The Role of Chest Computed Tomography in Asymptomatic Patients of Positive Coronavirus Disease 2019: A Case and Literature Review

Shatri, Jeton; Tafilaj, Lirim; Turkaj, Agon; Dedushi, Kreshnike; Shatri, Mete; Bexheti, Sadi; Mucaj, Serbeze Kabashi

doi:10.25259/jcis_58_2020

Cited by 14 publications

(9 citation statements)

References 6 publications

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“…Patients are undergoing routine CT examinations for non-COVID-19 related reasons. For example, CT examinations may be conducted for routine cancer screening, monitoring for elective surgical procedures ( 8 ), and neurological examinations ( 9 ). Since such CT examinations are being conducted as a routine procedure regardless of COVID-19, there are no additional cost or resource constraints associated with leveraging such examinations for COVID-19 screening as well.…”

Section: Introductionmentioning

confidence: 99%

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

2020

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

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Section: Introductionmentioning

confidence: 99%

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

2020

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“…Clinical institutes around the world have explored the use of computed tomography (CT) imaging as an effective, complementary screening tool alongside RT-PCR ( 2 , 5 , 6 ). In particular, studies have shown CT to have great utility in detecting COVID-19 infections during routine CT examinations for non-COVID-19 related reasons such as elective surgical procedure monitoring and neurological examinations ( 7 , 8 ). Other scenarios where CT imaging has been leveraged include cases where patients have worsening respiratory complications, as well as cases where patients with negative RT-PCR test results are suspected to be COVID-19 positive due to other factors.…”

Section: Introductionmentioning

confidence: 99%

COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 From Chest CT Images Through Bigger, More Diverse Learning

et al. 2022

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The COVID-19 pandemic continues to rage on, with multiple waves causing substantial harm to health and economies around the world. Motivated by the use of computed tomography (CT) imaging at clinical institutes around the world as an effective complementary screening method to RT-PCR testing, we introduced COVID-Net CT, a deep neural network tailored for detection of COVID-19 cases from chest CT images, along with a large curated benchmark dataset comprising 1,489 patient cases as part of the open-source COVID-Net initiative. However, one potential limiting factor is restricted data quantity and diversity given the single nation patient cohort used in the study. To address this limitation, in this study we introduce enhanced deep neural networks for COVID-19 detection from chest CT images which are trained using a large, diverse, multinational patient cohort. We accomplish this through the introduction of two new CT benchmark datasets, the largest of which comprises a multinational cohort of 4,501 patients from at least 16 countries. To the best of our knowledge, this represents the largest, most diverse multinational cohort for COVID-19 CT images in open-access form. Additionally, we introduce a novel lightweight neural network architecture called COVID-Net CT S, which is significantly smaller and faster than the previously introduced COVID-Net CT architecture. We leverage explainability to investigate the decision-making behavior of the trained models and ensure that decisions are based on relevant indicators, with the results for select cases reviewed and reported on by two board-certified radiologists with over 10 and 30 years of experience, respectively. The best-performing deep neural network in this study achieved accuracy, COVID-19 sensitivity, positive predictive value, specificity, and negative predictive value of 99.0%/99.1%/98.0%/99.4%/99.7%, respectively. Moreover, explainability-driven performance validation shows consistency with radiologist interpretation by leveraging correct, clinically relevant critical factors. The results are promising and suggest the strong potential of deep neural networks as an effective tool for computer-aided COVID-19 assessment. While not a production-ready solution, we hope the open-source, open-access release of COVID-Net CT-2 and the associated benchmark datasets will continue to enable researchers, clinicians, and citizen data scientists alike to build upon them.

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“…The severity grading is based on any opacication of lung parenchyma including (8) ground glass opacities, consolidation . We speculate that this relatively uncomplicated method could provide objective means to speed up the identication of patients with severe disease, especially in conditions of meager availability of healthcare resources and to ( 9,10).…”

Section: Discussionmentioning

confidence: 99%

Radiological Evaluation and Correlation of Ct Severity Scoring With the Clinical Outcome in Rt-PCR Confirmed Covid-19 Infected Patients

Parthasarathy

Anand

2021

ijsr

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Background Radiological evaluation of the severity of lung inflammation in Covid-19 patients could provide an objective approach to rapidly identify the treatment modalities and to calculate the duration of hospitalization, need for intubation and ICU stay thereby analyzing and managing the clinical outcome of the patient. Purpose To identify the significant relationship between CT severity score and clinical outcome among RT-PCR confirmed Covid-19 infected patients Materials and Methods Inclusion of 40 patients with COVID-19 confirmed by positive real-time reverse transcriptase polymerase chain reaction on throat swabs underwent High resolution chest CT (7 men and 33 women).The CT-SS was defined by summing up individual scores from 20 lung regions; scores of 0, 1, and 2 were respectively assigned for each region if parenchymal opacification involved 0%, less than 50%, or equal or more than 50% of each region (theoretical range of CT-SS from 0 to 40). The clinical and laboratory data were collected and analyzed. Results All patients (n=16) who underwent intubation died invariably. The lung findings in all patients who died consist of co-existence of Consolidation and ground glass opacities in periphery and central region of lungs. Age is a predominant determinant factor for the course and outcome of disease as 17 patients with a mean age of 62 died when compared to 23 patients alive with a mean age of 47. This implies that mortality has a direct relationship with age. Out of 19 patients who had a CT –Severity score of >19 (Severe), 17 were shifted to ICU and 15 patients died. Only 4 patients recovered. Out of 5 patients whose CT-SS score were < 19, 5 patients were shifted to ICU and 2 cases died. 95 % of Covid-19 positive patients had Fever as their predominant complaint. The results are statistically significant with a p value of <0.01 and specificity of 82.6% and sensitivity of 88.2% Conclusion Along with RT-PCR, CT severity scoring is invariably a valuable modality for the management and planning of treatment for the patients by predicting the prognosis. Summary The chest CT severity score could be used to rapidly identify patients with severe forms of COVID-19 and clearly impacts the outcome of the patients.

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The Role of Chest Computed Tomography in Asymptomatic Patients of Positive Coronavirus Disease 2019: A Case and Literature Review

Cited by 14 publications

References 6 publications

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

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

COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 From Chest CT Images Through Bigger, More Diverse Learning

Radiological Evaluation and Correlation of Ct Severity Scoring With the Clinical Outcome in Rt-PCR Confirmed Covid-19 Infected Patients

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