engineering, Albert Einstein Av. 500, 13083-852 Campinas, SP, Brazil
| | - Johmar Souza
- University of Campinas, School of Chemical Engineering, Albert Einstein Av. 500, 13083-852 Campinas, SP, Brazil
| | - Renan R O Silva
- Department of Biochemical and Pharmaceutical Technology, Sao Paulo University, 05508-000 São Paulo, São Paulo, Brazil
| | - Mauri S A Palma
- Department of Biochemical and Pharmaceutical Technology, Sao Paulo University, 05508-000 São Paulo, São Paulo, Brazil
| | - Wilson L V Nakano
- University of Campinas, School of Chemical Engineering, Albert Einstein Av. 500, 13083-852 Campinas, SP, Brazil
| | | | | | - Dirceu Noriler
- University of Campinas, School of Chemical Engineering, Albert Einstein Av. 500, 13083-852 Campinas, SP, Brazil
| | - Osvaldir P Taranto
- University of Campinas, School of Chemical Engineering, Albert Einstein Av. 500, 13083-852 Campinas, SP, Brazil
| | - João L Silva
- Federal University of ABC, CECS - Center for Engineering, Modeling and Applied Social Sciences, Alameda da Universidade, s/n., 09606-045 São Bernardo do Campo, SP, Brazil
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361
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Brunese L, Mercaldo F, Reginelli A, Santone A. Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 196:105608. [PMID: 32599338 PMCID: PMC7831868 DOI: 10.1016/j.cmpb.2020.105608] [Citation(s) in RCA: 233] [Impact Index Per Article: 58.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Accepted: 06/09/2020] [Indexed: 05/03/2023]
Abstract
BACKGROUND AND OBJECTIVE Coronavirus disease (COVID-19) is an infectious disease caused by a new virus never identified before in humans. This virus causes respiratory disease (for instance, flu) with symptoms such as cough, fever and, in severe cases, pneumonia. The test to detect the presence of this virus in humans is performed on sputum or blood samples and the outcome is generally available within a few hours or, at most, days. Analysing biomedical imaging the patient shows signs of pneumonia. In this paper, with the aim of providing a fully automatic and faster diagnosis, we propose the adoption of deep learning for COVID-19 detection from X-rays. METHOD In particular, we propose an approach composed by three phases: the first one to detect if in a chest X-ray there is the presence of a pneumonia. The second one to discern between COVID-19 and pneumonia. The last step is aimed to localise the areas in the X-ray symptomatic of the COVID-19 presence. RESULTS AND CONCLUSION Experimental analysis on 6,523 chest X-rays belonging to different institutions demonstrated the effectiveness of the proposed approach, with an average time for COVID-19 detection of approximately 2.5 seconds and an average accuracy equal to 0.97.
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Affiliation(s)
- Luca Brunese
- Department of Medicine and Health Sciences "Vincenzo Tiberio", University of Molise, Campobasso, Italy
| | - Francesco Mercaldo
- Department of Medicine and Health Sciences "Vincenzo Tiberio", University of Molise, Campobasso, Italy; Institute for Informatics and Telematics, National Research Council of Italy (CNR), Pisa, Italy
| | - Alfonso Reginelli
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Napoli, Italy
| | - Antonella Santone
- Department of Biosciences and Territory, University of Molise, Pesche (IS), Italy
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362
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Trivizakis E, Tsiknakis N, Vassalou EE, Papadakis GZ, Spandidos DA, Sarigiannis D, Tsatsakis A, Papanikolaou N, Karantanas AH, Marias K. Advancing COVID-19 differentiation with a robust preprocessing and integration of multi-institutional open-repository computer tomography datasets for deep learning analysis. Exp Ther Med 2020; 20:78. [PMID: 32968435 PMCID: PMC7500043 DOI: 10.3892/etm.2020.9210] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 09/11/2020] [Indexed: 12/15/2022] Open
Abstract
The coronavirus pandemic and its unprecedented consequences globally has spurred the interest of the artificial intelligence research community. A plethora of published studies have investigated the role of imaging such as chest X-rays and computer tomography in coronavirus disease 2019 (COVID-19) automated diagnosis. Οpen repositories of medical imaging data can play a significant role by promoting cooperation among institutes in a world-wide scale. However, they may induce limitations related to variable data quality and intrinsic differences due to the wide variety of scanner vendors and imaging parameters. In this study, a state-of-the-art custom U-Net model is presented with a dice similarity coefficient performance of 99.6% along with a transfer learning VGG-19 based model for COVID-19 versus pneumonia differentiation exhibiting an area under curve of 96.1%. The above was significantly improved over the baseline model trained with no segmentation in selected tomographic slices of the same dataset. The presented study highlights the importance of a robust preprocessing protocol for image analysis within a heterogeneous imaging dataset and assesses the potential diagnostic value of the presented COVID-19 model by comparing its performance to the state of the art.
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Affiliation(s)
- Eleftherios Trivizakis
- Computational Biomedicine Laboratory (CBML), Foundation for Research and Technology Hellas (FORTH), 70013 Heraklion, Greece
- Department of Radiology, Medical School, University of Crete, 71003 Heraklion, Greece
| | - Nikos Tsiknakis
- Computational Biomedicine Laboratory (CBML), Foundation for Research and Technology Hellas (FORTH), 70013 Heraklion, Greece
| | - Evangelia E. Vassalou
- Department of Medical Imaging, University Hospital of Heraklion, 71110 Heraklion, Greece
- Department of Radiology, Sitia District Hospital, 72300 Lasithi, Greece
| | - Georgios Z. Papadakis
- Computational Biomedicine Laboratory (CBML), Foundation for Research and Technology Hellas (FORTH), 70013 Heraklion, Greece
- Department of Radiology, Medical School, University of Crete, 71003 Heraklion, Greece
| | - Demetrios A. Spandidos
- Laboratory of Clinical Virology, Medical School, University of Crete, 71003 Heraklion, Greece
| | - Dimosthenis Sarigiannis
- HERACLES Research Center on the Exposome and Health, Centre for Interdisciplinary Research and Innovation, Aristotle University of Thessaloniki, 57001 Thermi, Greece
- University School for Advanced Studies IUSS, I-27100 Pavia, Italy
| | - Aristidis Tsatsakis
- Department of Forensic Sciences and Toxicology, Medical School, University of Crete, 71003 Heraklion, Greece
| | - Nikolaos Papanikolaou
- Computational Biomedicine Laboratory (CBML), Foundation for Research and Technology Hellas (FORTH), 70013 Heraklion, Greece
- Computational Clinical Imaging Group, Centre for the Unknown, Champalimaud Foundation, 1400-038 Lisbon, Portugal
| | - Apostolos H. Karantanas
- Computational Biomedicine Laboratory (CBML), Foundation for Research and Technology Hellas (FORTH), 70013 Heraklion, Greece
- Department of Radiology, Medical School, University of Crete, 71003 Heraklion, Greece
- Department of Medical Imaging, University Hospital of Heraklion, 71110 Heraklion, Greece
| | - Kostas Marias
- Computational Biomedicine Laboratory (CBML), Foundation for Research and Technology Hellas (FORTH), 70013 Heraklion, Greece
- Department of Electrical and Computer Engineering, Hellenic Mediterranean University, 71410 Heraklion, Greece
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363
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Khalifa NEM, Taha MHN, Manogaran G, Loey M. A deep learning model and machine learning methods for the classification of potential coronavirus treatments on a single human cell. JOURNAL OF NANOPARTICLE RESEARCH : AN INTERDISCIPLINARY FORUM FOR NANOSCALE SCIENCE AND TECHNOLOGY 2020; 22:313. [PMID: 33100894 PMCID: PMC7568014 DOI: 10.1007/s11051-020-05041-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 10/06/2020] [Indexed: 05/24/2023]
Abstract
Coronavirus pandemic is burdening healthcare systems around the world to the full capacity they can accommodate. There is an overwhelming need to find a treatment for this virus as early as possible. Computer algorithms and deep learning can participate positively by finding a potential treatment for SARS-CoV-2. In this paper, a deep learning model and machine learning methods for the classification of potential coronavirus treatments on a single human cell will be presented. The dataset selected in this work is a subset of the publicly online datasets available on RxRx.ai. The objective of this research is to automatically classify a single human cell according to the treatment type and the treatment concentration level. A DCNN model and a methodology are proposed throughout this work. The methodical idea is to convert the numerical features from the original dataset to the image domain and then fed them up into a DCNN model. The proposed DCNN model consists of three convolutional layers, three ReLU layers, three pooling layers, and two fully connected layers. The experimental results show that the proposed DCNN model for treatment classification (32 classes) achieved 98.05% in testing accuracy if it is compared with classical machine learning such as support vector machine, decision tree, and ensemble. In treatment concentration level prediction, the classical machine learning (ensemble) algorithm achieved 98.5% in testing accuracy while the proposed DCNN model achieved 98.2%. The performance metrics strengthen the obtained results from the conducted experiments for the accuracy of treatment classification and treatment concentration level prediction.
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Affiliation(s)
- Nour Eldeen M. Khalifa
- Department of Information Technology, Faculty of Computers & Artificial Intelligence, Cairo University, Cairo, 12613 Egypt
| | - Mohamed Hamed N. Taha
- Department of Information Technology, Faculty of Computers & Artificial Intelligence, Cairo University, Cairo, 12613 Egypt
| | - Gunasekaran Manogaran
- University of California, Davis, USA
- College of Information and Electrical Engineering, Asia University, Taichung, Taiwan
| | - Mohamed Loey
- Department of Computer Science, Faculty of Computers and Artificial Intelligence, Benha University, Benha, 13518 Egypt
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364
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Ragab DA, Attallah O. FUSI-CAD: Coronavirus (COVID-19) diagnosis based on the fusion of CNNs and handcrafted features. PeerJ Comput Sci 2020; 6:e306. [PMID: 33816957 PMCID: PMC7924442 DOI: 10.7717/peerj-cs.306] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Accepted: 09/30/2020] [Indexed: 05/19/2023]
Abstract
The precise and rapid diagnosis of coronavirus (COVID-19) at the very primary stage helps doctors to manage patients in high workload conditions. In addition, it prevents the spread of this pandemic virus. Computer-aided diagnosis (CAD) based on artificial intelligence (AI) techniques can be used to distinguish between COVID-19 and non-COVID-19 from the computed tomography (CT) imaging. Furthermore, the CAD systems are capable of delivering an accurate faster COVID-19 diagnosis, which consequently saves time for the disease control and provides an efficient diagnosis compared to laboratory tests. In this study, a novel CAD system called FUSI-CAD based on AI techniques is proposed. Almost all the methods in the literature are based on individual convolutional neural networks (CNN). Consequently, the FUSI-CAD system is based on the fusion of multiple different CNN architectures with three handcrafted features including statistical features and textural analysis features such as discrete wavelet transform (DWT), and the grey level co-occurrence matrix (GLCM) which were not previously utilized in coronavirus diagnosis. The SARS-CoV-2 CT-scan dataset is used to test the performance of the proposed FUSI-CAD. The results show that the proposed system could accurately differentiate between COVID-19 and non-COVID-19 images, as the accuracy achieved is 99%. Additionally, the system proved to be reliable as well. This is because the sensitivity, specificity, and precision attained to 99%. In addition, the diagnostics odds ratio (DOR) is ≥ 100. Furthermore, the results are compared with recent related studies based on the same dataset. The comparison verifies the competence of the proposed FUSI-CAD over the other related CAD systems. Thus, the novel FUSI-CAD system can be employed in real diagnostic scenarios for achieving accurate testing for COVID-19 and avoiding human misdiagnosis that might exist due to human fatigue. It can also reduce the time and exertion made by the radiologists during the examination process.
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Affiliation(s)
- Dina A. Ragab
- Electronics and Communications Engineering Department, Arab Academy for Science, Technology, and Maritime Transport (AASTMT), Alexandria, Egypt
| | - Omneya Attallah
- Electronics and Communications Engineering Department, Arab Academy for Science, Technology, and Maritime Transport (AASTMT), Alexandria, Egypt
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365
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Kaushik A, Patel S, Dubey K. Digital cardiovascular care in COVID-19 pandemic: A potential alternative? J Card Surg 2020; 35:3545-3550. [PMID: 33040399 PMCID: PMC7675673 DOI: 10.1111/jocs.15094] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 09/06/2020] [Accepted: 09/13/2020] [Indexed: 12/11/2022]
Abstract
BACKGROUND Cardiovascular patients are at increased risk of acquiring coronavirus disease 2019 (COVID-19) infection while their visit to healthcare facilities. There is a need for alternative tools for optimal monitoring and management of cardiovascular patients in the present pandemic situation. Digital health care may prove to be a new revolutionary tool to protect cardiovascular patients from coronavirus disease by avoiding routine visits to health care facilities that are already overwhelmed with COVID-19 patients. METHODS To evaluate the role of digital health care in the present era of the COVID-19 pandemic, we have reviewed the published literature on digital health services providing cardiovascular care. RESULTS AND CONCLUSION Digital health including telemedicine services, robotic telemedicine carts, use of artificial intelligence and machine learning, use of digital gadgets like smartwatches and web-based applications may be a safe alternative for the management of cardiovascular patients in the present pandemic situation.
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Affiliation(s)
- Atul Kaushik
- Department of Cardiology, All India Institute of Medical Sciences (AIIMS), Jodhpur, India
| | - Surendra Patel
- Department of Trauma and Emergency Cardiothoracic Surgery, All India Institute of Medical Sciences (AIIMS), Jodhpur, India
| | - Kalika Dubey
- Department of Maternal and Reproductive Health, Sanjay Gandhi Post Graduate Institute of Medical Sciences (SGPGIMS), Lucknow, India
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366
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Pham TD. A comprehensive study on classification of COVID-19 on computed tomography with pretrained convolutional neural networks. Sci Rep 2020; 10:16942. [PMID: 33037291 PMCID: PMC7547710 DOI: 10.1038/s41598-020-74164-z] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 09/28/2020] [Indexed: 02/05/2023] Open
Abstract
The use of imaging data has been reported to be useful for rapid diagnosis of COVID-19. Although computed tomography (CT) scans show a variety of signs caused by the viral infection, given a large amount of images, these visual features are difficult and can take a long time to be recognized by radiologists. Artificial intelligence methods for automated classification of COVID-19 on CT scans have been found to be very promising. However, current investigation of pretrained convolutional neural networks (CNNs) for COVID-19 diagnosis using CT data is limited. This study presents an investigation on 16 pretrained CNNs for classification of COVID-19 using a large public database of CT scans collected from COVID-19 patients and non-COVID-19 subjects. The results show that, using only 6 epochs for training, the CNNs achieved very high performance on the classification task. Among the 16 CNNs, DenseNet-201, which is the deepest net, is the best in terms of accuracy, balance between sensitivity and specificity, [Formula: see text] score, and area under curve. Furthermore, the implementation of transfer learning with the direct input of whole image slices and without the use of data augmentation provided better classification rates than the use of data augmentation. Such a finding alleviates the task of data augmentation and manual extraction of regions of interest on CT images, which are adopted by current implementation of deep-learning models for COVID-19 classification.
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Affiliation(s)
- Tuan D Pham
- Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, 31952, Saudi Arabia.
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367
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Canayaz M. MH-COVIDNet: Diagnosis of COVID-19 using deep neural networks and meta-heuristic-based feature selection on X-ray images. Biomed Signal Process Control 2020; 64:102257. [PMID: 33042210 PMCID: PMC7538100 DOI: 10.1016/j.bspc.2020.102257] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 09/29/2020] [Accepted: 10/04/2020] [Indexed: 12/24/2022]
Abstract
COVID-19 is a disease that causes symptoms in the lungs and causes deaths around the world. Studies are ongoing for the diagnosis and treatment of this disease, which is defined as a pandemic. Early diagnosis of this disease is important for human life. This process is progressing rapidly with diagnostic studies based on deep learning. Therefore, to contribute to this field, a deep learning-based approach that can be used for early diagnosis of the disease is proposed in our study. In this approach, a data set consisting of 3 classes of COVID19, normal and pneumonia lung X-ray images was created, with each class containing 364 images. Pre-processing was performed using the image contrast enhancement algorithm on the prepared data set and a new data set was obtained. Feature extraction was completed from this data set with deep learning models such as AlexNet, VGG19, GoogleNet, and ResNet. For the selection of the best potential features, two metaheuristic algorithms of binary particle swarm optimization and binary gray wolf optimization were used. After combining the features obtained in the feature selection of the enhancement data set, they were classified using SVM. The overall accuracy of the proposed approach was obtained as 99.38%. The results obtained by verification with two different metaheuristic algorithms proved that the approach we propose can help experts during COVID-19 diagnostic studies.
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Affiliation(s)
- Murat Canayaz
- Computer Engineering Department, Engineering Faculty, Van Yuzuncu Yil University, 65000, Van, Turkey
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368
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Lalmuanawma S, Hussain J, Chhakchhuak L. Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review. CHAOS, SOLITONS, AND FRACTALS 2020; 139:110059. [PMID: 32834612 PMCID: PMC7315944 DOI: 10.1016/j.chaos.2020.110059] [Citation(s) in RCA: 285] [Impact Index Per Article: 71.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Accepted: 06/23/2020] [Indexed: 05/17/2023]
Abstract
BACKGROUND AND OBJECTIVE During the recent global urgency, scientists, clinicians, and healthcare experts around the globe keep on searching for a new technology to support in tackling the Covid-19 pandemic. The evidence of Machine Learning (ML) and Artificial Intelligence (AI) application on the previous epidemic encourage researchers by giving a new angle to fight against the novel Coronavirus outbreak. This paper aims to comprehensively review the role of AI and ML as one significant method in the arena of screening, predicting, forecasting, contact tracing, and drug development for SARS-CoV-2 and its related epidemic. METHOD A selective assessment of information on the research article was executed on the databases related to the application of ML and AI technology on Covid-19. Rapid and critical analysis of the three crucial parameters, i.e., abstract, methodology, and the conclusion was done to relate to the model's possibilities for tackling the SARS-CoV-2 epidemic. RESULT This paper addresses on recent studies that apply ML and AI technology towards augmenting the researchers on multiple angles. It also addresses a few errors and challenges while using such algorithms in real-world problems. The paper also discusses suggestions conveying researchers on model design, medical experts, and policymakers in the current situation while tackling the Covid-19 pandemic and ahead. CONCLUSION The ongoing development in AI and ML has significantly improved treatment, medication, screening, prediction, forecasting, contact tracing, and drug/vaccine development process for the Covid-19 pandemic and reduce the human intervention in medical practice. However, most of the models are not deployed enough to show their real-world operation, but they are still up to the mark to tackle the SARS-CoV-2 epidemic.
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Affiliation(s)
- Samuel Lalmuanawma
- Department of Mathematics & Computer Science, Mizoram University, Tanhril, Aizawl, Mizoram, 796004, India
| | - Jamal Hussain
- Department of Mathematics & Computer Science, Mizoram University, Tanhril, Aizawl, Mizoram, 796004, India
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369
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Attallah O, Ragab DA, Sharkas M. MULTI-DEEP: A novel CAD system for coronavirus (COVID-19) diagnosis from CT images using multiple convolution neural networks. PeerJ 2020; 8:e10086. [PMID: 33062453 PMCID: PMC7532764 DOI: 10.7717/peerj.10086] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Accepted: 09/11/2020] [Indexed: 12/15/2022] Open
Abstract
Coronavirus (COVID-19) was first observed in Wuhan, China, and quickly propagated worldwide. It is considered the supreme crisis of the present era and one of the most crucial hazards threatening worldwide health. Therefore, the early detection of COVID-19 is essential. The common way to detect COVID-19 is the reverse transcription-polymerase chain reaction (RT-PCR) test, although it has several drawbacks. Computed tomography (CT) scans can enable the early detection of suspected patients, however, the overlap between patterns of COVID-19 and other types of pneumonia makes it difficult for radiologists to diagnose COVID-19 accurately. On the other hand, deep learning (DL) techniques and especially the convolutional neural network (CNN) can classify COVID-19 and non-COVID-19 cases. In addition, DL techniques that use CT images can deliver an accurate diagnosis faster than the RT-PCR test, which consequently saves time for disease control and provides an efficient computer-aided diagnosis (CAD) system. The shortage of publicly available datasets of CT images, makes the CAD system's design a challenging task. The CAD systems in the literature are based on either individual CNN or two-fused CNNs; one used for segmentation and the other for classification and diagnosis. In this article, a novel CAD system is proposed for diagnosing COVID-19 based on the fusion of multiple CNNs. First, an end-to-end classification is performed. Afterward, the deep features are extracted from each network individually and classified using a support vector machine (SVM) classifier. Next, principal component analysis is applied to each deep feature set, extracted from each network. Such feature sets are then used to train an SVM classifier individually. Afterward, a selected number of principal components from each deep feature set are fused and compared with the fusion of the deep features extracted from each CNN. The results show that the proposed system is effective and capable of detecting COVID-19 and distinguishing it from non-COVID-19 cases with an accuracy of 94.7%, AUC of 0.98 (98%), sensitivity 95.6%, and specificity of 93.7%. Moreover, the results show that the system is efficient, as fusing a selected number of principal components has reduced the computational cost of the final model by almost 32%.
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Affiliation(s)
- Omneya Attallah
- Electronics and Communications Engineering Department, Arab Academy for Science, Technology, and Maritime Transport (AASTMT), Alexandria, Egypt
| | - Dina A. Ragab
- Electronics and Communications Engineering Department, Arab Academy for Science, Technology, and Maritime Transport (AASTMT), Alexandria, Egypt
| | - Maha Sharkas
- Electronics and Communications Engineering Department, Arab Academy for Science, Technology, and Maritime Transport (AASTMT), Alexandria, Egypt
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370
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El-Kenawy ESM, Ibrahim A, Mirjalili S, Eid MM, Hussein SE. Novel Feature Selection and Voting Classifier Algorithms for COVID-19 Classification in CT Images. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2020; 8:179317-179335. [PMID: 34976558 PMCID: PMC8545288 DOI: 10.1109/access.2020.3028012] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 09/27/2020] [Indexed: 05/07/2023]
Abstract
Diagnosis is a critical preventive step in Coronavirus research which has similar manifestations with other types of pneumonia. CT scans and X-rays play an important role in that direction. However, processing chest CT images and using them to accurately diagnose COVID-19 is a computationally expensive task. Machine Learning techniques have the potential to overcome this challenge. This article proposes two optimization algorithms for feature selection and classification of COVID-19. The proposed framework has three cascaded phases. Firstly, the features are extracted from the CT scans using a Convolutional Neural Network (CNN) named AlexNet. Secondly, a proposed features selection algorithm, Guided Whale Optimization Algorithm (Guided WOA) based on Stochastic Fractal Search (SFS), is then applied followed by balancing the selected features. Finally, a proposed voting classifier, Guided WOA based on Particle Swarm Optimization (PSO), aggregates different classifiers' predictions to choose the most voted class. This increases the chance that individual classifiers, e.g. Support Vector Machine (SVM), Neural Networks (NN), k-Nearest Neighbor (KNN), and Decision Trees (DT), to show significant discrepancies. Two datasets are used to test the proposed model: CT images containing clinical findings of positive COVID-19 and CT images negative COVID-19. The proposed feature selection algorithm (SFS-Guided WOA) is compared with other optimization algorithms widely used in recent literature to validate its efficiency. The proposed voting classifier (PSO-Guided-WOA) achieved AUC (area under the curve) of 0.995 that is superior to other voting classifiers in terms of performance metrics. Wilcoxon rank-sum, ANOVA, and T-test statistical tests are applied to statistically assess the quality of the proposed algorithms as well.
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Affiliation(s)
- El-Sayed M. El-Kenawy
- Department of Communications and ElectronicsDelta Higher Institute of Engineering and Technology (DHIET)Mansoura35111Egypt
| | - Abdelhameed Ibrahim
- Computer Engineering and Control Systems DepartmentFaculty of EngineeringMansoura UniversityMansoura35516Egypt
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and OptimizationTorrens University AustraliaFortitude ValleyQLD4006Australia
- Yonsei Frontier Laboratory (YFL)Yonsei UniversitySeoul03722South Korea
| | - Marwa Metwally Eid
- Department of Communications and ElectronicsDelta Higher Institute of Engineering and Technology (DHIET)Mansoura35111Egypt
| | - Sherif E. Hussein
- Computer Engineering and Control Systems DepartmentFaculty of EngineeringMansoura UniversityMansoura35516Egypt
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371
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Z-scan method to measure the nonlinear optical behavior of cells for evaluating the cytotoxic effects of chemotherapy and hyperthermia treatments. Lasers Med Sci 2020; 36:1067-1075. [PMID: 32968961 DOI: 10.1007/s10103-020-03148-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2019] [Accepted: 09/16/2020] [Indexed: 10/23/2022]
Abstract
The effects of new treatments must be investigated in vitro before using clinically or in vivo. The aim of this study was to introduce the Z-scan technique as a fast, accurate, inexpensive, and safe in vitro method to distinguish the cytotoxic effects of various treatments. C6 and OLN-93 cell lines were prepared and treated with Temozolomide (TMZ), radiofrequency hyperthermia (HT), and chemo-hyperthermia (HT+TMZ). The cytotoxic effects of different treatments on both cell lines were evaluated using colony formation assay and Z-scan method. The results of colony assay showed that the surviving fraction (SF) of C6 cells treated with TMZ, HT, and HT + TMZ were significantly decreased compared to the control group. Whereas, hyperthermia treatment had no significant effect on the SF of OLN-93 cells. The results of Z-scan technique indicated that the control group of C6 cells had the negative nonlinear refractive index (n2). Whereas, the C6 cells treated with HT, TMZ, and HT + TMZ had the positive n2 index. The sign of n2 index in the control and HT groups of OLN-93 cells was positive but treatment of cells with TMZ and HT + TMZ changed the sign of it. Moreover, with increasing the cytotoxic effects of different treatments, the SF value of both cell lines decreased and the magnitude of n2 index increased. The results of Z-scan technique were completely in line with the results of colony assay. Therefore, Z-scan method could distinguish the cytotoxic effects of various treatments by examining the nonlinear optical properties of the samples.
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372
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Sakib S, Tazrin T, Fouda MM, Fadlullah ZM, Guizani M. DL-CRC: Deep Learning-Based Chest Radiograph Classification for COVID-19 Detection: A Novel Approach. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2020; 8:171575-171589. [PMID: 34976555 PMCID: PMC8675549 DOI: 10.1109/access.2020.3025010] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 08/26/2020] [Indexed: 05/05/2023]
Abstract
With the exponentially growing COVID-19 (coronavirus disease 2019) pandemic, clinicians continue to seek accurate and rapid diagnosis methods in addition to virus and antibody testing modalities. Because radiographs such as X-rays and computed tomography (CT) scans are cost-effective and widely available at public health facilities, hospital emergency rooms (ERs), and even at rural clinics, they could be used for rapid detection of possible COVID-19-induced lung infections. Therefore, toward automating the COVID-19 detection, in this paper, we propose a viable and efficient deep learning-based chest radiograph classification (DL-CRC) framework to distinguish the COVID-19 cases with high accuracy from other abnormal (e.g., pneumonia) and normal cases. A unique dataset is prepared from four publicly available sources containing the posteroanterior (PA) chest view of X-ray data for COVID-19, pneumonia, and normal cases. Our proposed DL-CRC framework leverages a data augmentation of radiograph images (DARI) algorithm for the COVID-19 data by adaptively employing the generative adversarial network (GAN) and generic data augmentation methods to generate synthetic COVID-19 infected chest X-ray images to train a robust model. The training data consisting of actual and synthetic chest X-ray images are fed into our customized convolutional neural network (CNN) model in DL-CRC, which achieves COVID-19 detection accuracy of 93.94% compared to 54.55% for the scenario without data augmentation (i.e., when only a few actual COVID-19 chest X-ray image samples are available in the original dataset). Furthermore, we justify our customized CNN model by extensively comparing it with widely adopted CNN architectures in the literature, namely ResNet, Inception-ResNet v2, and DenseNet that represent depth-based, multi-path-based, and hybrid CNN paradigms. The encouragingly high classification accuracy of our proposal implies that it can efficiently automate COVID-19 detection from radiograph images to provide a fast and reliable evidence of COVID-19 infection in the lung that can complement existing COVID-19 diagnostics modalities.
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Affiliation(s)
- Sadman Sakib
- Department of Computer ScienceLakehead UniversityThunder BayONP7B 5E1Canada
| | - Tahrat Tazrin
- Department of Computer ScienceLakehead UniversityThunder BayONP7B 5E1Canada
| | - Mostafa M. Fouda
- Department of Electrical and Computer EngineeringCollege of Science and EngineeringIdaho State UniversityPocatelloID83209USA
- Department of Electrical EngineeringFaculty of Engineering at ShoubraBenha UniversityCairo11629Egypt
| | - Zubair Md. Fadlullah
- Department of Computer ScienceLakehead UniversityThunder BayONP7B 5E1Canada
- Thunder Bay Regional Health Research Institute (TBRHRI)Thunder BayONP7B 7A5Canada
| | - Mohsen Guizani
- Department of Computer Science and EngineeringCollege of EngineeringQatar UniversityDohaQatar
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373
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Steinbuss G, Kriegsmann K, Kriegsmann M. Identification of Gastritis Subtypes by Convolutional Neuronal Networks on Histological Images of Antrum and Corpus Biopsies. Int J Mol Sci 2020; 21:ijms21186652. [PMID: 32932860 PMCID: PMC7555568 DOI: 10.3390/ijms21186652] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 09/07/2020] [Accepted: 09/08/2020] [Indexed: 12/02/2022] Open
Abstract
Background: Gastritis is a prevalent disease and commonly classified into autoimmune (A), bacterial (B), and chemical (C) type gastritis. While the former two subtypes are associated with an increased risk of developing gastric intestinal adenocarcinoma, the latter subtype is not. In this study, we evaluated the capability to classify common gastritis subtypes using convolutional neuronal networks on a small dataset of antrum and corpus biopsies. Methods: 1230 representative 500 × 500 µm images of 135 patients with type A, type B, and type C gastritis were extracted from scanned histological slides. Patients were allocated randomly into a training set (60%), a validation set (20%), and a test set (20%). One classifier for antrum and one classifier for corpus were trained and optimized. After optimization, the test set was analyzed using a joint result from both classifiers. Results: Overall accuracy in the test set was 84% and was particularly high for type B gastritis with a sensitivity of 100% and a specificity of 93%. Conclusions: Classification of gastritis subtypes is possible using convolutional neural networks on a small dataset of histopathological images of antrum and corpus biopsies. Deep learning strategies to support routine diagnostic pathology merit further evaluation.
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Affiliation(s)
- Georg Steinbuss
- Department of Hematology, Oncology and Rheumatology, University Hospital Heidelberg, 69120 Heidelberg, Germany; (G.S.); (K.K.)
- Institute of Pathology, University Hospital Heidelberg, 69120 Heidelberg, Germany
| | - Katharina Kriegsmann
- Department of Hematology, Oncology and Rheumatology, University Hospital Heidelberg, 69120 Heidelberg, Germany; (G.S.); (K.K.)
| | - Mark Kriegsmann
- Institute of Pathology, University Hospital Heidelberg, 69120 Heidelberg, Germany
- Correspondence: ; Tel.: +49-6221-56-36930
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Abstract
As of the end of 2019, the world suffered from a disease caused by the SARS-CoV-2 virus, which has become the pandemic COVID-19. This aggressive disease deteriorates the human respiratory system. Patients with COVID-19 can develop symptoms that belong to the common flu, pneumonia, and other respiratory diseases in the first four to ten days after they have been infected. As a result, it can cause misdiagnosis between patients with COVID-19 and typical pneumonia. Some deep-learning techniques can help physicians to obtain an effective pre-diagnosis. The content of this article consists of a deep-learning model, specifically a convolutional neural network with pre-trained weights, which allows us to use transfer learning to obtain new retrained models to classify COVID-19, pneumonia, and healthy patients. One of the main findings of this article is that the following relevant result was obtained in the dataset that we used for the experiments: all the patients infected with SARS-CoV-2 and all the patients infected with pneumonia were correctly classified. These results allow us to conclude that the proposed method in this article may be useful to help physicians decide the diagnoses related to COVID-19 and typical pneumonia.
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COVID-19 Deep Learning Prediction Model Using Publicly Available Radiologist-Adjudicated Chest X-Ray Images as Training Data: Preliminary Findings. Int J Biomed Imaging 2020; 2020:8828855. [PMID: 32849861 PMCID: PMC7439162 DOI: 10.1155/2020/8828855] [Citation(s) in RCA: 68] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2020] [Revised: 07/27/2020] [Accepted: 08/07/2020] [Indexed: 12/23/2022] Open
Abstract
The key component in deep learning research is the availability of training data sets. With a limited number of publicly available COVID-19 chest X-ray images, the generalization and robustness of deep learning models to detect COVID-19 cases developed based on these images are questionable. We aimed to use thousands of readily available chest radiograph images with clinical findings associated with COVID-19 as a training data set, mutually exclusive from the images with confirmed COVID-19 cases, which will be used as the testing data set. We used a deep learning model based on the ResNet-101 convolutional neural network architecture, which was pretrained to recognize objects from a million of images and then retrained to detect abnormality in chest X-ray images. The performance of the model in terms of area under the receiver operating curve, sensitivity, specificity, and accuracy was 0.82, 77.3%, 71.8%, and 71.9%, respectively. The strength of this study lies in the use of labels that have a strong clinical association with COVID-19 cases and the use of mutually exclusive publicly available data for training, validation, and testing.
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COVIDiag: a clinical CAD system to diagnose COVID-19 pneumonia based on CT findings. Eur Radiol 2020; 31:121-130. [PMID: 32740817 PMCID: PMC7395802 DOI: 10.1007/s00330-020-07087-y] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 06/16/2020] [Accepted: 07/20/2020] [Indexed: 02/07/2023]
Abstract
Objectives CT findings of COVID-19 look similar to other atypical and viral (non-COVID-19) pneumonia diseases. This study proposes a clinical computer-aided diagnosis (CAD) system using CT features to automatically discriminate COVID-19 from non-COVID-19 pneumonia patients. Methods Overall, 612 patients (306 COVID-19 and 306 non-COVID-19 pneumonia) were recruited. Twenty radiological features were extracted from CT images to evaluate the pattern, location, and distribution of lesions of patients in both groups. All significant CT features were fed in five classifiers namely decision tree, K-nearest neighbor, naïve Bayes, support vector machine, and ensemble to evaluate the best performing CAD system in classifying COVID-19 and non-COVID-19 cases. Results Location and distribution pattern of involvement, number of the lesion, ground-glass opacity (GGO) and crazy-paving, consolidation, reticular, bronchial wall thickening, nodule, air bronchogram, cavity, pleural effusion, pleural thickening, and lymphadenopathy are the significant features to classify COVID-19 from non-COVID-19 groups. Our proposed CAD system obtained the sensitivity, specificity, and accuracy of 0.965, 93.54%, 90.32%, and 91.94%, respectively, using ensemble (COVIDiag) classifier. Conclusions This study proposed a COVIDiag model obtained promising results using CT radiological routine features. It can be considered an adjunct tool by the radiologists during the current COVID-19 pandemic to make an accurate diagnosis. Key Points • Location and distribution of involvement, number of lesions, GGO and crazy-paving, consolidation, reticular, bronchial wall thickening, nodule, air bronchogram, cavity, pleural effusion, pleural thickening, and lymphadenopathy are the significant features between COVID-19 from non-COVID-19 groups. • The proposed CAD system, COVIDiag, could diagnose COVID-19 pneumonia cases with an AUC of 0.965 (sensitivity = 93.54%; specificity = 90.32%; and accuracy = 91.94%). • The AUC, sensitivity, specificity, and accuracy obtained by radiologist diagnosis are 0.879, 87.10%, 88.71%, and 87.90%, respectively. Electronic supplementary material The online version of this article (10.1007/s00330-020-07087-y) contains supplementary material, which is available to authorized users.
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377
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Shibly KH, Dey SK, Islam MTU, Rahman MM. COVID faster R-CNN: A novel framework to Diagnose Novel Coronavirus Disease (COVID-19) in X-Ray images. INFORMATICS IN MEDICINE UNLOCKED 2020; 20:100405. [PMID: 32835082 PMCID: PMC7395610 DOI: 10.1016/j.imu.2020.100405] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Revised: 07/20/2020] [Accepted: 07/25/2020] [Indexed: 12/23/2022] Open
Abstract
COVID-19 or novel coronavirus disease, which has already been declared as a worldwide pandemic, at first had an outbreak in a large city of China, named Wuhan. More than two hundred countries around the world have already been affected by this severe virus as it spreads by human interaction. Moreover, the symptoms of novel coronavirus are quite similar to the general seasonal flu. Screening of infected patients is considered as a critical step in the fight against COVID-19. As there are no distinctive COVID-19 positive case detection tools available, the need for supporting diagnostic tools has increased. Therefore, it is highly relevant to recognize positive cases as early as possible to avoid further spreading of this epidemic. However, there are several methods to detect COVID-19 positive patients, which are typically performed based on respiratory samples and among them, a critical approach for treatment is radiologic imaging or X-Ray imaging. Recent findings from X-Ray imaging techniques suggest that such images contain relevant information about the SARS-CoV-2 virus. Application of Deep Neural Network (DNN) techniques coupled with radiological imaging can be helpful in the accurate identification of this disease, and can also be supportive in overcoming the issue of a shortage of trained physicians in remote communities. In this article, we have introduced a VGG-16 (Visual Geometry Group, also called OxfordNet) Network-based Faster Regions with Convolutional Neural Networks (Faster R-CNN) framework to detect COVID-19 patients from chest X-Ray images using an available open-source dataset. Our proposed approach provides a classification accuracy of 97.36%, 97.65% of sensitivity, and a precision of 99.28%. Therefore, we believe this proposed method might be of assistance for health professionals to validate their initial assessment towards COVID-19 patients.
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Affiliation(s)
- Kabid Hassan Shibly
- Department of Computer Science and Engineering (CSE), Dhaka International University (DIU), Dhaka, 1205, Bangladesh
| | - Samrat Kumar Dey
- Department of Computer Science and Engineering (CSE), Dhaka International University (DIU), Dhaka, 1205, Bangladesh
| | - Md Tahzib-Ul Islam
- Department of Computer Science and Engineering (CSE), Dhaka International University (DIU), Dhaka, 1205, Bangladesh
| | - Md Mahbubur Rahman
- Department of Computer Science and Engineering (CSE), Military Institute of Science and Technology (MIST), Mirpur Cantonment, Dhaka, 1216, Bangladesh
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378
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Farhat H, Sakr GE, Kilany R. Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19. MACHINE VISION AND APPLICATIONS 2020; 31:53. [PMID: 32834523 PMCID: PMC7386599 DOI: 10.1007/s00138-020-01101-5] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 06/21/2020] [Accepted: 07/07/2020] [Indexed: 05/07/2023]
Abstract
Shortly after deep learning algorithms were applied to Image Analysis, and more importantly to medical imaging, their applications increased significantly to become a trend. Likewise, deep learning applications (DL) on pulmonary medical images emerged to achieve remarkable advances leading to promising clinical trials. Yet, coronavirus can be the real trigger to open the route for fast integration of DL in hospitals and medical centers. This paper reviews the development of deep learning applications in medical image analysis targeting pulmonary imaging and giving insights of contributions to COVID-19. It covers more than 160 contributions and surveys in this field, all issued between February 2017 and May 2020 inclusively, highlighting various deep learning tasks such as classification, segmentation, and detection, as well as different pulmonary pathologies like airway diseases, lung cancer, COVID-19 and other infections. It summarizes and discusses the current state-of-the-art approaches in this research domain, highlighting the challenges, especially with COVID-19 pandemic current situation.
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Affiliation(s)
- Hanan Farhat
- Saint Joseph University of Beirut, Mar Roukos, Beirut, Lebanon
| | - George E. Sakr
- Saint Joseph University of Beirut, Mar Roukos, Beirut, Lebanon
| | - Rima Kilany
- Saint Joseph University of Beirut, Mar Roukos, Beirut, Lebanon
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379
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Hussain AA, Bouachir O, Al-Turjman F, Aloqaily M. AI Techniques for COVID-19. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2020; 8:128776-128795. [PMID: 34976554 PMCID: PMC8545328 DOI: 10.1109/access.2020.3007939] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Accepted: 07/04/2020] [Indexed: 05/18/2023]
Abstract
Artificial Intelligence (AI) intent is to facilitate human limits. It is getting a standpoint on human administrations, filled by the growing availability of restorative clinical data and quick progression of insightful strategies. Motivated by the need to highlight the need for employing AI in battling the COVID-19 Crisis, this survey summarizes the current state of AI applications in clinical administrations while battling COVID-19. Furthermore, we highlight the application of Big Data while understanding this virus. We also overview various intelligence techniques and methods that can be applied to various types of medical information-based pandemic. We classify the existing AI techniques in clinical data analysis, including neural systems, classical SVM, and edge significant learning. Also, an emphasis has been made on regions that utilize AI-oriented cloud computing in combating various similar viruses to COVID-19. This survey study is an attempt to benefit medical practitioners and medical researchers in overpowering their faced difficulties while handling COVID-19 big data. The investigated techniques put forth advances in medical data analysis with an exactness of up to 90%. We further end up with a detailed discussion about how AI implementation can be a huge advantage in combating various similar viruses.
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Affiliation(s)
- Adedoyin Ahmed Hussain
- Department of Computer EngineeringNear East University99138NicosiaMersin 10Turkey
- Research Centre for AI and IoTDepartment of Artificial Intelligence EngineeringNear East University99138NicosiaMersin 10Turkey
| | - Ouns Bouachir
- Department of Computer EngineeringZayed UniversityDubaiUnited Arab Emirates
- College of Technological InnovationZayed UniversityDubaiUnited Arab Emirates
| | - Fadi Al-Turjman
- Research Centre for AI and IoTDepartment of Artificial Intelligence EngineeringNear East University99138NicosiaMersin 10Turkey
| | - Moayad Aloqaily
- College of EngineeringAl Ain UniversityAl AinUnited Arab Emirates
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380
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Wynants L, Van Calster B, Collins GS, Riley RD, Heinze G, Schuit E, Bonten MMJ, Dahly DL, Damen JAA, Debray TPA, de Jong VMT, De Vos M, Dhiman P, Haller MC, Harhay MO, Henckaerts L, Heus P, Kammer M, Kreuzberger N, Lohmann A, Luijken K, Ma J, Martin GP, McLernon DJ, Andaur Navarro CL, Reitsma JB, Sergeant JC, Shi C, Skoetz N, Smits LJM, Snell KIE, Sperrin M, Spijker R, Steyerberg EW, Takada T, Tzoulaki I, van Kuijk SMJ, van Bussel B, van der Horst ICC, van Royen FS, Verbakel JY, Wallisch C, Wilkinson J, Wolff R, Hooft L, Moons KGM, van Smeden M. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ 2020; 369:m1328. [PMID: 32265220 PMCID: PMC7222643 DOI: 10.1136/bmj.m1328] [Citation(s) in RCA: 1649] [Impact Index Per Article: 412.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/31/2020] [Indexed: 12/12/2022]
Abstract
OBJECTIVE To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease. DESIGN Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group. DATA SOURCES PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020. STUDY SELECTION Studies that developed or validated a multivariable covid-19 related prediction model. DATA EXTRACTION At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). RESULTS 37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models. CONCLUSION Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. SYSTEMATIC REVIEW REGISTRATION Protocol https://osf.io/ehc47/, registration https://osf.io/wy245. READERS' NOTE This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.
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Affiliation(s)
- Laure Wynants
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Peter Debyeplein 1, 6229 HA Maastricht, Netherlands
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
| | - Ben Van Calster
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, Netherlands
| | - Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Musculoskeletal Sciences, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Richard D Riley
- Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Keele, UK
| | - Georg Heinze
- Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Ewoud Schuit
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Marc M J Bonten
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Department of Medical Microbiology, University Medical Centre Utrecht, Utrecht, Netherlands
| | - Darren L Dahly
- HRB Clinical Research Facility, Cork, Ireland
- School of Public Health, University College Cork, Cork, Ireland
| | - Johanna A A Damen
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Thomas P A Debray
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Valentijn M T de Jong
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Maarten De Vos
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Electrical Engineering, ESAT Stadius, KU Leuven, Leuven, Belgium
| | - Paul Dhiman
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Musculoskeletal Sciences, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Maria C Haller
- Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
- Ordensklinikum Linz, Hospital Elisabethinen, Department of Nephrology, Linz, Austria
| | - Michael O Harhay
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Palliative and Advanced Illness Research Center and Division of Pulmonary and Critical Care Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Liesbet Henckaerts
- Department of Microbiology, Immunology and Transplantation, KU Leuven-University of Leuven, Leuven, Belgium
- Department of General Internal Medicine, KU Leuven-University Hospitals Leuven, Leuven, Belgium
| | - Pauline Heus
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Michael Kammer
- Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
- Department of Nephrology, Medical University of Vienna, Vienna, Austria
| | - Nina Kreuzberger
- Evidence-Based Oncology, Department I of Internal Medicine and Centre for Integrated Oncology Aachen Bonn Cologne Dusseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Anna Lohmann
- Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, Netherlands
| | - Kim Luijken
- Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, Netherlands
| | - Jie Ma
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Glen P Martin
- Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
| | - David J McLernon
- Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK
| | - Constanza L Andaur Navarro
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Johannes B Reitsma
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Jamie C Sergeant
- Centre for Biostatistics, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
- Centre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Chunhu Shi
- Division of Nursing, Midwifery and Social Work, School of Health Sciences, University of Manchester, Manchester, UK
| | - Nicole Skoetz
- Department of Nephrology, Medical University of Vienna, Vienna, Austria
| | - Luc J M Smits
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Peter Debyeplein 1, 6229 HA Maastricht, Netherlands
| | - Kym I E Snell
- Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Keele, UK
| | - Matthew Sperrin
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - René Spijker
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Amsterdam UMC, University of Amsterdam, Amsterdam Public Health, Medical Library, Netherlands
| | - Ewout W Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, Netherlands
| | - Toshihiko Takada
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Ioanna Tzoulaki
- Department of Epidemiology and Biostatistics, Imperial College London School of Public Health, London, UK
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - Sander M J van Kuijk
- Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre+, Maastricht, Netherlands
| | - Bas van Bussel
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Peter Debyeplein 1, 6229 HA Maastricht, Netherlands
- Department of Intensive Care, Maastricht University Medical Centre+, Maastricht University, Maastricht, Netherlands
| | - Iwan C C van der Horst
- Department of Intensive Care, Maastricht University Medical Centre+, Maastricht University, Maastricht, Netherlands
| | - Florien S van Royen
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Jan Y Verbakel
- EPI-Centre, Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Christine Wallisch
- Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
- Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
| | - Jack Wilkinson
- Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
| | | | - Lotty Hooft
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Maarten van Smeden
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
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Yoo SJ, Goo JM, Yoon SH. Role of Chest Radiographs and CT Scans and the Application of Artificial Intelligence in Coronavirus Disease 2019. JOURNAL OF THE KOREAN SOCIETY OF RADIOLOGY 2020; 81:1334-1347. [PMID: 36237723 PMCID: PMC9431829 DOI: 10.3348/jksr.2020.0138] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 09/02/2020] [Accepted: 09/15/2020] [Indexed: 01/08/2023]
Abstract
코로나바이러스감염증-19 (coronavirus disease 2019; 이하 COVID-19)는 전 세계적 대유행 질환으로 인류 보건을 위협하고 있다. 흉부 CT 및 흉부X선사진은 COVID-19의 표준 진단검사인 역전사 중합효소 연쇄반응에 더하여 COVID-19 진단 및 중증도 평가에서 중요한 역할을 하고 있다. 본 종설에서는 흉부 CT 및 흉부X선사진의 COVID-19 폐렴에 대한 현재 역할에 대하여 살펴보고 인공지능을 적용한 대표적 초기 연구들과 저자들의 경험을 소개함으로써 향후 활용가치에 대해 살펴보고자 한다.
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Affiliation(s)
- Seung-Jin Yoo
- Department of Radiology, Hanyang University Medical Center, Hanyang University College of Medicine, Seoul, Korea
| | - Jin Mo Goo
- Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Korea
| | - Soon Ho Yoon
- Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Korea
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