Development of computer-aided model to differentiate COVID-19 from pulmonary edema in lung CT scan: EDECOVID-net

Velichko, N. Elena; Shariaty, Faridoddin; Orooji, Mahdi; Pavlov, A. V.; Pervunina, Tatiana; Zavjalov, Sergey V.; Khazaei, Razieh; Radmard, Amir Reza

doi:10.1016/j.compbiomed.2021.105172

Cited by 12 publications

(6 citation statements)

References 53 publications

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“…Regarding the results of COVID‐19 radiomics, literature review showed that multiple studies evaluated the ability of CT‐based radiomic features to differentiate COVID‐19 pneumonia from other types of pneumonia or other lung diseases. For instance, Velichko et al 32 studied a total of 5759 chest CT image patches of confirmed COVID‐19 pneumonia and 7958 CT images patches of pulmonary edema and proposed a model based on radiomic features to differentiate them. They achieved an AUC of 0.994, which performed better compared to other known neural networks.…”

Section: Discussionmentioning

confidence: 99%

Differentiation of COVID‐19 pneumonia from other lung diseases using CT radiomic features and machine learning: A large multicentric cohort study

Shiri,

Salimi,

Saberi

et al. 2024

Int J Imaging Syst Tech

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To derive and validate an effective machine learning and radiomics‐based model to differentiate COVID‐19 pneumonia from other lung diseases using a large multi‐centric dataset. In this retrospective study, we collected 19 private and five public datasets of chest CT images, accumulating to 26 307 images (15 148 COVID‐19; 9657 other lung diseases including non‐COVID‐19 pneumonia, lung cancer, pulmonary embolism; 1502 normal cases). We tested 96 machine learning‐based models by cross‐combining four feature selectors (FSs) and eight dimensionality reduction techniques with eight classifiers. We trained and evaluated our models using three different strategies: #1, the whole dataset (15 148 COVID‐19 and 11 159 other); #2, a new dataset after excluding healthy individuals and COVID‐19 patients who did not have RT‐PCR results (12 419 COVID‐19 and 8278 other); and #3 only non‐COVID‐19 pneumonia patients and a random sample of COVID‐19 patients (3000 COVID‐19 and 2582 others) to provide balanced classes. The best models were chosen by one‐standard‐deviation rule in 10‐fold cross‐validation and evaluated on the hold out test sets for reporting. In strategy#1, Relief FS combined with random forest (RF) classifier resulted in the highest performance (accuracy = 0.96, AUC = 0.99, sensitivity = 0.98, specificity = 0.94, PPV = 0.96, and NPV = 0.96). In strategy#2, Recursive Feature Elimination (RFE) FS and RF classifier combination resulted in the highest performance (accuracy = 0.97, AUC = 0.99, sensitivity = 0.98, specificity = 0.95, PPV = 0.96, NPV = 0.98). Finally, in strategy #3, the ANOVA FS and RF classifier combination resulted in the highest performance (accuracy = 0.94, AUC =0.98, sensitivity = 0.96, specificity = 0.93, PPV = 0.93, NPV = 0.96). Lung radiomic features combined with machine learning algorithms can enable the effective diagnosis of COVID‐19 pneumonia in CT images without the use of additional tests.

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Section: Discussionmentioning

confidence: 99%

Differentiation of COVID‐19 pneumonia from other lung diseases using CT radiomic features and machine learning: A large multicentric cohort study

Shiri,

Salimi,

Saberi

et al. 2024

Int J Imaging Syst Tech

Self Cite

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“…A fusion of the classifiers was deployed as a strategy and the recall score increased to 99.2% while the F-1 score increased to 99.4%. Research in [ 88 ], examines the challenge of diagnosing patients with COVID-19 and differentiating it from pulmonary edema. A machine learning model EDECOVID-net was used to differentiate using lung Computed Tomography radiomic features.…”

Section: Covid-19 Prediction Using Deep Learningmentioning

confidence: 99%

A Comprehensive Review of Machine Learning Used to Combat COVID-19

et al. 2022

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Coronavirus disease (COVID-19) has had a significant impact on global health since the start of the pandemic in 2019. As of June 2022, over 539 million cases have been confirmed worldwide with over 6.3 million deaths as a result. Artificial Intelligence (AI) solutions such as machine learning and deep learning have played a major part in this pandemic for the diagnosis and treatment of COVID-19. In this research, we review these modern tools deployed to solve a variety of complex problems. We explore research that focused on analyzing medical images using AI models for identification, classification, and tissue segmentation of the disease. We also explore prognostic models that were developed to predict health outcomes and optimize the allocation of scarce medical resources. Longitudinal studies were conducted to better understand COVID-19 and its effects on patients over a period of time. This comprehensive review of the different AI methods and modeling efforts will shed light on the role that AI has played and what path it intends to take in the fight against COVID-19.

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“…Currently, the most effective measure to prevent COVID-19 transmission is to isolate and quarantine suspected cases. A study in [ 19 ] demonstrated the feasibility of using computer techniques and CT scans to differentiate COVID-19 from pulmonary edema. EDECOVID-net was developed.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Super-Resolution and Classification of Lung Disease Scans

et al. 2023

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Acute lower respiratory infection is a leading cause of death in developing countries. Hence, progress has been made for early detection and treatment. There is still a need for improved diagnostic and therapeutic strategies, particularly in resource-limited settings. Chest X-ray and computed tomography (CT) have the potential to serve as effective screening tools for lower respiratory infections, but the use of artificial intelligence (AI) in these areas is limited. To address this gap, we present a computer-aided diagnostic system for chest X-ray and CT images of several common pulmonary diseases, including COVID-19, viral pneumonia, bacterial pneumonia, tuberculosis, lung opacity, and various types of carcinoma. The proposed system depends on super-resolution (SR) techniques to enhance image details. Deep learning (DL) techniques are used for both SR reconstruction and classification, with the InceptionResNetv2 model used as a feature extractor in conjunction with a multi-class support vector machine (MCSVM) classifier. In this paper, we compare the proposed model performance to those of other classification models, such as Resnet101 and Inceptionv3, and evaluate the effectiveness of using both softmax and MCSVM classifiers. The proposed system was tested on three publicly available datasets of CT and X-ray images and it achieved a classification accuracy of 98.028% using a combination of SR and InceptionResNetv2. Overall, our system has the potential to serve as a valuable screening tool for lower respiratory disorders and assist clinicians in interpreting chest X-ray and CT images. In resource-limited settings, it can also provide a valuable diagnostic support.

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Development of computer-aided model to differentiate COVID-19 from pulmonary edema in lung CT scan: EDECOVID-net

Cited by 12 publications

References 53 publications

Differentiation of COVID‐19 pneumonia from other lung diseases using CT radiomic features and machine learning: A large multicentric cohort study

Differentiation of COVID‐19 pneumonia from other lung diseases using CT radiomic features and machine learning: A large multicentric cohort study

A Comprehensive Review of Machine Learning Used to Combat COVID-19

Simultaneous Super-Resolution and Classification of Lung Disease Scans

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