scholarly journals Multi-task multi-modality SVM for early COVID-19 Diagnosis using chest CT data

2022 ◽  
Vol 59 (1) ◽  
pp. 102782
Author(s):  
Rongyao Hu ◽  
Jiangzhang Gan ◽  
Xiaofeng Zhu ◽  
Tong Liu ◽  
Xiaoshuang Shi
Keyword(s):  
Chest Ct ◽  
Ct Data

scholarly journals Diagnostic value of lung ultrasound versus chest CT in COVID-19

2020 ◽  
Vol 11 (2) ◽  
pp. 5-18
Author(s):  
S. S. Petrikov ◽  
I. E. Popova ◽  
V. M. Abuchina ◽  
R. Sh. Muslimov ◽  
L. T. Khamidova ◽  
...  
Keyword(s):  
Diagnostic Accuracy ◽  
Lung Diseases ◽  
Structural Changes ◽  
Lung Ultrasound ◽  
Ground Glass Opacity ◽  
Diagnostic Value ◽  
Chest Ct ◽  
Ct Scans ◽  
Chest Computed Tomography ◽  
Ct Data

Lung ultrasound demonstrates a high diagnostic value in the assessment of lung diseases.Aim. To determine the diagnostic accuracy of lung ultrasound compared to chest computed tomography (CT) in the diagnosis of lung changes in COVID-19. Materials and methods. The retrospective study included 45 patients (28 men) aged 37 to 90 years who underwent polypositional lung ultrasound with an assessment of 14 zones. The study compared lung echograms with chest CT data in assessing the prevalence of the process and the nature of structural changes. The diagnostic accuracy, sensitivity, and specificity of lung ultrasound in comparison with CT scans were determined, 95% confidence intervals (CI) were calculated.Results. In 44 patients (98%), CT revealed pathological changes with subpleural localization in both lungs. Of these, in 30 cases, the inflammation was limited only to the subpleural parts, and in 14 cases, the changes spread to the basal parts of the lungs, while ultrasound revealed changes at the depth of the lesion no more than 4 cm. The lesion of 10–11 zones according to lung ultrasound corresponds to CT 1–2 degrees, the lesion of 13–14 zones — CT 3–4 degrees. The sensitivity of ultrasound to detect lung changes of various types was ≥ 92%. The highest sensitivity of 97.9% (95% CI: 92.8–99.8%) was determined for small consolidations on the background of interstitial changes (degree 1A+, 1B+), which corresponded to “crazy-paving” pattern on CT. The specificity depended on the nature of the changes and varied from 46.7 to 70.0%. Diagnostic accuracy was ≥ 81%, the maximum values of 90.6% (95% CI: 85.6–94.2%) were obtained for moderate interstitial changes (grade 1A) corresponding to ground-glass opacity (type one) according to CT data.Conclusion. The sensitivity of ultrasound to detect lung changes in COVID-19 is more than 90%. Lung ultrasound has some limitations: inability to determine the prevalence of the process clearly and identify centrally located areas of changes in the lung tissue.

scholarly journals Prediction of outcome in COVID-19 patients based on clinical and radiomics chest CT data

2021 ◽  
Author(s):  
Julien Guiot ◽  
Akshayaa Vaidyanathan ◽  
Fadila Zerka ◽  
Louis Deprez ◽  
Denis Danthine ◽  
...  
Keyword(s):  
Chest Ct ◽  
Prediction Of Outcome ◽  
Ct Data

Wavelet Compression of Low-Dose Chest CT Data: Effect on Lung Nodule Detection

Radiology ◽  
2003 ◽  
Vol 228 (1) ◽  
pp. 70-75 ◽  
Author(s):  
Jane P. Ko ◽  
Henry Rusinek ◽  
David P. Naidich ◽  
Georgeann McGuinness ◽  
Ami N. Rubinowitz ◽  
...  
Keyword(s):  
Low Dose ◽  
Lung Nodule ◽  
Chest Ct ◽  
Wavelet Compression ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Ct Data

scholarly journals Deep multi-path network integrating incomplete biomarker and chest CT data for evaluating lung cancer risk

2021 ◽  
Author(s):  
Riqiang Gao ◽  
Yucheng Tang ◽  
Kaiwen Xu ◽  
Michael Kammer ◽  
Sanja Antic ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Risk ◽  
Lung Cancer Risk ◽  
Chest Ct ◽  
Ct Data

scholarly journals The frequency and character of community-acquired pneumonia comparison before and during the COVID-19 epidemic in the multi-specialty hospital

2020 ◽  
Vol 1 (1) ◽  
pp. 37-47
Author(s):  
Stepan A. Yaremenko ◽  
Natalia A. Rucheva ◽  
Kirill N. Zhuravlev ◽  
Valentin E. Sinitsyn
Keyword(s):  
Retrospective Analysis ◽  
False Negative ◽  
Community Acquired Pneumonia ◽  
Chest Ct ◽  
Viral Pneumonia ◽  
Negative Results ◽  
False Negative Results ◽  
Polymerase Chain ◽  
Ct Data ◽  
Suspected Pneumonia

BACKGROUND:The 2019 coronavirus disease outbreak (COVID-19) quickly swept the world in just a month. Polymerase chain reaction (PCR) is used in the diagnosis of this disease, but this test has limitations related to false negative results, as well as PCR is a time-consuming procedure. Under these conditions, chest computed tomography (CT) can become one of the main methods in the Clinicians Arsenal used for early detection of COVID-19 in patients who first seek medical help. AIMS:comparison of the frequency of community-acquired pneumonia and its characteristics according to CT data before and during the COVID-19 epidemic and study of the possibilities of their timely detection and differential diagnosis. MATERIALS AND METHODS:A retrospective analysis of chest CT scans results was performed in Davydovsky hospital (Moscow) from April 1 to April 17, 2020. It included all patients diagnosed with viral pneumonia at the CT. All patients with suspected diagnosis of viral pneumonia underwent PCR testing. Retrospective analysis of chest CT data from patients admitted to the hospital with suspected pneumonia for the same period in 2019 was taken as a comparison group. RESULTS:For the period from April 1 to April 17, 2020 according to chest CT, pneumonia was diagnosed in 140 cases, of which 65 (46.4%) were described as viral, compared with the same period in 2019 7 diagnoses of viral pneumonia (10.3%) were described a significant increase in cases of viral pneumonia (5.723;p0.01). Results of PCR test in patients with viral pneumonia according to CT data was: positive in 34 (52.3%), negative in 22 (33.8%), 9 (13.9%) patients were not tested. When comparing the frequency of detection on CT of viral pneumonia patterns in patients for the same period of time in 2019 and 2020, no significant differences were found. The probability of COVID-19 due to results of chest CT was: average 13.8%, high 75.4%. The severity of viral pneumonia according to CT data was: light 38.5%, medium 46.2%, severe 12.3%, extremely severe 3.1%. CONCLUSIONS:Rapid CT diagnostics of COVID-19, even with false negative results of PCR tests, can help to isolate a patient with suspected COVID-19, start treatment on time and prevent the further spread of viral infection in a pandemic. Nevertheless, due to the non-specificity of the revealed picture, the possibilities of CT to identify lung lesions by specific viral agents are limited.

scholarly journals Are "fibrosis-like" strips on chest CT in patients with COVID-19 pneumonia true fibrosis?

2020 ◽  
Author(s):  
Qiulian Sun ◽  
Xinjian Xu ◽  
Hai Yang ◽  
Guanliang Wang ◽  
Jingjing Li ◽  
...  
Keyword(s):  
Ct Scan ◽  
Morphological Changes ◽  
Chest Ct ◽  
Inclusion Criteria ◽  
Rapid Absorption ◽  
Early Appearance ◽  
Process And Outcome ◽  
Ct Data ◽  
Chest Ct Scan

Abstract Objective: To investigate the evolution and characteristics of "fibrosis-like" strips on chest CT in patients with COVID-19 pneumonia. Methods: From January 17, 2020, to February 27, 2020, inpatients diagnosed with COVID-19 pneumonia in Enze Hospital, Taizhou, Zhejiang Province, were selected. The chest CT data of the patients were collected, and patients were included in the present study according to the predefined inclusion criteria. The dynamic evolution process and outcome of "fibrosis-like" strips on chest CT were analyzed.Results: A total of 36 patients (20 males and 16 females) with COVID-19 were included in the study, all of whom were diagnosed with mild or common COVID-19; "fibrosis-like" strips were observed in 29 patients (80.6%) on the first chest CT scan, and "fibrosis-like" strips were observed in 7 patients (19.4%) on the second chest CT scan, Repeated chest CT during the course of treatment showed that all patients with "fibrosis-like" strips had varying degrees of absorption. "Fibrosis-like" strips in 15 patients demonstrated a trend of first increasing in number, and then decreasing in number; "fibrosis-like" strips in 3 patients demonstrated a trend of first decreasing in number first, followed by an increase in number and then a decrease in number; and "fibrosis-like" strips in 18 patients demonstrated a trend of a gradual decrease in number. Follow-up chest CT after treatment showed that "fibrosis-like" strips in 15 patients completely disappeared in both lungs, and "fibrosis-like" strips completely disappeared in unilateral lung of 8 cases.Conclusion: In the present study, we observed that "fibrosis-like" strips in COVID-19 patients had characteristics of early appearance and rapid morphological changes as well as rapid absorption, suggesting that "fibrosis-like" strips may be a sign of subsegmental atelectasis rather than fibrotic changes.

Establishment of A Deformable Aorta Model Based on Patient's Chest CT Data

2009 ◽  
Author(s):  
Tatsushi Tokuyasu ◽  
Takashi Shuto ◽  
Kenji Yufu ◽  
Hironori Abe ◽  
Akira Marui ◽  
...  
Keyword(s):  
Chest Ct ◽  
Model Based ◽  
Ct Data

scholarly journals A Novel Implementation of Machine Learning for the Efficient, Explainable Diagnosis of COVID-19 from Chest CT

2021 ◽  
Author(s):  
Justin Liu
Keyword(s):  
Machine Learning ◽  
False Negative ◽  
Model Performance ◽  
Chest Ct ◽  
Health Crisis ◽  
Fine Grained ◽  
Novel Approach ◽  
Proposed Model ◽  
Ct Data ◽  
Computational Resources

Abstract Background: In a worldwide health crisis as severe as COVID-19, there has become a pressing need for rapid, reliable diagnostics. Currently, popular testing methods such as reversetranscription polymerase chain reaction (RT-PCR) can have high false negative rates. Consequently, COVID-19 patients are not accurately identified nor treated quickly enough to prevent transmission of the virus. However, the recent rise of medical CT data has presented promising avenues, since CT manifestations contain key characteristics indicative of COVID-19. Findings: This study aimed to take a novel approach in the machine learning-based detection of COVID-19 from chest CT scans. First, the dataset utilized in this study was derived from three major sources, comprising a total of 17,698 chest CT slices across 923 patient cases. Additionally, image preprocessing algorithms were developed to reduce noise by excluding irrelevant features. Transfer learning was also implemented with the EfficientNetB7 pre-trained model to provide a backbone architecture and save computational resources. Lastly, several explainability techniques were leveraged to qualitatively validate model performance by localizing infected regions and highlighting fine-grained pixel details. The proposed model attained an overall accuracy of 92.71% and a sensitivity of 95.79%. Explainability measures showed that the model correctly distinguished between relevant, critical features pertaining to COVID-19 chest CT images and normal controls.Conclusions: Deep learning frameworks provide efficient, human-interpretable COVID-19 diagnostics that could complement a radiologist’s decision or serve as an alternative screening tool. Future endeavors could provide insight into infection severity, patient risk stratification, and more precise visualizations

scholarly journals Are "fibrosis-like" strips on chest CT in patients with COVID-19 pneumonia true fibrosis?

2020 ◽  
Author(s):  
Qiulian Sun ◽  
Xinjian Xu ◽  
Hai Yang ◽  
Guanliang Wang ◽  
Jingjing Li ◽  
...  
Keyword(s):  
Ct Scan ◽  
Morphological Changes ◽  
Chest Ct ◽  
Inclusion Criteria ◽  
Rapid Absorption ◽  
Early Appearance ◽  
Process And Outcome ◽  
Ct Data ◽  
Chest Ct Scan

Abstract Objective: To investigate the evolution and characteristics of "fibrosis-like" strips on chest CT in patients with COVID-19 pneumonia.Methods: From January 17, 2020, to February 27, 2020, inpatients diagnosed with COVID-19 pneumonia in Enze Hospital, Taizhou, Zhejiang Province, were selected. The chest CT data of the patients were collected, and patients were included in the present study according to the predefined inclusion criteria. The dynamic evolution process and outcome of "fibrosis-like" strips on chest CT were analyzed.Results: A total of 36 patients (20 males and 16 females) with COVID-19 were included in the study, all of whom were diagnosed with mild or common COVID-19; "fibrosis-like" strips were observed in 29 patients (80.6%) on the first chest CT scan, and "fibrosis-like" strips were observed in 7 patients (19.4%) on the second chest CT scan, Repeated chest CT during the course of treatment showed that all patients with "fibrosis-like" strips had varying degrees of absorption. "Fibrosis-like" strips in 15 patients demonstrated a trend of first increasing in number, and then decreasing in number; "fibrosis-like" strips in 3 patients demonstrated a trend of first decreasing in number first, followed by an increase in number and then a decrease in number; and "fibrosis-like" strips in 18 patients demonstrated a trend of a gradual decrease in number. Follow-up chest CT after treatment showed that "fibrosis-like" strips in 15 patients completely disappeared in both lungs, and "fibrosis-like" strips completely disappeared in unilateral lung of 8 cases.Conclusion: In the present study, we observed that "fibrosis-like" strips in COVID-19 patients had characteristics of early appearance and rapid morphological changes as well as rapid absorption, suggesting that "fibrosis-like" strips may be a sign of subsegmental atelectasis rather than fibrotic changes.

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