scholarly journals High resolution computed tomography for the diagnosis of 2019 novel coronavirus (2019-nCoV) pneumonia: a study from multiple medical centers in western China

2020 ◽  
Author(s):  
Hong-Wei Li ◽  
Li-Hua Zhuo ◽  
Gao-Wu Yan ◽  
Ji-Sheng Wang ◽  
Guo-Ping Huang ◽  
...  
Keyword(s):  
Computed Tomography ◽  
High Resolution ◽  
Early Detection ◽  
Western China ◽  
Imaging Features ◽  
High Resolution Computed Tomography ◽  
Medical Centers ◽  
Detection And Diagnosis ◽  
Novel Coronavirus ◽  
Early Detection And Diagnosis

Abstract Objective: To evaluate the role of high-resolution computed tomography (HRCT) in the diagnosis of 2019 novel coronavirus (2019-nCoV) pneumonia and to provide experience in the early detection and diagnosis of 2019-nCoV pneumonia. Methods: 72 patients confirmed to be infected with 2019-nCoV from multiple medical centers in western China were retrospectively analyzed, including epidemiologic characteristics, clinical manifestations, laboratory findings and HRCT chest features. Results: All patients had lung parenchymal abnormalities on HRCT scans, which were mostly multifocal in both lungs and asymmetric in all patients, and were mostly in the peripheral or subpleural lung regions in 52 patients (72.22%), in the central lung regions in sixteen (22.22%), and in both lungs, with "white lung "manifestations in four (5.56%). Subpleural multifocal consolidation was predominant abnormality in 38 patients (52.78%). Ground-glass opacity was seen in 34 patients (47.22%). Interlobular septal thickening was found in 18patients, of which eight had only generally mild thickening with no zonal predominance. Reticulation was seen in 8 patients (11.11%), in all of whom it was mild and randomly distributed. In addition, both lungs of 28 patients had two or three CT imaging features. Out of these 72 patients, 36 were diagnosed as early stage, 32 patients as progressive stage and 4 patient as severe stage pneumonia. Moreover, the diagnostic accuracy of HRCT features combined with epidemiological history was not significantly different from the detection of viral nucleic acid (all P >0.05). Conclusion: The HRCT features of 2019-nCoV pneumonia are characteristic to a certain degree, which when combined with epidemiological history yield high clinical value in the early detection and diagnosis of 2019-nCoV pneumonia.Authors Hong-Wei Li, Li-Hua Zhuo, Gao-Wu Yan contributed equally to this work.

scholarly journals The multicenter study of Chest HRCT imaging characteristics of 2019 Novel Coronavirus (COVID-19) Pneumonia

2020 ◽  
Author(s):  
Zhehao Lyu ◽  
Meiji Ren ◽  
Lian-Ming Wu ◽  
Yuxin Yang ◽  
Yi-Bo Lu ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Acute Respiratory Distress Syndrome ◽  
Distress Syndrome ◽  
Chest Ct ◽  
Imaging Features ◽  
Wholesale Market ◽  
High Resolution Computed Tomography ◽  
Imaging Characteristics ◽  
History Of ◽  
Novel Coronavirus

Abstract Background: In December 2019, a cluster of patients with pneumonia of unknown cause was linked to a seafood wholesale market in Wuhan, China. A novel coronavirus was detected, capable of infecting humans, on 6 January 2020 and termed COVID-19. By 16 February 2020, there were 51857 confirmed cases with 2019-nCoV (COVID-19) pneumonia in 25 countries. COVID-19 can also lead to acute respiratory distress syndrome (ARDS).Methods: 149 patients with 2019 Novel Coronavirus (COVID-19)pneumonia(68 males, 81 females, ages 1-89)from 6 research centers in China were diagnosed with positive 2019 Novel Coronavirus(COVID-19)nucleic acids antibodies. And their high-resolution computed tomography(HRCT) imaging datas were evaluated.Results: 136/149(91.3%)patients had a clear history of exposure to Wuhan. Fever (122/149, 81.9%)and cough(83/149, 55.7%)were the most common symptoms. The main imaging characteristics within 4 days of onset included 30(20.13%) cases of pure ground glass opacities (P<0.05), 38(25.50%) cases of GGO with reticulation(P<0.01), 12(8.05%) cases of consolidation(P<0.01). In the 5-8 days group, the main imaging features included 71(47.65%) cases of pGGO(P<0.05), 69(46.31%) cases of GGO with reticulation(P<0.01). In the 9-12 days group, the main feature was 85(57.04%) cases with GGO with reticulation(P<0.01). In the group of 13-16 days group, the main imaging characteristics included 48(32.21%) cases of GGO with reticulation(P < 0.01), 34(22.82%) cases of consolidation(P<0.01).Conclusion: Patients infected with COVID-19 pneumonia show more chest CT characteristics within 5-8 days after the onset of disease. The main manifestations included pGGO, GGO with reticulation, consolidation and GGO with consolidation.

Impact of Metaheuristic Techniques in Pandemic of COVID-19

Coronaviruses ◽  
2020 ◽  
Vol 01 ◽  
Author(s):  
Vijay Kumar ◽  
Dilbag Singh ◽  
Manjit Kaur
Keyword(s):  
Early Detection ◽  
Drug Combination ◽  
Genome Structure ◽  
Infected Person ◽  
Precautionary Measures ◽  
Detection And Diagnosis ◽  
Metaheuristic Techniques ◽  
Novel Coronavirus ◽  
Early Detection And Diagnosis ◽  
Automated Tool

: The early detection and diagnosis of novel coronavirus disease 2019 (COVID-19) is required to cure the disease. Metaheuristic techniques can be used to develop the automated tool for detecting the symptoms of infected person and provide the appropriate precautionary measures. The metaheuristic-based software can be designed to analyze the radiographic patterns of infected persons and determine the severity of COVID-19. The genome structure of coronavirus can be easily understand through metaheuristic techniques. Based on the genome structure, an effective drug combination can be explored by using metaheuristics for treatment of COVID-19.

scholarly journals Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Jun Chen ◽  
Lianlian Wu ◽  
Jun Zhang ◽  
Liang Zhang ◽  
Dexin Gong ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
High Resolution ◽  
Model Development ◽  
Imaging Method ◽  
High Resolution Computed Tomography ◽  
Expert Radiologist ◽  
Comparable Performance ◽  
Novel Coronavirus ◽  
External Test

Abstract Computed tomography (CT) is the preferred imaging method for diagnosing 2019 novel coronavirus (COVID19) pneumonia. We aimed to construct a system based on deep learning for detecting COVID-19 pneumonia on high resolution CT. For model development and validation, 46,096 anonymous images from 106 admitted patients, including 51 patients of laboratory confirmed COVID-19 pneumonia and 55 control patients of other diseases in Renmin Hospital of Wuhan University were retrospectively collected. Twenty-seven prospective consecutive patients in Renmin Hospital of Wuhan University were collected to evaluate the efficiency of radiologists against 2019-CoV pneumonia with that of the model. An external test was conducted in Qianjiang Central Hospital to estimate the system’s robustness. The model achieved a per-patient accuracy of 95.24% and a per-image accuracy of 98.85% in internal retrospective dataset. For 27 internal prospective patients, the system achieved a comparable performance to that of expert radiologist. In external dataset, it achieved an accuracy of 96%. With the assistance of the model, the reading time of radiologists was greatly decreased by 65%. The deep learning model showed a comparable performance with expert radiologist, and greatly improved the efficiency of radiologists in clinical practice.

scholarly journals Ultra-high-resolution computed tomography can demonstrate alveolar collapse in novel coronavirus (COVID-19) pneumonia

2020 ◽  
Vol 38 (5) ◽  
pp. 394-398 ◽  
Author(s):  
Tae Iwasawa ◽  
Midori Sato ◽  
Takafumi Yamaya ◽  
Yozo Sato ◽  
Yoshinori Uchida ◽  
...  
Keyword(s):  
Computed Tomography ◽  
High Resolution ◽  
High Resolution Computed Tomography ◽  
Alveolar Collapse ◽  
Novel Coronavirus

High-resolution computed tomography in the early detection of asbestosis

1994 ◽  
Vol 65 (5) ◽  
pp. 299-304 ◽  
Author(s):  
Panu Oksa ◽  
Hannu Suoranta ◽  
Heikki Koskinen ◽  
Anders Zitting ◽  
Henrik Nordman
Keyword(s):  
Computed Tomography ◽  
High Resolution ◽  
Early Detection ◽  
High Resolution Computed Tomography

scholarly journals Assessment of Disease Activity and Complications in Patients of Pulmonary Tuberculosis by High Resolution Computed Tomography

2021 ◽  
Author(s):  
Ashwani Jain ◽  
Ankur Malhotra ◽  
Deepti Arora ◽  
Mazher Maqusood ◽  
Sunil Kumar
Keyword(s):  
Computed Tomography ◽  
High Resolution ◽  
Pulmonary Tuberculosis ◽  
Disease Activity ◽  
Post Treatment ◽  
Research Centre ◽  
Imaging Features ◽  
Ct Scanner ◽  
High Resolution Computed Tomography

Background: Tuberculosis (TB) is a global health problem and the second most common infectious cause of death. High-resolution computed tomography (HRCT) is far more superior to chest radiography as well as conventional CT for analyzing the pulmonary parenchyma. This study aimed to evaluate the role of HRCT in pulmonary tuberculosis (PTB) with respect to disease activity and complication after anti-tubercular therapy (ATT). Methods: This prospective observational study was conducted in the Department of Radiodiagnosis, Teerthanker Mahaveer Medical College & Research Centre (TMMC&RC) for a period of 1.5 years. A total of 50 cases of newly diagnosed TB were included in the study and a standard six-month ATT was given to the patients. Pulmonary involvement was evaluated by HRCT (128 slice multi-detector PHILIPS INGENUITY CT scanner), twice for each patient (first scan after diagnosis and second after treatment completion). The acquired HRCT images were reconstructed on a highresolution lung algorithm and parenchymal, bronchial, and extra parenchymal findings were recorded systematically. Results: Out of the 50 patients, 5 died within two months of the initiation of treatment and four were lost to follow-up. Thus, post treatment follow-up sample size was reduced to 41 patients. Ill-defined nodules (96%), tree-in-bud pattern (74%), consolidation (86%), cavitary lesions (98%), and ground glass opacities (58%) were the main imaging features of active cases of TB on HRCT. Resolution to thin-walled cavitary lesions (36.5%), bronchiectasis (41.5%), and fibrotic (parenchymal) bands (66%) were common complications or sequelae which were observed after completion of treatment. Conclusion: HRCT thorax is a sensitive modality for evaluation of parenchymal and airway manifestations in cases of PTB and can aid in differentiation of active disease from healed disease. It allows early identification of post-treatment complications and sequelae in patients of PTB.

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