Yam Code

New Paste Browse

https://www.selleckchem.com/products/gsk2879552-2hcl.html Background Coronavirus disease has widely spread all over the world since the beginning of 2020. It is desirable to develop automatic and accurate detection of COVID-19 using chest CT. Purpose To develop a fully automatic framework to detect COVID-19 using chest CT and evaluate its performances. Materials and Methods In this retrospective and multi-center study, a deep learning model, COVID-19 detection neural network (COVNet), was developed to extract visual features from volumetric chest CT exams for the detection of COVID-19. Community acquired pneumonia (CAP) and other non-pneumonia CT exams were included to test the robustness of the model. The datasets were collected from 6 hospitals between August 2016 and February 2020. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC), sensitivity and specificity. Results The collected dataset consisted of 4356 chest CT exams from 3,322 patients. The average age is 49±15 years and there were slightly more male patients than female (1838 vs 1484; p-value=0.29). The per-exam sensitivity and specificity for detecting COVID-19 in the independent test set was 114 of 127 (90% [95% CI 83%, 94%]) and 294 of 307 (96% [95% CI 93%, 98%]), respectively, with an AUC of 0.96 (p-value less then 0.001). The per-exam sensitivity and specificity for detecting CAP in the independent test set was 87% (152 of 175) and 92% (239 of 259), respectively, with an AUC of 0.95 (95% CI 0.93, 0.97). Conclusions A deep learning model can accurately detect COVID-19 and differentiate it from community acquired pneumonia and other lung diseases.Background CT may play a central role in the diagnosis and management of COVID-19 pneumonia. Purpose To perform a longitudinal study to analyze the serial CT findings over time in patients with COVID-19 pneumonia. Materials and Methods During January 16 to February 17, 2020, 90 patients (malefemale, 3357; me

Paste Settings

Paste Title : [Optional]

Paste Folder : [Optional]

Syntax Highlighting : [Optional]

Paste Expiration : [Optional]

Paste Status : [Optional]

Password : [Optional]

Description: [Optional]

Tags: [Optional]

Encrypt Paste (?)

You are currently not logged in, this means you can not edit or delete anything you paste. Sign Up or Login

Paste Settings

Site Languages